Avalanche sites and avalanche events in Val Ferret (data: 2020 update) [7].
\r\n\tThe book aims to introduce the potential reader to the problems associated with aeronautics, ranging from academic research to actual application and precise work, and to be of interest to those who want to research and build their techniques in the related fields.
",isbn:"978-1-80355-301-6",printIsbn:"978-1-80355-300-9",pdfIsbn:"978-1-80355-302-3",doi:null,price:0,priceEur:0,priceUsd:0,slug:null,numberOfPages:0,isOpenForSubmission:!0,isSalesforceBook:!1,hash:"a6b8e86015392b400a37551116fc0c13",bookSignature:"Associate Prof. Zain Anwar Anwar Ali",publishedDate:null,coverURL:"https://cdn.intechopen.com/books/images_new/11522.jpg",keywords:"Aeronautics, Aircraft, Control System, Surveillance, Guidance, Fixed-Wing, Rotorcraft, Jet Engine, Modern Drone, Path Planning, Adaptive Control, Hybrid Control",numberOfDownloads:null,numberOfWosCitations:0,numberOfCrossrefCitations:null,numberOfDimensionsCitations:null,numberOfTotalCitations:null,isAvailableForWebshopOrdering:!0,dateEndFirstStepPublish:"February 9th 2022",dateEndSecondStepPublish:"April 12th 2022",dateEndThirdStepPublish:"June 11th 2022",dateEndFourthStepPublish:"August 30th 2022",dateEndFifthStepPublish:"October 29th 2022",remainingDaysToSecondStep:"a month",secondStepPassed:!0,currentStepOfPublishingProcess:3,editedByType:null,kuFlag:!1,biosketch:"Engr. Dr. Zain Anwar Ali is working as an Associate Prof. and Editor of Sir Syed University Research Journal of Engineering and Technology. He received research funding from Higher Education Commission (HEC), Pakistan, and has research collaborations with several universities in China, including Nanjing University of Aeronautics and Astronautics, Donghua University, Shanghai University, and South East University, under different research grants provided by the National Nature Science Foundation of China.",coeditorOneBiosketch:null,coeditorTwoBiosketch:null,coeditorThreeBiosketch:null,coeditorFourBiosketch:null,coeditorFiveBiosketch:null,editors:[{id:"415526",title:"Associate Prof.",name:"Zain",middleName:"Anwar",surname:"Anwar Ali",slug:"zain-anwar-ali",fullName:"Zain Anwar Ali",profilePictureURL:"https://mts.intechopen.com/storage/users/415526/images/system/415526.png",biography:"Engr. Dr. Zain Anwar Ali received his B.S. degree in Electronic Engineering from Sir Syed University of Engineering and Technology, Karachi, Pakistan, in 2009. In the same year, he joined Sir Syed UET as a Research Assistant in the Electronic Engineering department, and was soon promoted to a Junior Lecturer due to his hard work and research contributions. He completed his Master's in Industrial Control and Automation at the Hamdard University of Engineering in 2012, securing his second position and soon being promoted to a Lecturer. Later he joined Nanjing University of Aeronautics and Astronautics (NUAA) as a Ph.D. research scholar and the Nanjing Strong Flight Electronics and Machinery LTD to complete his Ph.D. experimental work there. In 2017, he completed his Ph.D. in the field of Control Theory and Control Engineering NUAA. He then rejoined Sir Syed UET as an Assistant Professor in the Electronics Engineering department. In the same year, he was selected as a highly talented foreign expert by the Ministry of China, Beijing, at Liaocheng. After seeing his research background, the vice-chancellor of SSUET gave him the extra responsibility of an Associate Editor of Sir Syed UET research journal which is indexed at various indexing agencies and published in two issues annually. In 2018-2019, he received research funding from Higher Education Commission (HEC), Pakistan, and started some different research collaborations with several universities in China, including Nanjing University of Aeronautics and Astronautics (NUAA-Nanjing), Donghua University (DU-Shanghai), Shanghai University (SU-Shanghai), and South East University (SEU-Nanjing), under different research grants provided by the National Nature Science Foundation of China (NSFC). Currently, Dr. Ali is working as an Associate Professor at the Electronic Engineering Department, Sir Syed University of Engineering and Technology, Karachi, Pakistan, and as the Editor of Sir Syed University Research Journal of Engineering and Technology.",institutionString:"Sir Syed University of Engineering and Technology",position:null,outsideEditionCount:0,totalCites:0,totalAuthoredChapters:"1",totalChapterViews:"0",totalEditedBooks:"0",institution:{name:"Sir Syed University of Engineering and Technology",institutionURL:null,country:{name:"Pakistan"}}}],coeditorOne:null,coeditorTwo:null,coeditorThree:null,coeditorFour:null,coeditorFive:null,topics:[{id:"11",title:"Engineering",slug:"engineering"}],chapters:null,productType:{id:"1",title:"Edited Volume",chapterContentType:"chapter",authoredCaption:"Edited by"},personalPublishingAssistant:{id:"347258",firstName:"Marica",lastName:"Novakovic",middleName:null,title:"Ms.",imageUrl:"//cdnintech.com/web/frontend/www/assets/author.svg",email:"marica@intechopen.com",biography:null}},relatedBooks:[{type:"book",id:"10198",title:"Response Surface Methodology in Engineering Science",subtitle:null,isOpenForSubmission:!1,hash:"1942bec30d40572f519327ca7a6d7aae",slug:"response-surface-methodology-in-engineering-science",bookSignature:"Palanikumar Kayaroganam",coverURL:"https://cdn.intechopen.com/books/images_new/10198.jpg",editedByType:"Edited by",editors:[{id:"321730",title:"Prof.",name:"Palanikumar",surname:"Kayaroganam",slug:"palanikumar-kayaroganam",fullName:"Palanikumar Kayaroganam"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"1591",title:"Infrared Spectroscopy",subtitle:"Materials Science, Engineering and Technology",isOpenForSubmission:!1,hash:"99b4b7b71a8caeb693ed762b40b017f4",slug:"infrared-spectroscopy-materials-science-engineering-and-technology",bookSignature:"Theophile Theophanides",coverURL:"https://cdn.intechopen.com/books/images_new/1591.jpg",editedByType:"Edited by",editors:[{id:"37194",title:"Dr.",name:"Theophile",surname:"Theophanides",slug:"theophile-theophanides",fullName:"Theophile Theophanides"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"3161",title:"Frontiers in Guided Wave Optics and Optoelectronics",subtitle:null,isOpenForSubmission:!1,hash:"deb44e9c99f82bbce1083abea743146c",slug:"frontiers-in-guided-wave-optics-and-optoelectronics",bookSignature:"Bishnu Pal",coverURL:"https://cdn.intechopen.com/books/images_new/3161.jpg",editedByType:"Edited by",editors:[{id:"4782",title:"Prof.",name:"Bishnu",surname:"Pal",slug:"bishnu-pal",fullName:"Bishnu Pal"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"3092",title:"Anopheles mosquitoes",subtitle:"New insights into malaria vectors",isOpenForSubmission:!1,hash:"c9e622485316d5e296288bf24d2b0d64",slug:"anopheles-mosquitoes-new-insights-into-malaria-vectors",bookSignature:"Sylvie Manguin",coverURL:"https://cdn.intechopen.com/books/images_new/3092.jpg",editedByType:"Edited by",editors:[{id:"50017",title:"Prof.",name:"Sylvie",surname:"Manguin",slug:"sylvie-manguin",fullName:"Sylvie Manguin"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"371",title:"Abiotic Stress in Plants",subtitle:"Mechanisms and Adaptations",isOpenForSubmission:!1,hash:"588466f487e307619849d72389178a74",slug:"abiotic-stress-in-plants-mechanisms-and-adaptations",bookSignature:"Arun Shanker and B. Venkateswarlu",coverURL:"https://cdn.intechopen.com/books/images_new/371.jpg",editedByType:"Edited by",editors:[{id:"58592",title:"Dr.",name:"Arun",surname:"Shanker",slug:"arun-shanker",fullName:"Arun Shanker"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"72",title:"Ionic Liquids",subtitle:"Theory, Properties, New Approaches",isOpenForSubmission:!1,hash:"d94ffa3cfa10505e3b1d676d46fcd3f5",slug:"ionic-liquids-theory-properties-new-approaches",bookSignature:"Alexander Kokorin",coverURL:"https://cdn.intechopen.com/books/images_new/72.jpg",editedByType:"Edited by",editors:[{id:"19816",title:"Prof.",name:"Alexander",surname:"Kokorin",slug:"alexander-kokorin",fullName:"Alexander Kokorin"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"314",title:"Regenerative Medicine and Tissue Engineering",subtitle:"Cells and Biomaterials",isOpenForSubmission:!1,hash:"bb67e80e480c86bb8315458012d65686",slug:"regenerative-medicine-and-tissue-engineering-cells-and-biomaterials",bookSignature:"Daniel Eberli",coverURL:"https://cdn.intechopen.com/books/images_new/314.jpg",editedByType:"Edited by",editors:[{id:"6495",title:"Dr.",name:"Daniel",surname:"Eberli",slug:"daniel-eberli",fullName:"Daniel Eberli"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"57",title:"Physics and Applications of Graphene",subtitle:"Experiments",isOpenForSubmission:!1,hash:"0e6622a71cf4f02f45bfdd5691e1189a",slug:"physics-and-applications-of-graphene-experiments",bookSignature:"Sergey Mikhailov",coverURL:"https://cdn.intechopen.com/books/images_new/57.jpg",editedByType:"Edited by",editors:[{id:"16042",title:"Dr.",name:"Sergey",surname:"Mikhailov",slug:"sergey-mikhailov",fullName:"Sergey Mikhailov"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"1373",title:"Ionic Liquids",subtitle:"Applications and Perspectives",isOpenForSubmission:!1,hash:"5e9ae5ae9167cde4b344e499a792c41c",slug:"ionic-liquids-applications-and-perspectives",bookSignature:"Alexander Kokorin",coverURL:"https://cdn.intechopen.com/books/images_new/1373.jpg",editedByType:"Edited by",editors:[{id:"19816",title:"Prof.",name:"Alexander",surname:"Kokorin",slug:"alexander-kokorin",fullName:"Alexander Kokorin"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"2270",title:"Fourier Transform",subtitle:"Materials Analysis",isOpenForSubmission:!1,hash:"5e094b066da527193e878e160b4772af",slug:"fourier-transform-materials-analysis",bookSignature:"Salih Mohammed Salih",coverURL:"https://cdn.intechopen.com/books/images_new/2270.jpg",editedByType:"Edited by",editors:[{id:"111691",title:"Dr.Ing.",name:"Salih",surname:"Salih",slug:"salih-salih",fullName:"Salih Salih"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}}]},chapter:{item:{type:"chapter",id:"40628",title:"Application of Nanofluids in Heat Transfer",doi:"10.5772/52496",slug:"application-of-nanofluids-in-heat-transfer",body:'A wide variety of industrial processes involve the transfer of heat energy. Throughout any industrial facility, heat must be added, removed, or moved from one process stream to another and it has become a major task for industrial necessity. These processes provide a source for energy recovery and process fluid heating/cooling.
The enhancement of heating or cooling in an industrial process may create a saving in energy, reduce process time, raise thermal rating and lengthen the working life of equipment. Some processes are even affected qualitatively by the action of enhanced heat transfer. The development of high performance thermal systems for heat transfer enhancement has become popular nowadays. A number of work has been performed to gain an understanding of the heat transfer performance for their practical application to heat transfer enhancement. Thus the advent of high heat flow processes has created significant demand for new technologies to enhance heat transfer
There are several methods to improve the heat transfer efficiency. Some methods are utilization of extended surfaces, application of vibration to the heat transfer surfaces, and usage of micro channels. Heat transfer efficiency can also be improved by increasing the thermal conductivity of the working fluid. Commonly used heat transfer fluids such as water, ethylene glycol, and engine oil have relatively low thermal conductivities, when compared to the thermal conductivity of solids. High thermal conductivity of solids can be used to increase the thermal conductivity of a fluid by adding small solid particles to that fluid. The feasibility of the usage of such suspensions of solid particles with sizes on the order of 2 millimeters or micrometers was previously investigated by several researchers and the following significant drawbacks were observed (Das and Choi, 2006).
The particles settle rapidly, forming a layer on the surface and reducing the heat transfer capacity of the fluid.
If the circulation rate of the fluid is increased, sedimentation is reduced, but the erosion of the heat transfer devices, pipelines, etc., increases rapidly.
The large size of the particles tends to clog the flow channels, particularly if the cooling channels are narrow.
The pressure drop in the fluid increases considerably.
Finally, conductivity enhancement based on particle concentration is achieved (i.e., the greater the particle volume fraction is, the greater the enhancement—and greater the problems, as indicated above).
Thus, the route of suspending particles in liquid was a well known but rejected option for heat transfer applications.
However, the emergence of modern materials technology provided the opportunity to produce nanometer-sized particles which are quite different from the parent material in mechanical, thermal, electrical, and optical properties.
The situation changed when Choi and Eastman in Argonne National Laboratory revisited this field with their nanoscale metallic particle and carbon nanotube suspensions (Choi and Eastman (1995); Eastman et al. (1996)). Choi and Eastman have tried to suspend various metal and metal oxides nanoparticles in several different fluids (Choi (1998); Choi et al. (2001); Chon et al. (2005); Chon et al. (2006); Eastman et al. (2001); Eastman et al. (1999); Eastman et al. (2004)) and their results are promising, however, many things remain elusive about these suspensions of nano-structured materials, which have been termed “nanofluids” by Choi and Eastman.
Nanofluid is a new kind of heat transfer medium, containing nanoparticles (1–100 nm) which are uniformly and stably distributed in a base fluid. These distributed nanoparticles, generally a metal or metal oxide greatly enhance the thermal conductivity of the nanofluid, increases conduction and convection coefficients, allowing for more heat transfer
Nanofluids have been considered for applications as advanced heat transfer fluids for almost two decades. However, due to the wide variety and the complexity of the nanofluid systems, no agreement has been achieved on the magnitude of potential benefits of using nanofluids for heat transfer applications. Compared to conventional solid–liquid suspensions for heat transfer intensifications, nanofluids having properly dispersed nanoparticles possess the following advantages:
High specific surface area and therefore more heat transfer surface between particles and fluids.
High dispersion stability with predominant Brownian motion of particles.
Reduced pumping power as compared to pure liquid to achieve equivalent heat transfer intensification.
Reduced particle clogging as compared to conventional slurries, thus promoting system miniaturization.
Adjustable properties, including thermal conductivity and surface wettability, by varying particle concentrations to suit different applications.
The first test with nanofluids gave more encouraging features than they were thought to possess. The four unique features observed are listed below (Das and Choi, 2006).
The above potentials provided the thrust necessary to begin research in nanofluids, with the expectation that these fluids will play an important role in developing the next generation of cooling technology. The result can be a highly conducting and stable nanofluid with exciting newer applications in the future.
Thermo physical properties of the nanofluids are quite essential to predict their heat transfer behavior. It is extremely important in the control for the industrial and energy saving perspectives. There is great industrial interest in nanofluids. Nanoparticles have great potential to improve the thermal transport properties compared to conventional particles fluids suspension, millimetre and micrometer sized particles. In the last decade, nanofluids have gained significant attention due to its enhanced thermal properties.
Experimental studies show that thermal conductivity of nanofluids depends on many factors such as particle volume fraction, particle material, particle size, particle shape, base fluid material, and temperature. Amount and types of additives and the acidity of the nanofluid were also shown to be effective in the thermal conductivity enhancement.
The transport properties of nanofluid: dynamic thermal conductivity and viscosity are not only dependent on volume fraction of nanoparticle, also highly dependent on other parameters such as particle shape, size, mixture combinations and slip mechanisms, surfactant, etc. Studies showed that the thermal conductivity as well as viscosity both increases by use of nanofluid compared to base fluid. So far, various theoretical and experimental studies have been conducted and various correlations have been proposed for thermal conductivity and dynamic viscosity of nanofluids. However, no general correlations have been established due to lack of common understanding on mechanism of nanofluid.
A wide range of experimental and theoretical studies were conducted in the literature to model thermal conductivity of nanofluids. The existing results were generally based on the definition of the effective thermal conductivity of a two-component mixture. The Maxwell (1881) model was one the first models proposed for solid–liquid mixture with relatively large particles. It was based on the solution of heat conduction equation through a stationary random suspension of spheres. The effective thermal conductivity (Eq.1) is given by
Where kp is the thermal conductivity of the particles, keff is the effective thermal conductivity of nanofluid, kbf is the base fluid thermal conductivity, and ϕ is the volume fraction of the suspended particles.
The general trend in the experimental data is that the thermal conductivity of nanofluids increases with decreasing particle size. This trend is theoretically supported by two mechanisms of thermal conductivity enhancement; Brownian motion of nanoparticles and liquid layering around nanoparticles (Ozerinc et al, 2010). However, there is also a significant amount of contradictory data in the literature that indicate decreasing thermal conductivity with decreasing particle size.
Published results illustrated neither agreement about the mechanisms for heat transfer enhancement nor a unified possible explanation regarding the large discrepancies in the results even for the same base fluid and nanoparticles size. There are various models available for the measurement of effective thermal conductivity of nanofluids (Wang and Mujumdar, 2007) but there exists large deviations among them. Currently, there are no theoretical results available in the literature that predicts accurately the thermal conductivity of nanofluids.
Compared with the experimental studies on thermal conductivity of nanofluids, there are limited rheological studies reported in the literature for viscosity. Different models of viscosity have been used by researchers to model the effective viscosity of nanofluid as a function of volume fraction. Einstein (1956) determined the effective viscosity of a suspension of spherical solids as a function of volume fraction (volume concentration lower than 5%) using the phenomenological hydrodynamic equations (Eq.2). This equation was expressed by
Where µeff is the effective viscosity of nanofluid, µbf is the base fluid viscosity, and ϕ is the volume fraction of the suspended particles.
Later, Brinkman (1952) presented a viscosity correlation (Eq.3) that extended Einstein’s equation to suspensions with moderate particle volume fraction, typically less than 4%.
The effect of Brownian motion on the effective viscosity in a suspension of rigid spherical particles was studied by Batchelor (1977). For isotropic structure of suspension, the effective viscosity was given by(Eq.4):
Using classical formulas derived for a two-phase mixture, the specific heat capacity. (Pak and Cho,1998) and density( Xuan and Roetzel, 2000). of the nanofluid as a function of the particle volume concentration and individual properties can be computed using following equations(Eqs 5,and 6) respectively:
The novel and advanced concepts of nanofluids offer fascinating heat transfer characteristics compared to conventional heat transfer fluids. There are considerable researches on the superior heat transfer properties of nanofluids especially on thermal conductivity and convective heat transfer. Applications of nanofluids in industries such as heat exchanging devices appear promising with these characteristics. Kostic reported that nanofluids can be used in following specific areas:
Heat-transfer nanofluids.
Tribological nanofluids.
Surfactant and coating nanofluids.
Chemical nanofluids.
Process/extraction nanofluids.
Environmental (pollution cleaning) nanofluids.
Bio- and pharmaceutical-nanofluids.
Medical nanofluids (drug delivery and functional tissue–cell interaction).
Nanofluids can be used to cool automobile engines and welding equipment and to cool high heat-flux devices such as high power microwave tubes and high-power laser diode arrays. A nanofluid coolant could flow through tiny passages in MEMS to improve its efficiency. The measurement of nanofluids critical heat flux (CHF) in a forced convection loop is useful for nuclear applications. Nanofluids can effectively be used for a wide variety of industries, ranging from transportation to energy production and in electronics systems like microprocessors, Micro-Electro-Mechanical Systems (MEMS) and in the field of biotechnology. Recently, the number of industrial application potential of nanofluids technology and their focus for specific industrial applications is increasing. This chapter deals the some of the important application of nanofluids in the field of heat transfer.
The increases in effective thermal conductivity are important in improving the heat transfer behavior of fluids. A number of other variables also play key roles. For example, the heat transfer coefficient for forced convection in tubes depends on many physical quantities related to the fluid or the geometry of the system through which the fluid is flowing. These quantities include intrinsic properties of the fluid such as its thermal conductivity, specific heat, density, and viscosity, along with extrinsic system parameters such as tube diameter and length and average fluid velocity. Therefore, it is essential to measure the heat transfer performance of nanofluids directly under flow conditions. Researchers have shown that nanofluids have not only better heat conductivity but also greater convective heat transfer capability than that of base fluids. The following section provides the wide usage and effective utilization of nanofluids in heat exchangers as heat transfer fluids.
Pak and Cho (1998) investigated experimentally the turbulent friction and heat transfer behaviors of dispersed fluids (i.e., ultrafine metallic oxide particles suspended in water) in a circular pipe. Two different metallic oxide particles, γ-alumina (Al2O3) and titanium dioxide (TiO2) with mean diameters of 13 and 27 nm, respectively, were used as suspended particles. In their flow loop, the hydrodynamic entry section and the heat transfer section was made using a seamless, stainless steel tube, of which the inside diameter and the total length were 1.066 crn and 480 crn, respectively. The hydrodynamic entry section was long enough (i.e.,
They proposed a new correlation (Eq.7) for the Nusselt number under their experimental ranges of volume concentration (0-3%), the Reynolds number (104 - 105), and the Prandtl number (6.54 - 12.33) for the dispersed fluids γ-Al2O3 and TiO2 particles as given below
Xuan and Li (2003) built an experimental rig to study the flow and convective heat transfer feature of the nanofluid flowing in a tube. Their test section was a straight brass tube of the inner diameter of 10 mm and the length of 800 mm. Eight thermocouples were mounted at different places of the heat transfer test section to measure the wall temperatures and other two thermocouples were respectively located at the entrance and exit of the test section to read the bulk temperatures of the nanofluid. They investigated convective heat transfer feature and flow performance of Cu-water nanofluids for the turbulent flow. The suspended nanoparticles remarkably enhance heat transfer process and the nanofluid has larger heat transfer coefficient than that of the original base liquid under the same Reynolds number. They found that at fixed velocities, the heat transfer coefficient of nanofluids containing 2.0 vol% Cu nanoparticles was improved by as much as 40% compared to that of water. The Dittus–Boelter correlation failed to predict the improved experimental heat transfer behavior of nanofluids. The heat transfer feature of a nanofluid increases with the volume fraction of nanoparticles.
They have proposed the following correlation (Eq.8) to correlate the experimental data for the nanofluid. The Nusselt number Nu for the turbulent flow of nanofluids inside a tube are obtained as follows
They found good coincidence between the results calculated from this correlation and the experimental ones.
The Peclet number Pe describes the effect of thermal dispersion caused by micro convective and micro diffusion of the suspended nanoparticles. The case c2 = 0 refers to zero thermal dispersion, which namely corresponds to the case of the pure base fluid. The particle Peclet number Ped, Renf and Prnf in (Eq.8) are defined as
The thermal diffusivity of the nanofluid in Eq.8 is defined as Eq 8.iv
They defined the friction factor (Eq.9) as
It should be noted that, correlations developed by Pak and Cho (1998) and Xuan and Li (2003) were of a form similar to that of well known Dittus - Boelter formula. In both the works, the nanofluid was treated as a single phase fluid for the calculation of nanofluid Nusselt number
Wen and Ding (2004) were first to study the laminar entry flow of nanofluids in circular tubes. A straight copper tube with 970 mm length, 4.5 mm inner diameter, and 6.4 mm outer diameter was used as the test section. The whole test section was heated by a silicon rubber flexible heater. Their results showed a substantial increase in the heat transfer coefficient of water-based nanofluids containing γ-Al2O3 nanoparticles in the entrance region and a longer entry length is needed for the nanofluids than that for water. They concluded that the enhancement of the convective heat transfer could not be solely attributed to the enhancement of the effective thermal conductivity. Particle migration is proposed to be a reason for the enhancement, which results a non-uniform distribution of thermal conductivity and viscosity field and reduces the thermal boundary layer thickness.
Yang et al., (2005) measured the convective heat transfer coefficients of several nanofluids under laminar flow in a horizontal tube heat exchanger. A small circular tube of inner diameter 0.457 cm, outside diameter of 0.635 cm and length 45.7 cm was used as test section. The whole system was heavily insulated to reduce heat loss. Pipes were wrapped with insulation material, and plastic fittings were attached at both ends of the test area to thermally isolate the connection. The average diameter of the disk-shaped graphite nanoparticles used in this research was about 1 to 2μm, with a thickness of around 20 to 40 nm.
They applied the correlations for the convective heat transfer of the single-phase fluid to predict heat transfer coefficient of a nanofluid system, if the volume fraction of particles is
very low. They used the following correlation (Eq.10) to identify the impact of Reynolds number on the heat transfer coefficient
Their results indicated that the increase in the heat transfer coefficient of the nanofluids is much less than that predicted from a conventional correlation. Near-wall particle depletion in laminar shear flow is one possible reason for the phenomenon. However, there is a doubt whether this work falls in the category of nanofluids at all because the particle diameter is too large for the particles to be called nanoparticles.
Maiga et al., (2005) presented the numerical study of fully developed turbulent flow of Al2O3 - water nanofluid in circular tube at uniform heat flux of 50 W/cm2. The classical k-ε model was used for turbulence modeling and their study clearly showed that the inclusion of nanoparticles into the base fluids has produced a considerable augmentation of the heat transfer coefficient that clearly increases with an increase of the particle concentration. However, the presence of such particles has also induced drastic effects on the wall shear stress that increases appreciably with the particle loading. Among the mixtures studied, the ethylene glycol γ-Al2O3nanofluid appears to offer a better heat transfer enhancement than water– γ-Al2O3. The following correlations(Eqs 11 and 12) have been proposed for computing the averaged Nusselt number for the nanofluids considered for both the thermal boundary conditions, valid for Re ≤ 1000, 6 ≤ Pr ≤ 7.53 and ϕ ≤ 10%
Maiga et al., (2006) studied the hydrodynamic and thermal behavior of turbulent flow in a tube using Al2O3 nanoparticle suspension at various concentrations under the constant heat flux boundary condition. Assuming single-phase model, governing equations were solved by a numerical method of control volume. The following correlation (Eq.13) was proposed to calculate the heat transfer coefficient in terms of the Reynolds and the Prandtl numbers, valid for 104 ≤ Re ≤ 5x105, 6.6 ≤ Pr ≤ 13.9 and 0 ≤ ϕ ≤ 10%.
Ding et al., (2006) were first to study the laminar entry flow of water-based nanofluids containing multiwalled carbon nanotubes (CNT nanofluids). The experimental system for measuring the convective heat transfer coefficient was similar to the one reported by Wen and Ding (2004). Significant enhancement in the convective heat transfer was observed in relation to pure water as the working fluid. The enhancement depends on the flow condition, CNT concentration and the pH level, and the effect of pH is observed to be small. They stated that the enhancement in convective heat transfer is a function of the axial distance from the inlet of the test section. This enhancement increases first, reaches a maximum, and then decreases with increasing axial distance. For nanofluids containing only 0.5 wt% CNTs, the maximum enhancement in the convection heat transfer coefficient reaches over 350% at Re = 800. Such a high level of enhancement could not be attributed purely to enhanced thermal conductivity. They proposed possible mechanisms such as particle rearrangement, reduction of thermal boundary layer thickness due to the presence of nanotubes, and the very high aspect ratio of CNTs. They also concluded that, the observed large enhancement of the convective heat transfer could not be attributed purely to the enhancement of thermal conduction under the static conditions. Particle re-arrangement, shear induced thermal conduction enhancement, reduction of thermal boundary layer thickness due to the presence of nanoparticles, as well as the very high aspect ratio of CNTs are proposed to be possible mechanisms.
Heriz et al., (2006) investigated laminar flow convective heat transfer through circular tube with constant wall temperature boundary condition for nanofluids containing CuO and Al2O3 oxide nanoparticles in water as base fluid. The experimental apparatus consisting of a test chamber constructed of 1 m annular tube with 6 mm diameter inner copper tube and with 0.5 mm thickness and 32 mm diameter outer stainless steel tube. Nanofluid flows inside the inner tube while saturated steam enters annular section, which creates constant wall temperature boundary condition. The fluid after passing through the test section enters heat exchanger in which water was used as cooling fluid. The experimental results emphasized that the single phase correlation with nanofluids properties (Homogeneous Model) was not able to predict heat transfer coefficient enhancement of nanofluids. The comparison between experimental results obtained for CuO/ water and Al2O3 / water nanofluids indicated that heat transfer coefficient ratios for nanofluid to homogeneous model in low concentration were close to each other but by increasing the volume fraction, higher heat transfer enhancement for Al2O3/water was observed. They concluded that heat transfer enhancement by nanofluid depends on several factors including increment of thermal conductivity, nanoparticles chaotic movements, fluctuations and interactions.
The flow and heat transfer behavior of aqueous TiO2 nanofluids flowing through a straight vertical pipe was carried out by He et al., (2007) under both the laminar and turbulent flow conditions. Their experimental system consisted of a flow loop, a heating unit, a cooling unit, and a measuring and control unit. The test section was a vertically oriented straight copper tube with 1834 mm length, 3.97 mm inner diameter, and 6.35 mm outer diameter. The tube was heated by two flexible silicon rubber heaters. There was a thick thermal isolating layer surrounding the heaters to obtain a constant heat flux condition along the test section. Two pressure transducers were installed at the inlet and outlet of the test section to measure the pressure drop. They investigated the effects of nanoparticles concentrations, particle size, and the flow Reynolds number. They reported that, addition of nanoparticles into the base liquid enhanced the thermal conduction and the enhancement increased with increasing particle concentration and decreasing particle size. Their results also showed that the convective heat transfer coefficient increases with nanoparticle concentration in both the laminar and turbulent flow regimes and the effect of particle concentration seems to be more considerable in the turbulent flow regimes for the given flow Reynolds number and particle size. Pressure drop of nanofluids was very close to that of the base liquid for given flow Reynolds number. Predictions of the pressure drop with the conventional theory for the base liquid agree well with the measurements at relatively low Reynolds numbers. Deviation occurs at high Reynolds numbers possibly due to the entrance effect.
Kulkarni et al., (2008) investigated heat transfer and fluid dynamic performance of nanofluids comprised of silicon dioxide (SiO2) nanoparticles suspended in a 60:40 (% by weight) ethylene glycol and water (EG/water) mixture. The heat transfer test section was a straight copper tube with outside diameter of 4.76 mm, inside diameter of 3.14 mm, and a length of 1 m. The wall temperature was measured by means of six thermocouples mounted on the tube surface along the length. The inlet and outlet temperatures of the nanofluid were measured using two thermowells at the inlet and outlet of the test section. Two plastic fittings at inlet and outlet section of the copper tube provide a thermal barrier to axial heat conduction. The test section was heated electrically by four strip heaters to attain the constant heat flux boundary condition. The test section was insulated by 10 cm of fiber glass to minimize the heat loss from the heat transfer test system to ambient air. A four-pass shell and tube counter flow heat exchanger cools the nanofluids to keep the inlet fluid temperature constant using shop water. The effect of particle diameter (20 nm, 50 nm, 100 nm) on the viscosity of the fluid was investigated. They performed experiments to investigate the convective heat transfer enhancement of nanofluids in the turbulent regime by using the viscosity values measured. They observed increase in heat transfer coefficient due to nanofluids for various volume concentrations and loss in pressure was observed with increasing nanoparticle volume concentration.
Hwang et al., (2009) investigated flow and convective heat transfer characteristics of water-based Al2O3 nanofluids flowing through a circular tube of 1.812 mm inner diameter with the constant heat flux in fully developed laminar regime. Water-based Al2O3 nanofluids with various volume fractions ranging from 0.01% to 0.3% are manufactured by the two-step method. They also measured physical properties of water-based Al2O3 nanofluids such as the viscosity, the density, the thermal conductivity and the heat capacity. They presented that the nanoparticles suspended in water enhance the convective heat transfer coefficient in the thermally fully developed regime, despite low volume fraction between 0.01 and 0.3 vol%. Especially, the heat transfer coefficient of water-based Al2O3 nanofluids was increased by 8% at 0.3 vol% under the fixed Reynolds number compared with that of pure water and the enhancement of the heat transfer coefficient is larger than that of the effective thermal conductivity at the same volume concentration. Based on their experimental results, it was shown that the Darcy friction factor of water-based Al2O3 nanofluids experimentally measured has a good agreement with theoretical results from the friction factor correlation for the single-phase flow (f = 64/ReD).
Sharma et al., (2009) conducted experiments to evaluate heat transfer coefficient and friction factor for flow in a tube and with twisted tape inserts in the transition range of flow with Al2O3 nanofluid. Hydro dynamically and thermally developed heat transfer test section is having 1.5 m long with an L/D ratio of 160. The tube was heated uniformly for a length of 1.5 m by wrapping with two nichrome heaters of 1 kW electrical rating. Their twisted tapes were made from 1 mm thick and 0.018 m width aluminum strip. The two ends of the strip are held on a lathe and subjected to 180° twist by turning the chuck manually and obtained twist ratios of 5, 10 and 15. The results showed considerable enhancement of convective heat transfer with Al2O3 nanofluids compared to flow with water. They found that the effect of inclusion of twisted tape in the flow path gives higher heat transfer rates compared to flow in a plain tube. They also observed the equation of Gleninski(1976) applicable in transitional flow range for single-phase fluids exhibited considerable deviation when compared with values obtained with nanofluid. The heat transfer coefficient of nanofluid flowing in a tube with 0.1% volume concentration was 23.7% higher when compared with water at number of 9000.
Heat transfer coefficient and pressure drop with nanofluid were experimentally determined with tapes of different twist ratios and found to deviate with values obtained from equations (Eqs 14and 15) developed for single-phase flow. The data of Al2O3 nanofluid for flow in plain tube and with twisted tape insert is fit to a regression equation with average deviation of 4.0% and standard deviation of 5.0%.
0 < H/D < 15, 3500 < Re <8500, 4.5 < Pr <5.5 and 35 < Tb < 40.
The data of friction factor for flow of fluids a plain tube and with tape insert is also subjected to regression with the assumption that nanofluid behaves as single-phase fluid in the low volume concentration given by
Valid for water (ϕ = 0) and nanofluid of ϕ < 0.1 volume concentration
Yu et al., (2009) measured the heat transfer rates in the turbulent flow of a potential commercially viable nanofluid consisting of a 3.7% volume of 170-nm silicon carbide particles suspended in water. Their test facility was a closed-loop system with major components consisting of a pump with variable speed drive, pre heater, horizontal tube test section, heat exchanger (cooler), and flow meter. The test section itself was a stainless steel circular tube with dimensions of 2.27-mm inside diameter, 4.76-mm outside diameter, and 0.58-m heated length. Heat transfer coefficient increase of 50–60% above the base fluid water was obtained when compared on the basis of constant Reynolds number. This enhancement is 14–32% higher than predicted by a standard single-phase turbulent heat transfer correlation pointing to heat transfer mechanisms that involve particle interactions. The data were well predicted by a correlation modified for Prandtl number dependence although experiments in the present study did not support the postulated mechanisms of Brownian diffusion and thermophoresis. The pumping power penalty of the SiC/water nanofluid was shown to be less than that of an Al2O3/water nanofluid of comparable particle concentration. The two nanofluids were compared using a figure of the merit(Eq.16) consisting of the ratio of heat transfer enhancement to pumping power increase.
The merit parameter was 0.8 for the SiC/water nanofluid compared to 0.6 for the Al2O3/water nanofluid, which is favorable to the SiC/water nanofluid for applications that are pumping power sensitive.
Torii and Yang (2009) studied the convective heat transfer behavior of aqueous suspensions of nanodiamond particles flowing through a horizontal tube heated under a constant heat flux condition. Their experimental system consisting of a flow loop, a power supply unit, a cooling device, and a flow measuring and control unit. The flow loop includes a pump, a digital flow meter, a reservoir, a collection tank, and a test section. A straight seamless stainless tube with 1000 mm length, 4.0 mm inner diameter, and 4.3 mm outer diameter was used as the test section. The whole test section was heated with the aid of the Joule heating method through an electrode linked to a dc power supply. They reported that (i) significant enhancement of heat transfer performance due to suspension of nanodiamond particles in the circular tube flow was observed in comparison with pure water as the working fluid, (ii) the enhancement was intensified with an increase in the Reynolds number and the nanodiamond concentration, and (iii) substantial amplification of heat transfer performance is not attributed purely to the enhancement of thermal conductivity due to suspension of nanodiamond particles.
Effect of particle size on the convective heat transfer in nanofluid by Anoop et al., (2009) in the developing region of pipe flow with constant heat flux showed that the enhancement in heat transfer coefficient was around 25% whereas for the 150 nm particle based nanofluids it was found to be around 11%. The heated test section was made of copper tube of 1200 mm length and 4.75 ± 0.05 mm inner diameter and the thickness of the tube was around 1.25 mm. Electrically insulated nickel chrome wire was uniformly wound along the length giving a maximum power of 200 W. They found that, with increase in particle concentration and flow rate the average heat transfer coefficient value was increased. They also observed that at the developing region the heat transfer coefficient is more than that at nearly developed region. It was further observed that the nanofluid with 45 nm particles shows higher heat transfer coefficient than that with 150 nm particles. For instance at x/D = 147, for 45 nm particle based nanofluid (4 wt%) with Re = 1550, the enhancement in heat transfer coefficient was around 25% whereas for the 150 nm particle based nanofluids it was found to be around 11%. After conducting sufficient number of experiments, they proposed the following correlation (Eq.17)
Where, dref = 100 nm and x+ is the dimensionless distance.
Rea et al., (2009) investigated laminar convective heat transfer and viscous pressure loss for alumina–water and zirconia–water nanofluids in a flow loop. The vertical heated test section was a stainless steel tube with an inner diameter (ID) of 4.5 mm, outer diameter (OD) of 6.4 mm, and length of 1.01 m. The test section had eight sheathed and electrically insulated T-type thermocouples soldered onto the outer wall of the tubing along axial locations of 5, 16, 30, 44, 58, 89, 100 cm from inlet of the heated section. Two similar T-type thermocouples were inserted into the flow channel before and after the test section to measure the bulk fluid temperatures. The heat transfer coefficients in the entrance region and in the fully developed region were found to increase by 17% and 27%, respectively, for alumina–water nanofluid at 6 vol % with respect to pure water. The zirconia–water nanofluid heat transfer coefficient increases by approximately 2% in the entrance region and 3% in the fully developed region at 1.32 vol %. The measured pressure loss for the nanofluids was in general much higher than for pure water and in good agreement with the traditional model predictions for laminar flow
Garg et al., (2009) used a straight copper tube of 914.4 mm length, 1.55 mm inner diameter and 3.175 mm outer diameter. The whole section was heated by an AWG 30 nichrome 80 wire wound on the tube. Both ends of the copper tube were connected to well-insulated plastic tubing to insulate the heat transfer section and fluid from axial heat conduction, and to avoid heat losses. The experiments were run under constant heat flux conditions using a current of 0.2 A. The test section was insulated to prevent loss of heat to the surroundings. Four surface-mount thermocouples were mounted on the test section at axial positions of 19 cm, 39.5 cm, 59 cm and 79 cm from the inlet of the section to measure wall temperatures. Additionally, two thermocouples were mounted on individual, unheated, and thermally insulated, short copper tubes located before and after the heat transfer section to measure the fluid bulk temperature at the inlet and outlet of the heat transfer section. De-ionized (DI) water, Gum Arabic (GA) and multi-walled carbon nanotubes (MWCNT) were used to produce aqueous suspensions. The nanotubes procured had a specified average outside diameter of 10–20 nm, length of 0.5–40 lm and purity of 95%. They observed a maximum percentage enhancement of 32% in heat transfer coefficient at Re - 600 ± 100. This percentage enhancement in heat transfer coefficient was found to continuously increase with axial distance. The percentage enhancement in heat transfer coefficient was found to continuously increase with axial distance. The reason behind the phenomenon is explained by the contribution from a considerable increase in thermal conductivity with an increase in bulk temperature with axial distance.
Lai et al., (2009) experimentally investigated the convection heat transfer performance of 20-nm, γ Al2O3 water-based nanofluids in a single 1.02-mm inner diameter, and constant heat flux stainless steel tube for laminar flow in both the developing and fully developed regions. Overall experimental results showed that the heat transfer coefficient increases with volume flow rate and nanoparticle volume fraction. In the developing region, the heat transfer coefficient enhancement decreased with increasing axial distance from the test section entrance. These results also showed that the higher the volume fraction, the longer is the thermal entrance length.
Chandrasekar et al., (2010) carried out experimental investigations on convective heat transfer and pressure drop characteristics of Al2O3/water nanofluid in the fully developed laminar region of pipe flow with constant heat flux with and without wire coil inserts. Their test loop consisting of a pump, calming section, heated test section, cooling section, a collecting station and a reservoir. Calming section of straight copper tube 800 mm long, 4.85 mm inner diameter, and 6.3 mm outer diameter was used to eliminate the entrance effect and to ensure fully developed laminar flow in the test section. A straight copper tube with 1200 mm length, 4.85 mm inner diameter, and 6.3 mm outer diameter was used as the test section. The test section was first wound with sun mica to isolate it electrically. Then, ceramic beads coated electrical SWG Nichrome heating wire giving a maximum power of 300W was wounded over it. Over the electrical winding, thick insulation consisting of layers of ceramic fiber, asbestos rope, glass wool and another layer of asbestos rope at the outer surface was provided to prevent the radial heat loss. The test section was isolated thermally from its upstream and downstream sections by plastic bushings to minimize the heat loss resulting from axial heat conduction. Two types of wire coil inserts were used which were fabricated using 0.5 mm stainless steel wire having a coil diameter of 4.5 mm and coil pitch ratio (defined as the ratio of pitch of the coil to diameter of tube) of 2 and 3. Dilute 0.1% Al2O3/water nanofluid increased the Nusselt number by 12.24% at Re = 2275 compared to that of distilled water. Further enhancements in Nusselt numbers was observed when Al2O3/water nanofluid is used with wire coil inserts. Nusselt numbers were increased by 15.91% and 21.53% when Al2O3/water nanofluid was used with their two types of wire coil inserts respectively at Re = 2275 compared to those of distilled water.
The Nusselt number and friction factor experimental results have been correlated by the following equations (Eqs 18 and 19).
The regression equation coefficients were assessed with the help of classical the least square method and the correlation is valid for laminar flow with Re < 2300, dilute Al2O3/water nanofluid with volume concentration ϕ = 0.1% and wire coil inserts with 2 ≤ p/d ≤ 3. They also found that, when compared to the pressure drop with distilled water, there was no significant increase in pressure drop for the nanofluid.
Amrollahi et al., (2010) measured the convective heat transfer coefficients of water-based functionalized multi walled nano tubes (FMWNT) nanofluid under both laminar and turbulent regimes flowing through a uniformly heated horizontal tube in entrance region. The straight copper tube with 11.42 mm inner diameter and 1 m length was used as the test section. The tube surface is electrically heated by an AC power supply to generate constant heat 800W and was insulated thermally by about 150 mm thick blanket to minimize the heat loss from the tube to the ambient. Five thermocouples were soldered on at different places along the test section to measure the wall temperature of the copper tube and the mean temperature of the fluids at the inlet, and two thermocouples were inserted at the inlet and outlet of the test section. They compared effective parameters to measure the convective heat transfer coefficients for functionalized MWNT suspensions such as Re, mass fraction and temperature altogether in entrance region for the first time. Their experimental results indicated that the convective heat transfer coefficient of these nanofluids increases by up to 33–40% at a concentration of 0.25 wt. % compared with that of pure water in laminar and turbulent flows respectively. Their results also showed that, increasing the nanoparticles concentration does not show much effect on heat transfer enhancement in turbulent regime in the range of concentrations studied. Also the ratio of heat transfer coefficient decreased with increasing Reynolds number. It was observed that the wall temperature of the test tube decreased considerably when the nanofluid flowed in the tube. Furthermore, this coefficient of nanofluids at the entrance of the test tube increases with Reynolds number, contrary to the fully developed laminar region that is constant.
Xie et al., (2010) reported on investigation of the convective heat transfer enhancement of nanofluids as coolants in laminar flows inside a circular copper tube with constant wall temperature. Nanofluids containing Al2O3, ZnO, TiO2, and MgO nanoparticles were prepared with a mixture of 55 vol. % distilled water and 45 vol. % ethylene glycol as base fluid. It was found that the heat transfer behaviors of the nanofluids were highly depended on the volume fraction, average size, species of the suspended nanoparticles and the flow conditions. MgO, Al2O3, and ZnO nanofluids exhibited superior enhancements of heat transfer coefficient with the highest enhancement up to 252% at a Reynolds number of 1000 for MgO nanofluid. They also demonstrated that these oxide nanofluids might be promising alternatives for conventional coolants.
Fotukian and Esfahany (2010a) experimentally investigated the CuO/water nanofluid convective heat transfer in turbulent regime inside a tube. The test section was constructed of 1 m annular tube with inner copper tube of 5 mm inner diameter and 0.5 mm thickness and 32mm diameter outer stainless steel tube. Nanofluid flowed inside the inner tube while saturated steam entered annular section. They used dilute nanofluids with nanoparticles volume fractions less than 0.3%. They got excellent agreement between the measured heat transfer coefficients of pure water and the Dittus–Boelter predictions. They observed that heat transfer coefficients for nanofluids were greater than that of water and increasing the nanoparticle concentration showed a very weak effect on heat transfer coefficient. In such low concentrations of nanofluid investigated, the augmentation of heat transfer coefficient could not be attributed to the increase of thermal conductivity. The heat transfer coefficient increased about 25% compared to pure water. They concluded that, increasing nanoparticles concentration does not show much effect on heat transfer enhancement in turbulent regime in their studied range of concentrations. Also, the ratio of convective heat transfer coefficient of nanofluid to that of pure water decreased with increasing Reynolds number. It was also reported that the wall temperature of the test tube decreased considerably when the nanofluid flowed in the tube.
Fotukian and Esfahany (2010b) investigated turbulent convective heat transfer and pressure drop of γ Al2O3 /water nanofluid inside a circular tube, the same as described previously. The volume fraction of nanoparticles in base fluid was less than 0.2%. Their results indicated that addition of small amounts of nanoparticles to the base fluid augmented heat transfer remarkably. Increasing the volume fraction of nanoparticles in the range studied did not show much effect on heat transfer enhancement. Their experimental measurements showed that pressure drop for the dilute nanofluid was much greater than that of the base fluid.
Experimental investigations on convective heat transfer and pressure drop characteristics of three different concentration of CuO/water nanofluid was carried out by Suresh et al., (2010) in the fully developed turbulent region of pipe flow with constant heat flux. Experiments were done with a dimpled tube having dimensions of 4.85 mm diameter and 800 mm length. They reported that i) the relative viscosity of nanofluids increase with an increase in concentration of nanoparticles. ii) The thermal conductivity of nanofluid increases nonlinearly with the volume concentration of nanoparticles. iii) The convective heat transfer coefficient increases with increasing Reynolds number and increasing volume concentration in plain tube, and increases further with a dimpled tube. The Nusselt number and friction factor experimental results of nanofluids with dimpled tubes have been correlated by the following expressions (Eqs 20 and 21) using the least squares regression analysis
Pathipakka and Sivashanmugam (2010) numerically estimated the heat transfer behavior of nanofluids in a uniformly heated circular tube fitted with helical inserts in laminar flow. They used Al2O3 nanoparticles in water of 0.5%, 1.0% and 1.5% concentrations and helical twist inserts of twist ratios (ratio of length of one twist to diameter of the twist) 2.93, 3.91 and 4.89 for the simulation. Assuming the nanofluid behave as a single phase fluid, they investigated three dimensional steady state heat transfer behavior using Fluent 6.3.26. They concluded that the heat transfer increases with Reynolds number and decrease in twist ratio with maximum for the twist ratio 2.93. The increase in Nusselt number was 5%_31% for helical inserts of different twist ratio and nanofluids of different volume concentrations. The heat transfer enhancement was 31% for helical tape insert of twist ratio 2.93 and Al2O3volume concentration of 1.5% corresponding to the Reynolds number of 2039.
Suresh et al., (2011) presented a comparison of thermal performance of helical screw tape inserts in laminar flow of Al2O3/water and CuO/water nanofluids through a straight circular duct with constant heat flux boundary condition. Their experimental set up consists of a test section, calming section, pump, cooling unit, and a fluid reservoir. Both the calming section and test sections were made of straight copper tube with the dimension 1000 mm long, 10 mm ID and 12 mm OD. The calming section was used to eliminate the entrance effect. The test section tube was wounded with ceramic beads coated electrical SWG Nichrome heating wire. Over the electrical winding a thick insulation is provided using glass wool to minimize heat loss. They used three types of helical screw tape inserts with various twist ratio (1.78, 2.44, and 3) was made by winding uniformly a copper strip of 3.5 mm width over a 2.5 mm copper rod. The twist ratio ‘Y’, defined as the ratio of length of one twist (pitch, P) to diameter of the twist.
They used their experimental results of heat transfer to derive the following correlations(Eqs 22 and 23) of Nusselt number using least square method of regression analysis. The correlations are valid for laminar flow (Re < 2300) of 0.1% volume concentration of Al2O3/water and CuO/water nanofluids and for helical screw tape inserts of twist ratio ranging from 1.78 to 3.
Their results showed thermal performance factor of helical screw tape inserts using CuO/water nanofluid is found to be higher when compared with the corresponding value using Al2O3/water.
The experimental results on convective heat transfer of non-Newtonian nanofluids flowing through a horizontal uniformly heated tube under turbulent flow conditions by Hojjat et al., (2011a) states that convective heat transfer coefficient and Nusselt number of nanofluids are remarkably higher than those of the base fluid. Their experimental setup consists of a flow loop comprised of three sections: cooling unit, measuring and control units. The test section consists of a straight stainless steel (type 316) tube, 2.11-m long, 10-mm inner diameter, and 14-mm outer diameter. The test section was electrically heated by an adjustable DC power supply in order to impose a constant wall heat flux boundary condition. Ten K-type thermocouples were mounted on the tube outside wall to measure the wall temperature at different axial locations. The locations of the thermocouples were placed at the following axial positions from the test section inlet: 100, 150, 200, 350, 550, 800, 1100, 1400, 1700, and 2000 mm. The test section was thermally insulated from the upstream and downstream sections by thick Teflon bushings in order to reduce the heat loss along the axial direction. Two K-type thermocouples were also inserted in the calming chamber and the mixing chamber to measure the inlet and outlet bulk temperatures of the nanofluid, respectively. The whole test section including the calming and mixing chambers were heavily insulated. Three different types of nanofluids were prepared by first dispersing γ-Al2O3, TiO2 and CuO nanoparticles in deionized water. The solution were subjected to ultrasonic vibration to obtain uniform suspensions, and then appropriate amounts of concentrated Carboxy Methyl Cellulose (CMC) solution were added to the suspension and mixed thoroughly with a mechanical mixer to achieve homogeneous nanofluids with the desired concentration. Average sizes of γ-Al2O3, TiO2 and CuO nanoparticles were 25, 10, and 30-50 nm, respectively. Their results showed that Convective heat transfer coefficient and Nusselt number of nanofluids are remarkably higher than those of the base fluid. These enhancements of nanofluids were directly proportional to the particle concentration and Peclet number. Since the enhancement of heat transfer coefficient of nanofluids was much higher than that attributed to the improvement of the thermal conductivity, it was expected that the enhancement of heat transfer coefficient of nanofluids was affected by some other factors. Based on the experimental results, they proposed the following empirical correlation (Eq.24) to predict the heat transfer coefficients of non-Newtonian nanofluids.
2800 < Re < 8400; 40 < Pr < 73.
Hojjat et al., (2011b) experimentally investigated the forced convection heat transfer of non-Newtonian nanofluids in a circular tube with constant wall temperature under turbulent flow conditions. Three types of nanofluids were prepared by dispersing homogeneously γ-Al2O3, TiO2 and CuO nanoparticles into the base fluid. An aqueous solution of carboxymethyl cellulose (CMC) was used as the base fluid. Nanofluids as well as the base fluid show shear-thinning (pseudoplastic) rheological behavior. The test section consists of two 2-m long concentric tubes. The internal diameter of inner tube was 10 mm and a thickness 2 mm. The internal diameter of outer tube was 48 mm. Both tubes were made of stainless steel (type 316). The nanofluid flows through the inner tube whereas hot water was circulated through the annular section at high flow rates in order to create constant wall temperature boundary condition. Results indicated that the convective heat transfer coefficient of nanofluids is higher than that of the base fluid. The enhancement of the convective heat transfer coefficient increases with an increase in the Peclet number and the nanoparticle concentration. The increase in the convective heat transfer coefficient of nanofluids was greater than the increase that would be observed considering strictly the increase in the effective thermal conductivity of nanofluids. Experimental data were compared to heat transfer coefficients predicted using available correlations for purely viscous non-Newtonian fluids. Results showed poor agreement between experimental and predicted values. Hence they proposed a new correlation(Eq.25) to successfully predict Nusselt numbers of non-Newtonian nanofluids as a function of Reynolds and Prandtl numbers.
Mahrood et al., (2011) experimentally investigated free convection heat transfer of non Newtonian nanofluids under constant heat flux condition. Two different kinds of non-Newtonian nanofluids were prepared by dispersion of Al2O3 and TiO2 nanoparticles in a 0.5 wt. % aqueous solution of carboxy methyl cellulose (CMC). Experimental investigation of natural convection heat transfer behavior of non- Newtonian nanofluids in a vertical cylinder was attempted. Test section was a vertical cylindrical enclosure made up of PTFE (Poly Tetra Fluoro Ethylene). Fluid in the test section was heated from below by a heating system which consists of an aluminum circular plate and an electrical heater. In order to achieve a constant wall heat flux, the heater was placed between the aluminum plate and a thick PTFE circular plate. The PTFE plate also acts as insulation. Their results showed that the heat transfer performance of nanofluids is significantly enhanced at low particle concentrations. Increasing nanoparticle concentration has a contrary effect on the heat transfer of nanofluids, so at concentrations greater than 1 vol. % of nanoparticles the heat transfer coefficient of nanofluids is less than that of the base fluid. Indeed it seems that for both nanofluids there exists an optimum nanoparticle concentration that heat transfer coefficient passes through a maximum. The optimum concentrations of Al2O3 and TiO2 nanofluids are about 0.2 and 0.1 vol. %, respectively. It is also observed that the heat transfer enhancement of TiO2 nanofluids is higher than that of the Al2O3 nanofluids. The effect of enclosure aspect ratio was also investigated and the heat transfer coefficient of nanofluids as well as the base fluid increases by increasing the aspect ratio as expected.
Corcione et al., (2012) theoretically studied the heat transfer of nanoparticle suspensions in turbulent pipe flow. Both constant pumping power and constant heat transfer rate have been investigated for different values of the Reynolds number of the base fluid in the range between 2300 and 5x106, the diameter of the suspended nanoparticles in the range between 25 nm and 100 nm, the length-to-diameter ratio of the pipe in the range between 50 and 1000, the nanofluid bulk temperature in the range between 303 K and 343 K, as well as for three different nanoparticle materials (i.e., CuO, Al2O3, and TiO2) and two different base liquids (i.e., water and ethylene glycol). The significant findings of their study was the existence of an optimal particle loading for either maximum heat transfer at constant driving power or minimum cost of operation at constant heat transfer rate. In particular, for any assigned combination of solid and liquid phases, they found that the optimal concentration of suspended nanoparticles increases as the nanofluid bulk temperature is increased, the Reynolds number of the base fluid is increased, and the length-to-diameter ratio of the pipe is decreased, while it is practically independent of the nanoparticle diameter.
Chun et al., (2008) experimentally reported the convective heat transfer of nanofluids made of transformer oil and three kinds of alumina nanoparticles in laminar flow through a double pipe heat exchanger system. The experimental system consisted of two double-pipe heat exchangers for heating and cooling of nanofluid and was made of a non-corrosive stainless steel. Their experimental data showed that the addition of nanoparticles in the fluid increases the average heat transfer coefficient of the system in laminar flow. By non-linear regression of experimental data, the correlation (Eq.26) for heat transfer coefficient was decided as follows
The surface properties of nanoparticles, particle loading, and particle shape were key factors for enhancing the heat transfer properties of nanofluids. They stated that these increases of heat transfer coefficients may be caused by the high concentration of nanoparticles in the wall side by the particle migration.
Duangthongsuk and Wongwises (2009) experimentally studied the heat transfer coefficient and friction factor of a nanofluid consisting of water and 0.2 vol. % TiO2 flowing in a horizontal double-tube counter flow heat exchanger under turbulent flow conditions. Their test section was a 1.5 m long counter flow horizontal double-tube heat exchanger with nanofluid flowing inside the tube while hot water flows in the annular. The inner tube is made from smooth copper tubing with a 9.53 mm outer diameter and an 8.13 mm inner diameter, while the outer tube is made from PVC tubing and has a 33.9 mm outer diameter and a 27.8 mm inner diameter. The test section was thermally isolated from its upstream and downstream section by plastic tubes in order to reduce the heat loss along the axial direction.
They investigated the effects of the flow Reynolds number and the temperature of the nanofluid and the temperature and flow rate of the heating fluid on the heat transfer coefficient and flow characteristics. Their results showed that the convective heat transfer coefficient of nanofluid is slightly higher than that of the base liquid by about 6 -11%. The heat transfer coefficient of the nanofluid increased with an increase in the mass flow rate of the hot water and nanofluid, and increased with a decrease in the nanofluid temperature, and the temperature of the heating fluid had no significant effect on the heat transfer coefficient of the nanofluid. They also concluded that Gnielinski correlation for predicting the heat transfer coefficient of pure fluid is not applicable to a nanofluid. But, the Pak and Cho correlation (Eq. (7)) for predicting the heat transfer coefficient of a nanofluid agreed better with their experimental results than the Xuan and Li correlation (Eq. (8)).
Duangthongsuk and Wongwises (2010) experimentally studied the heat transfer coefficient and friction factor of the TiO2-water nanofluids flowing in a horizontal double tube counter-flow heat exchanger under turbulent flow conditions. Their test fluid was TiO2 nanoparticles with diameters of 21 nm dispersed in water with volume concentrations of 0.2 - 2 vol. %. The heat transfer coefficient of nanofluids was approximately 26% greater than that of pure water and the results also showed that the heat transfer coefficient of the nanofluids at a volume concentration of 2.0 vol.% was approximately 14% lower than that of base fluids for given conditions.
Their results showed that the Pak and Cho correlation (Eq. (7)) can predict the heat transfer coefficient of nanofluids and gives results that corresponded well only with the experimental results for the volume concentration of 0.2%. However, for the volume concentrations of 0.6% and 1.0%, the Pak and Cho equation fails to predict the heat transfer performance of the nanofluids. For the pressure drop, their results showed that the pressure drop of nanofluids was slightly higher than the base fluid and increases with increasing the volume concentrations.
New heat transfer and friction factor correlations(Eqs 27 and 28) for predicting the Nusselt number and friction factor of TiO2-water nanofluids were proposed in the form of
The majority of the data falls within ±10% of the proposed equation. These equations are valid in the range of Reynolds number between 3000 and 18,000 and particle volume concentrations in the range of 0 and 1.0 vol. % for Nusselt number and 0 and 2.0 vol. % for friction factor.
Asirvatham et al., (2011) investigated the convective heat transfer of nanofluids using silver – water nanofluids under laminar, transition and turbulent flow regimes in a horizontal 4.3 mm inner-diameter tube-in-tube counter-current heat transfer test section. The volume concentration of the nanoparticles were varied from 0.3% to 0.9% in steps of 0.3% and the effects of thermo-physical properties, inlet temperature, volume concentration, and mass flow rate on heat transfer coefficient were investigated. Experiments showed that the suspended nanoparticles remarkably increased the convective heat transfer coefficient, by as much as 28.7% and 69.3% for 0.3% and 0.9% of silver content, respectively. Based on the experimental results a correlation (Eq.29) was developed to predict the Nusselt number of the silver–water nanofluid, with ±10% agreement between experiments and prediction.
Zamzamian et al., (2011) used nanofluids of aluminum oxide and copper oxide in ethylene glycol base fluid. They investigated the effect of forced convective heat transfer coefficient in turbulent flow, using a double pipe and plate heat exchangers. The inner pipe of the double pipe heat exchanger was made of copper, 12 mm in diameter and 1 mm in thickness, with a heat exchange length of 70 cm. The shell was made of green pipes, 50.8 mm in diameter. The flow inside the double pipe heat exchanger was arranged in opposite directions. The plate heat exchanger was a small, particularly manufactured model of common home radiators, 40 cm in height and 60 cm in length, exchanging heat freely with the ambience through four fins. The forced convective heat transfer coefficient of the nanofluids using theoretical correlations also calculated in order to compare the results with the experimental data. The effects of particle concentration and operating temperature on the forced convective heat transfer coefficient of the nanofluids were evaluated. The findings indicated considerable enhancement in convective heat transfer coefficient of the nanofluids as compared to the base fluid, ranging from 2% to 50%. Moreover, the results indicated that with increasing nanoparticles concentration and nanofluid temperature, the convective heat transfer coefficient of nanofluid increases.
Farajollahi et al., (2010) measured the heat transfer characteristics of γ Al2O3 /water and TiO2/water nanofluids in a shell and tube heat exchanger under turbulent flow condition. Water was allowed to flow inside the shell with 55.6 mm inside diameter and the nanofluid was passed through the 16 tubes with 6.1 mm outside diameter, 1 mm thickness, and 815 mm length. The tube pitch is 8 mm and the baffle cut and baffle spacing are 25% and 50.8 mm, respectively. The heat exchanger and pipe lines are thermally insulated to reduce heat loss to the surrounding. The effects of Peclet number, volume concentration of suspended nanoparticles, and particle type on heat transfer characteristics were investigated.
The observed the overall heat transfer coefficient of nanofluids increases significantly with Peclet number. For both nanofluids the overall heat transfer coefficient at a constant Peclet number increases with nanoparticle concentration compared to the base fluid. The experimental results for the Nusselt number of γ Al2O3/water and TiO2/water nanofluids were compared with the prediction of Xuan and Li correlation (Eq. (6)). Results show that at 0.5 vol. % of γ Al2O3 nanoparticles and at 0.3 vol. % of TiO2 nanoparticles a good agreement exists between the experimental results and the predicted values by Eq. (6) especially at higher Peclet numbers. They observed that the correlation is almost valid for the prediction of Nusselt number at low volume concentrations.
They reported that, adding of nanoparticles to the base fluid causes the significant enhancement of heat transfer characteristics. They experimentally obtained two different optimum nanoparticle concentrations for both the nanofluids. the heat transfer behavior of two nanofluids were compared and the results indicated that at a certain Peclet number, heat transfer characteristics of TiO2/water nanofluid at its optimum nanoparticle concentration are greater than those of γ Al2O3 /water nanofluid while γ Al2O3 /water nanofluid possesses better heat transfer behavior at higher nanoparticle concentrations.
The emergence of several challenging issues such as climate change, fuel price hike and fuel security have become hot topics around the world. Therefore, introducing highly efficient devices and heat recovery systems are necessary to overcome these challenges. It is reported that a high portion of industrial energy is wasted as flue gas from heating plants, boilers, etc. Leong et al., (2012) focused on the application of nanofluids as working fluids in shell and tube heat recovery exchangers in a biomass heating plant. Heat exchanger specification, nanofluid properties and mathematical formulations were taken from the literature to analyze thermal and energy performance of the heat recovery system. It was observed that the convective and overall heat transfer coefficient increased with the application of nanofluids compared to ethylene glycol or water based fluids. In addition, 7.8% of the heat transfer enhancement could be achieved with the addition of 1% copper nanoparticles in ethylene glycol based fluid at a mass flow rate of 26.3 and 116.0 kg/s for flue gas and coolant, respectively.
Jwo et al., (2010) employed Al2O3 /water nanofluid to electronic chip cooling system to evaluate the practicability of its actual performance. Their experimental variables included nanofluids of different weight concentrations (0, 0.5, and 1.0 wt. %) and the inlet water temperature at different flow values. To determine if the addition of nanoparticles has any effects on overall heat transfer performance, they conducted a comparative experiment with water first. The control variables of their study were the mass flow rate, inlet water temperature, and heating power. Having completed the control experiment with water, nanofluids of different concentrations were used to carry out the same experiment. Using the same control variables, the ratio of the overall heat transfer performance of nanofluid to the overall heat transfer performance of water was calculated, and then acquired the overall heat transfer coefficient ratios under different conditions. Based on the collected temperature data for different mass flow rates, electric input powers, and nanofluid concentrations, the overall heat transfer coefficient ratio (rU) of the MCHE (Eq.30) can be written as follows:
Where, Tm = (Tliq.in + Tliq.out)/2 is the averaged temperature of liquid traversing the MCHE.
Results showed that the overall heat transfer coefficient ratio was higher at higher nanoparticle concentrations. In other words, the overall heat transfer coefficient ratio was higher when the probability of collision between nanoparticles and the wall of the heat exchanger were increased under higher concentration, confirming that nanofluids have considerable potential for use in electronic chip cooling systems. These results confirmed that nanofluid offers higher overall heat transfer performance than water, and a higher concentration of nanoparticles provides even greater enhancement of the overall heat transfer coefficient ratio.
Gherasim et al., (2009) presented an experimental investigation of heat transfer enhancement capabilities of coolants with suspended nanoparticles (Al2O3 dispersed in water) inside a radial flow cooling device. Steady, laminar radial flow of a nanofluid between a heated disk and a flat plate with axial coolant injection has been considered. An experimental test rig was built consisting of the space between the two coaxial disks with central axial injection through the lower, high-temperature resistant PVC disk and the upper disk was machined from aluminum stock piece. They investigated the influence of disk spacing on local Nusselt number and proved that the local Nusselt number increases with a decrease in gap spacing. This behavior is obviously due to the increase of convection effects. They also analyzed the influence of particle volume fraction and Reynolds number on mean Nusselt number and found that the local Nusselt number increases with particle volume fraction. Their results showed that heat transfer enhancements are possible in radial flow cooling systems with the use of nanofluids. In general, it was noticed that the Nusselt number increases with particle volume fraction and Reynolds number and decreases with an increase in disk spacing.
Nguyen et al., (2007) investigated the heat transfer enhancement and behavior of Al2O3 nanoparticle - water mixture, for use in a closed cooling system that was destined for microprocessors or other heated electronic components. Their experimental liquid cooling system was a simple closed fluidic circuit which is mainly composed of a 5 l open reservoir and a magnetically driven pump that ensures a forced recirculation of liquid. An electrically heated block (aluminum body) was considered which simulates heat generated by a microprocessor. On top of this heated block, water-block (copper body) was installed. A thin film of high thermal conductivity grease was applied to minimize the thermal contact resistance at the interface junction between the heated block and the water-block. The assembly of heated block and water block has been thermally very well insulated with respect to the surrounding environment by means of fiberglass. Their data showed clearly that the inclusion of nanoparticles into distilled water produced a considerable enhancement of the cooling convective heat transfer coefficient. For a particular particle volume concentration of 6.8%, the heat transfer coefficient was found to increase as much as 40% compared to that of the base fluid. They observed that an increase of particle volume concentration has produced a clear decrease of the heated block temperature. Their experimental results also shown that a nanofluid with 36 nm particle size provides higher convective heat transfer coefficients than the ones given by nanofluid with 47 nm particles.
Gherasim et al., (2011) carried out a numerical investigation for heat transfer enhancement capabilities of coolants with suspended nanoparticles (Al2O3 dispersed in water) inside a confined impinging jet cooling device. They considered a steady, laminar radial flow of a nanofluid in an axis-symmetric configuration with axial coolant injection. A single phase fluid approach was adopted to numerically investigate the behavior of nanofluids. Good agreement was found between numerical results and available experimental data. Results indicated that heat transfer enhancement is possible in this application using nanofluids. In general, it was noticed that the mean Nusselt number increases with particle volume fraction and Reynolds number and decreases with an increase in disk spacing.
G. Huminic and A. Huminic (2011) numerically studied heat transfer characteristics of double-tube helical heat exchangers using nanofluids under laminar flow conditions. CuO and TiO2 nanoparticles with diameters of 24 nm dispersed in water with volume concentrations of 0.5–3 vol. % were used as the working fluid. The effect of particle concentration level and the Dean number on the heat transfer characteristics of nanofluids and water are determined. The mass flow rate of the nanofluid from the inner tube was kept and the mass flow rate of the water from the annulus was set at either half, full, or double the value. They showed the variations of the nanofluids and water temperatures, heat transfer rates and heat transfer coefficients along inner and outer tubes.
The effect of the nanoparticle concentration level on the heat transfer enhancement was calculated for different nanofluids and mass flow rate of the water. The results of the CFD analysis were used to estimate of the heat transfer coefficients and of the Dean number.
The numerical heat transfer coefficients of the nanofluid and water and Dean number were computed from the following equations (Eqs 31 and 32)
Where the average heat transfer rate is defined as
Their results showed that for 2% CuO nanoparticles in water with the same mass flow rate in inner tube and annulus, the heat transfer rate of the nanofluid was approximately 14% greater than that of pure water. They also showed that the convective heat transfer coefficients of the nanofluids and water increased with increasing of the mass flow rate and with the Dean number.
A detailed description of the state-of-the-art nanofluids research for heat transfer application in several types of heat exchangers is presented in this chapter. It is important to note that preparation of nanofluids is an important step in experiments on nanofluids. Having successfully engineering the nanofluids, the estimation of thermo physical properties of nanofluids captures the attention. Great quanta of attempts have been made to exactly predict them but large amount of variations were found. Research works on convective heat transfer using nanofluids is found to increase exponentially in the last decade. Almost all the works showed that the inclusion of nanoparticles into the base fluids has produced a considerable augmentation of the heat transfer coefficient that clearly increases with an increase of the particle concentration. It was reported by many of the researchers that the increase in the effective thermal conductivity and huge chaotic movement of nanoparticles with increasing particle concentration is mainly responsible for heat transfer enhancement. However, there exists aplenty of controversy and inconsistency among the reported results. The outcome of all heat transfer works using nanofluids showed that our current understanding on nanofluids is still quite limited. There are a number of challenges facing the nanofluids community ranging from formulation, practical application to mechanism understanding. Engineering suitable nanofluids with controlled particle size and morphology for heat transfer applications is still a big challenge. Besides thermal conductivity effect, future research should consider other properties, especially viscosity and wettability, and examine systematically their influence on flow and heat transfer. An in-depth understanding of the interactions between particles, stabilizers, the suspending liquid and the heating surface will be important for applications.
Courmayeur (1,224 m asl) is a small mountain town located in the Aosta Valley Autonomous Region, in northwestern Italy. It is a famous tourist destination whose fame and history are largely related to the presence of the Mont Blanc massif, which is one of the most renowned attractions in the Alps. Two glacial valleys, the Val Veny to the west and the Val Ferret to the east, run parallel to the massif. Every year, these valleys become the holiday destination of thousands of tourists (more than 350,000 in 2019), and amongst them are many climbers and hikers who are drawn to this area for its majestic landscapes. Courmayeur is a typical example of an Alpine location whose population can easily triple due to the presence of tourists. In addition, this area is home to the Mont Blanc Tunnel, which is part of a crucial roadway linking Italy and the rest of Europe, as well as to the new Skyway Monte Bianco cable car, which crosses the Mont Blanc massif and connects Courmayeur to Chamonix.
Over the last two decades, in many alpine areas, the effects of global warming have increased the frequency of numerous types of gravitational slope instabilities (i.e. rockfalls, ice avalanches and debris flows), especially at high elevations, mainly due to glacial and permafrost degradation or extreme rainfall events. The Val Ferret PAR (Figure 1) has been affected by all these hazards.
The Aosta Valley Autonomous Region (northwestern Italy) and Val Ferret PAR in the municipality of Courmayeur.
The Aosta Valley Autonomous Region is a wide mountain basin consisting of a long main valley (100 km), corresponding to the Dora Baltea hydrographic basin and thirty major tributary valleys.
This area has a temperate oceanic climate, which is transitional to hemi-continental and lacks a dry season. The mean annual temperature varies greatly in the different areas of the valley, mostly because of the considerable altitude differences. For example, the annual average temperature ranges between +10 and +12°C at 500 m, but it is around −7.5°C at 1,200 m [1].
The region has an alpine pluviometric regime, with two rainfall maxima in the middle seasons and two minima during summer and winter. In the central sector of the region, the maximum rainfall occurs in autumn, while the minimum rainfall occurs in summer. The central area of the Aosta Valley is one of the driest sites in the Alps (550 mm/y). The mean annual precipitation is about 950 mm and increases with altitude and towards the watersheds, with the highest contribution on the Great St Bernard Pass (2,476 m) on the northern watershed, which averages 2,000 mm/y (data set since 1817) [1].
The topographic elevation ranges from 400 m, where the valley mouth merges into the Po Plain, to the 4,810 m summit of Mont Blanc, the highest peak in the European Alps. From a geological point of view, the Aosta Valley region belongs to the axial zone of the Western Alps, an imbricated stack of continental and oceanic metamorphic complexes [2].
A very high regional mean altitude of around 2,100 m guarantees the presence of numerous glaciers (precisely, 175), which, according to the Aosta Valley Glacier Inventory (updated in 2019), have an overall extent of approximately 120 km2, or 3.6% of the basin [3]. Amongst the morphodynamic agents in mountain environments, glaciers play the most important role, by modeling both erosional and depositional landforms as well as indirectly influencing slope dynamics.
Watercourses represent another crucial geomorphic agent, largely affecting the Aosta Valley’s landscape through the progressive erosional deepening of valleys. Evident signs of the constant shaping and reshaping of this territory are the widespread alluvial and mixed fans, mainly created by debris-flow phenomena. Finally, yet importantly, gravitational phenomena also contribute to the evolution of the Aosta Valley’s geomorphology.
Located at the foot of the Mont Blanc massif, the longitudinal profile of the Val Ferret valley progresses in a succession of steps, a distinctive trait of a glacial valley (Figure 2), from the village of La Palud to the Col Ferret. Small settlements populate the valley floor, and even if they are not permanently inhabited, they are renowned summer and winter holiday destinations where tourists come to engage in cross-country and backcountry skiing during the winter season and alpinism, mountain biking and trekking in summer. Thus, many economic activities, such as hotels, restaurants and campsites, flourish in this valley.
Mont Blanc, with Val Veny on the left and Val Ferret on the right (photo: FMS).
From a naturalistic point of view, Val Ferret is a Special Protection Area (SPA) [4] that is home to numerous species of nesting birds. Along with Mont Blanc’s glacial environments, the Talweg of the Val Ferret valley, an important reservoir for the conservation of hygrophilous plants, is also listed as a Site of Community Importance (SCI) [5].
Larch and pine forests cover both sides of Val Ferret up to an altitude of 1,750 m (on average). The right bank has steep rocky sides that descend from the peaks of the Mont Blanc range to the valley floor at 1,400 m. This incredible drop of approximately 2,600 m is the perfect terrain for avalanches and debris flows. In fact, these natural hazards usually originate well above the timberline and are often associated with rockfalls and extreme precipitation events or, sometimes, to the partial collapses of glacier fronts (i.e. Grandes Jorasses and Planpincieux glaciers). These events flow over existing stream incisions, and their frequency effectively precludes the presence of forests in these areas. The left bank, facing northwest, has more continuous forest cover, up to 2,000 m. On this side of the valley, the forest provides better protection from natural hazards, but despite the milder slopes, avalanche phenomena can still occur.
During the winter season, the Val Ferret road is open up to the village of Planpincieux, which is located at the entrance of the valley. From this little town, cross-country ski trails extend to the village of Lavachey, halfway up the valley. The end of Val Ferret is accessible only with ski mountaineering equipment. During the cold season, the valley is a famous tourist destination, even though the high frequency of snow avalanches make access to the valley quite difficult (Figure 3).
Aerial view of avalanches between Tête de Bernarde and Mont de La Saxe with the hamlet of Mayencet in the center (photo: FMS).
In mountain areas, local avalanche risk management has always been of crucial importance. In addition to being a complicated task, it requires excellent knowledge of the terrain and avalanche sites, as well as a deep understanding of the seasonal snow cover and the alpine micro-meteorology. For these reasons, the Aosta Valley Autonomous Region has officially established the Local Avalanche Commission (CLV – Commissione Locale Valanghe) [6].
Born to support the decision-making process of avalanche risk management in municipal areas, CLVs have the task of carrying out forecasting activities and assessing the snow and the meteorological conditions and the stability state of the snow masses in the designated areas. It also acts as a body of vigilance, alert and intervention in situations of risk and emergency management, in order to ensure, at the local level, the control of dangerous situations in its area of competence. CLVs also provide a civil protection technical consultative opinion to the mayor.
Regarding the Val Ferret PAR, in the event of avalanche danger, the mayor issues an order for the permanent closure of the municipal road of the Val Ferret valley and, if there is high risk to inhabited areas, the mayor can order the evacuation of the whole valley. The closure is generally preceded by a controlled evacuation plan, according to an intervention regulation as part of the Municipal Civil Protection Plan.
Snow avalanche data for the Val Ferret PAR are obtained from the Snow Avalanche Inventory of the Aosta Valley Autonomous Region [7]. This database has been updated every winter since the early 1970s and since 2005, it has been fully available online. Table 1 summarizes some data regarding Val Ferret.
Avalanche sites | 80 |
---|---|
Total avalanche events | 767 |
Avalanche events causing damage to forest cover | 66 |
Avalanche events involving people | 17 |
Avalanche events involving civil buildings | 45 |
Avalanche events affecting the municipal road system | 60 |
Avalanche events affecting the state road system | 17 |
Avalanche sites and avalanche events in Val Ferret (data: 2020 update) [7].
In summer Val Ferret is entirely accessible. The municipal road is open up to the hamlet of Arp-Nouva, and side paths are available for mountain bikers and trekkers. On the valley floor there are numerous tourist facilities, such as hotels, restaurants, campsites and a golf course. The valley is crossed by numerous hiking paths, the most famous of which is the Tour du Mont Blanc, a 170 km hiking trail that surrounds the Mont Blanc massif and passes through parts of Italy (Aosta Valley), France (Haute-Savoie and Savoie) and Switzerland (Valais). Every year, this trail becomes the stage of the Ultra-Trail du Mont Blanc, a world-renowned trail running race with up to 10,000 participants.
Given the large number of visitors throughout the whole summer season, risk management is more complex compared to the winter period. Regarding hydrogeological instability, the natural hazards that endanger the valley during summer are more rare and harder to forecast (i.e. debris flows) in comparison to the snow avalanches of the winter season. Alerting abilities are thus less developed, especially because the event precursors are not as evident as those in winter hazards. The Functional Center of the Aosta Valley Autonomous Region manages the meteorological alert system. In Italy, functional centers have been established in compliance with a decree of the President of the Council of Ministers [8] in 1999, following a tragic event in the year before, in an effort to better the civil protection system linked to weather-related hazards.
This legislative act established, for the first time in Italy, an integrated monitoring network, enabling the diffusion of hydro-pluviometric data throughout the whole country, while integrating them in a civil protection system designed to rapidly alert the population in case of a weather-related hazard.
In this context, the different functional centers create a tight net of forecasting, monitoring and surveillance bodies and provide technical support to the competent agencies dealing with risk management and civil protection.
The principal tool used by the functional centers to alert the different agencies and the municipalities is the Bulletin of Critical Issues. This concise report describes, with a progressive color scale, the increasing danger and the expected scenarios according to the forecasted weather and hydrological conditions.
In the Aosta Valley’s Bulletin of Critical Issues, the regional territory is subdivided into four areas, for which a risk forecast is presented, in order to activate only the municipalities, authorities and emergency management bodies actually involved in the forecasted event. The municipalities included in the alert areas are required to activate the operational phases defined within the Municipal Civil Protection Plan. The data acquired regarding landslides, rockfalls, floods and debris flows affecting the regional territory are reported in a specific inventory called the “Catasto Dissesti” [9].
During the summer season, the most influential gravitational processes in the Val Ferret PAR are linked to water-related hazards, more precisely to floods and debris flows. These phenomena occasionally reach the valley floor, endangering crowded areas, such as houses, hiking trails and the main road (Figure 4).
Debris flow deposit in Val Ferret and clearing operations of timber and debris from the stream bed (photo: FMS).
Streams and river basins that can potentially be affected by these phenomena are very numerous; there are hundreds in the entire regional territory and at least twenty in Val Ferret alone.
In addition, glacier-related hazards have to be considered as well in the risk assessment analysis of the Val Ferret PAR. In fact, the current risk management plan already considers ice avalanches originating from the Mont Blanc massif as a potential threat to the valley floor.
This subject is addressed in depth in Chapter 3, which is dedicated to the best practices carried out in Val Ferret, such as the open-air laboratory of the Grandes Jorasses glaciers.
About 30% of the total area of the Aosta Valley Autonomous Region is wooded, amounting to approximately 97,970 hectares [10], of which 38,207 hectares (39% of the total forest area) is public property and 59,763 hectares (61% of the total forest area) is private property.
The forestation index might seem low, especially when compared with the average of other Alpine regions. However, the surface morphology and characteristics of the Aosta Valley have to be taken into account. With an average altitude above 2,100 m, an entirely mountainous territory and the considerable presence of glacial and periglacial environments, the area available to forests is limited. In fact, excluding these barren areas would increase the potential surface area of forest to around 195,000 hectares, and would increase the forestation index from 30 to 45%. Therefore, the per capita area of forest in the Aosta Valley is 7,500 m2, compared to the Italian average of 1,600 m2. This clearly shows that the current wooded area is quantitatively a significant part of the regional territory. Potentially, this area can still see further expansion, however, and the forest has already regained areas where, in the last three decades, land-use has changed. The constant increase recorded for about a century (Table 2) is mainly the result of the decline of agriculture work in mountain areas, lower livestock pressure inside and on the edge of the forest, and reforestation carried out since the first post-war period.
Year | Area (hectares) |
---|---|
1962 | 66,000 |
1974 | 75,000 |
1996 | 86,550 |
1999 | 89,539 |
2011 | 97,970 |
Variation in the forested areas of the Aosta Valley in the past years [11].
Since the 1960s, the regional administration has implemented regional policies aimed at expanding knowledge about its forest heritage. An important planning instrument is the “Economic Plans of Forestry and Pastoral Assets”, which provides a subdivision of property into separate economic classes. The different classes are based on the prevailing functions that wooded areas are required to satisfy: productive, protective, touristic-recreational, and naturalistic. The economic plans define a set of rules that establish the extent of the interventions needed for conservation purposes and the strengthening of the forest.
These regulatory plans for the region, municipalities and consortiums have been constantly updated for 50 years. However, in 2010, due to financial reasons, the review was temporarily interrupted. Today, opportunities to complete the economic plan review, as well as to develop the agricultural and forestry sectors, arise mainly through the EU Rural Development Plan. As a matter of fact, The Rural Development Program 2014–2020 is currently operational in the Aosta Valley Autonomous Region, carrying out measures to guarantee forest conservation and to increase the potential of the forest-wood supply chain. The forestry plan for the Municipality of Courmayeur and the Val Ferret PAR is currently in place and valid until 2022.
The forest typology of the Aosta Valley Autonomous Region has been analyzed since 1998 in the context of various Interreg projects that have led to the creation of the publication “I tipi forestali della Valle d’Aosta” [12], which identifies 15 forest categories with 49 forest types. As part of the European Alcotra Renerfor Project, in 2011, a new forest map was created [10]. This inventory identified 21 forest categories (14 broad-leaved, 6 conifer and one mixed) and 95 forest types. High forests account for 68% of the wooded area as the prevailing silvicultural system, while coppices are limited to about 15% of the area. Conifers are certainly the most represented species, exceeding 90% in high forests. In particular, in order of occurrence, there are larch, spruce, Scots pine, stone pine and silver fir. Less common are broad-leaved trees, of which less than 10% are in high forest systems. The majority of broad-leaved trees are in coppice populations, generally old growth, and in spontaneous high forest successions. The most represented species are chestnut, downy oak, poplar, ash, cherry, rowan, sycamore maple and beech.
In regard to the Val Ferret PAR, Table 3 shows the forest types according to the technical report of European Environmental Agency No 9/2006 “European forest types – Categories and types for sustainable forest management reporting and policy” [13].
CODE | Forest type | % |
---|---|---|
3.1 | Subalpine larch-arolla pine and dwarf pine forest | 71 |
3.2 | Subalpine and montane spruce and montane mixed spruce-silver fir forest | 10 |
8.4 | Portuguese oak and Mirbeck’s oak Iberian forest | 3 |
11.2 | Alder swamp forest | 13 |
12.1 | Riparian forest | < 1 |
12.2 | Fluvial forest | 1 |
13.4 | Southern boreal birch forest | 2 |
Val Ferret forest types according to European environmental agency No 9/2006 “European forest types – Categories and types for sustainable forest management reporting and policy” [13].
The subdivision into prevalent functional destinations, with the exclusion of unmanaged forests, is linked to the different conditions of fertility, location and accessibility of each specific forested area. In the Aosta Valley, the main role of forests is protection from natural hazards, even if partially combined with timber production. In fact, it appears that about 80% of the forests of the Aosta Valley perform an irreplaceable protective function against rockfalls, avalanches, debris flows, floods and soil erosion.
With regard to forests protecting against natural hazards, a recent Italian legislative decree [14] defines “forest of direct protection” as wooded area that, thanks to its peculiar position, performs the function of the direct protection of people, goods and infrastructure from natural hazards such as avalanches, rockfall, landslides and debris flows, preventing the occurrence of such events or mitigating their effect.
In 2006, a publication containing guidelines for the management of protective forests [11] was published by the Aosta Valley Autonomous Region together with the Piedmont Region. This study, starting from the exchange of experiences between foresters in the Western Alps, provides the necessary silvicultural path needed to enable a specific forest to perform its protective role in a lasting and effective manner. These suggestions were defined considering the main natural hazards characterizing the area. However, the frequency and intensity of natural hazards seem to change, particularly under the effect of climate change. In fact, in response to shifts in temperature and precipitation, many climate models predict an increase in the frequency of extreme weather events (i.e. storms and fires), an impact on the population dynamics of insects, an alteration of ecological niches (species replacement), and an increase in sensitivity to pathogens.
The collaboration between the forest departments of the Piedmont and Aosta Valley regions, has been extended thanks to the Alcotra 2007–2013 Project [15]. This cross-border project between Italy (Aosta Valley and Piedmont regions) and Switzerland (Canton du Valais) resulted in, amongst other things, the publication of cognitive complements on the management of protective forests. The research and experience gained in these five years have been summarized in two publications [16, 17], which expand on the first one [11] through the deepening of the interactions between natural hazards and the stability of protective forest.
In the Aosta Valley, approximately 44,000 hectares of forest plays a “direct object protection” role, equivalent to 45% of the total forest coverage (Figure 5). This ratio is even greater in the Val Ferret PAR, despite only 10% of its territory being covered by forests.
Map of protective forests in the Aosta Valley Autonomous Region (data from [
Research on Mont Blanc has always been an interdisciplinary effort, with numerous national and international projects built upon high mountain observation, mass balance measurement and glacial and periglacial environment monitoring. It is of crucial importance to study glaciers and their evolution, especially during this period of rapid change in which global warming is expected to drastically reduce glaciated areas and increase their instability. Moreover, in most mountainous regions of the world and especially in densely populated areas such as the European Alps, glacier-related hazards are a threat to lives and sustainable development. A thorough understanding of glacier dynamics is essential for glacier risk management, enabling the development of mitigation strategies against climate change cryosphere alteration. In order to attain this objective, scientific observation, data acquisition and analysis are fundamental, but the severe mountain environment where most glaciers are located, makes the survey activities very complex. Accessing these impervious areas implies high economic costs as well as potential risks for technicians and scientists. However, the recent development of remote sensing systems allows investigations to be carried out with minimal risks, enabling the measurement of many parameters without even accessing the glaciers, thus representing in many cases the most suitable monitoring solution.
Spaceborne earth observation techniques enable, for example, the extraction of information such as glacier topography, albedo, equilibrium line, mass balance or flow velocity. However, the spatial and temporal resolution of satellite data is not yet effective enough to the measure fast processes (e.g. sub-daily movements - cm/day), and often close-range remote sensing is the best monitoring solution for glacier stability evaluation and risk assessment. The effectiveness of these techniques has been successfully tested and used for several years in the Val Ferret PAR for the polythermal glacial complex of the Grandes Jorasses and Planpincieux glaciers (Figure 6). These glaciers, located on the Italian side of the Grandes Jorasses peak, have a southeastern orientation, and their elevation ranges from 2,600 m to 4,200 m. The research and survey activities that have been conducted here since 2009 are arguably amongst the most intensive in the European Alps. Today, this area is an open-air laboratory for close-range remote sensing monitoring systems focused on developing new monitoring solutions and advanced research activities.
Overview of the Planpincieux and Grandes Jorasses glaciers. Orange and blue areas indicate the Montitaz lobe and the Whymper Serac, respectively.
Several documented ice avalanches and glacial floods (1929, 1952, 1982, 2005, 2017), which, in some cases, have threatened the village of Planpincieux and damaged the municipal road, have been linked to the Planpincieux Glacier (Figure 7) [18].
Aerial view of the Planpincieux Glacier and Montitaz stream (photo: FMS).
The Planpincieux Glacier’s accumulation area consists of two separate glacial cirques, the largest of which is located at the base of the Grandes Jorasses peak.
The two cirques converge into a singular basin area that feeds the two lower lobes, whose fronts are located at an altitude of about 2,600 m. The ice flow is channeled mainly into the right lobe, which, dynamically, is a very active area, with an average slope of 32° and has a strongly crevassed morphology.
The right lobe, characterized by a vertical front wall that reaches heights of up to 30 meters, leads into the steep Montitaz stream (Figure 7), where numerous ice collapses occur, mainly during the summer season.
Since 2011, when a large crevasse opened in the lower part of the right lobe, the glacier has been closely monitored using different technologies and methodologies. During this 10-year study period, speed increases of the entire right side of the glacier tongue have been recorded (up to 2 m/day at the glacier front), especially during the summer seasons. This fast-flowing motion is mainly induced by the flow of water present between the bedrock and the ice.
In the summer of 2019, a volume of about 300,000 m3 showed multiple signs of possible collapse. Phases of marked accelerations and decelerations of the ice flow, a subglacial drainage network distributed under most of the tongue, and a state of pervasive fracturation of the ice were all conditions that were present at the Planpincieux front. These unstable conditions could lead to the sudden detachment of the entire portion of the glacier, which, because it is hanging over a steep slope, could generate a large ice avalanche that could potentially reach the valley floor. These same conditions have been recorded in all known cases of temperate glaciers destabilizations: Le Tour – 1949 [19], Allalin – 1965, Fee Glacier – 2009, and Allalin – 2000 [20].
The Planpincieux Glacier is monitored by two time-lapse cameras with different focal lengths placed on the opposite side of the Val Ferret valley, 3,800 m from the glacier [21]. The monitoring station is equipped with two solar panels, and an electric cell for power supply and is remotely controlled by a single-board computer connected to a server. A robotic webcam was installed in 2018 to survey the functionality of the station. This monitoring system has been active since 2013 and acquires images at hourly frequency, enabling the accurate identification of the different phenomena affecting the glacier over time. Between 2013 and 2020, this camera system collected more than 40,000 images, contributing to one of the longest continuous series of hourly images in the European Alps.
The surface kinematics of the right lobe of the Planpincieux Glacier has been deeply investigated with image analysis of eight years of time-lapse monitoring [18 21]. The images are processed with a digital image correlation technique to estimate surface ice flow velocities (Figure 8).
Digital image correlation of the right anterior lobe of the Planpincieux Glacier. The displacement difference in the frontal area of the glacier, below the large crevasse, is clearly visible.
The ice flow pattern is often composed of distinct kinematic domains, especially during summer. Observation of this phenomenon may indicate the action of high strain rates localized in areas where large crevasses appear.
The behavior of the glacier snout is noteworthy because it reveals the occurrence of a few speed-up periods per year, which can end in significant ice collapses.
The Planpincieux Glacier is likely one of the few glaciers for which terrestrial radar interferometry investigations have been carried out using four different radar models [18], namely: GPRI™ (Gamma Remote Sensing), IBIS-L™ (IDS GeoRadar), FastGBSAR-S™ (MetaSensing) and LiSALab™ GbInSAR (Ellegi).
The surface kinematics of the glaciers was surveyed in five campaigns, in 2013, 2014, 2015, 2016 and 2019. The first two campaigns were conducted using the GPRI™ real-aperture radar (RAR), which surveyed the glacier from both the valley floor and the valley ridge opposite to the glacier. Both campaigns lasted for two days and were able to detect the surface displacement of the glacier tongue. For the following surveys, synthetic aperture radars (GB-SAR) were used.
Thanks to the 2015 campaign, which lasted for a longer period compared to the previous ones, it was possible to distinguish the different kinematic domains of the Montitaz Lobe, although the meteorological conditions were severe, and the radar acquisitions were affected by a strong atmospheric phase screen.
In order to minimize the atmospheric disturbance, FastGBSAR-S™ measurements with an acquisition frequency of 10 seconds, were carried out in 2016. The last investigation (LiSALab™ GBInSAR) began at the end of September 2019 for civil protection purposes (Figure 9). This campaign, which was still active during the writing of this article, is by far the longest, allowing for a dataset of almost two years.
Cumulated displacement measured by the LiSALab™ GBInSAR from December 2019 to march 2021.
During the Planpincieux Glacier ‘s most active phases, velocity displacements up to 200 cm/day were recorded on the right lobe, while movements on the left lobe never exceeded 20 cm per day. This separation and difference in surface velocities occurs during the summer season in particular.
In 2020, an AVYX™ Doppler radar (Geopraevent) was installed for real-time detection of ice avalanches from the Planpincieux Glacier (Figure 10). After the calibration phase and numerous simulations, the alert system went into operation at the beginning of July 2020. This system includes automatic road closures upon avalanche detection in the defined “closure zone”. The road is automatically re-opened if the avalanche does not reach the “danger zone”, which is a specific area at the bottom of the slope visible from the radar location.
Consecutive Doppler radar ice collapse detections from the Planpincieux Glacier (photo: Geopraevent AG).
The avalanche radar system placed in Planpincieux detected 103 avalanche events between July and December 2020. Of these, 75 were detected within, or at the border of, the closure zone, which led to the closure of the road. The radar further detected 28 events outside the closure zone which did not lead to a road closure.
In the summer of 2020, a water measurement system was installed in the Montitaz stream, at the end of the right lobe of the Planpincieux Glacier. This data could be useful for eventually identifying water pockets retained by the glacial body.
This system has been tested throughout the whole summer season but snow and ice deposition into the Montitaz channel, from glacier ice collapses, have made these data somewhat unreliable.
The south face of the Grandes Jorasses peak contains two steep glacial cirques, which form the accumulation zone of the Grandes Jorasses Glacier. The Whymper Serac, whose front is located at 3,800 m, forms the left cirque (Figure 11) and is defined as an unbalanced cold hanging glacier.
Aerial view of the Whymper Serac of the Grandes Jorasses Glacier (photo: FMS).
When hanging glaciers are entirely located above equilibrium-line altitude, ice avalanching becomes the dominant form of glacial ablation. Morphological evidence and historical data indicate, in fact, that the Whymper Serac is progressively increasing in volume, reaching unstable geometries that are subject to recurrent breakoffs. The time between these collapse events ranges from a few years to more than a decade.
Therefore, a collapse can also occur during the cold season, when it might easily trigger a large snow avalanche that could seriously threaten the buildings and the road located on the valley floor. Four collapse events have been well documented so far: August 1993, June 1998 [22] (Figure 12), September 2014 [23] and November 2020.
Aerial view of the deposit of the 1998 collapse. The ice and snow mass stopped a few hundred meters from the municipal road and houses of Val Ferret (photo: Lorenzo Cosson).
Despite the absence of fresh snow and the limited dimension of the avalanche triggered by the 1993 event, eight mountaineers that were climbing in the area lost their lives.
Since 2010, the Whymper Serac has been continuously monitored by a time-lapse camera placed on the Skyway Monte Bianco cable car station platform at Pointe Helbronner. Since 2019, digital image correlation has been applied to the images of the Whymper Serac in order to measure the surface displacement. However, due to the high brightness, low color contrast and smooth texture of the scene, measuring the ice flow is more difficult than for the Planpincieux Glacier. This requires a robust outlier correction method to remove the artifacts present in the displacement maps.
Surface velocity measurements of the Whymper Serac from a robotic total station installed in front of the village of Planpincieux 4,800 m from the glacier have been continuously taken since 2010 [22, 23]. The survey is conducted with a Leica TM30 that operates automatically on target recognition mode. The reflector network is composed of several prisms installed on poles inserted into the serac’s unstable portions (Figure 13), while a few prisms placed in the surrounding bedrock serve as reference points. A complete acquisition of the entire network is conducted every two hours, tracing the position of the prisms and enabling the analysis of surface displacements.
One of the prisms positioned on the Whymper Serac, pointing towards the robotic total station located in the hamlet of Planpincieux (photo: FMS).
Even if the sensor-to-target distance is beyond the operating limits declared by the producer, the instrument works correctly. However, extreme atmospheric conditions due to the high-mountain elevation, such as heavy snowfall and strong wind, occur frequently, causing the loss of some reflectors, which must be replaced with the help of mountain guides. It is thus clear that measuring the surface velocity of the Whymper Serac with a robotic total station is a difficult task, and a robust data processing method has been developed especially for it. Nonetheless, this data allowed the break-off of the serac in October 2014 to be forecasted ten days in advance [23].
In 2020, an IBIS-FM™ GbInSAR (IDS GeoRadar) was installed to test the capability of radar to monitor the Whymper Serac displacements beyond the instrument’s operating limits (approximately 5 km) and in unfavorable environmental conditions. In addition to the correlation between radar data and the existing monitoring system, this new tool was of fundamental importance in identifying the kinematic domains that underwent a progressive acceleration before the ice collapses of 18 October and 11 November 2020.
Starting from the end of October 2020 and during the month of November, a further test was carried out with a LiSALab™ GbInSAR (Ellegi) under the same conditions. This experimentation allowed the acquisition of important data allowing the comparison between the two radar systems, especially regarding the sensitivity to the glacial velocity increase before the collapse of the 11 November 2020 (Figure 14).
Comparison of the measurements (surface velocity) made by two different GbInSAR before the failure of 11 November 2020.
Due to climate change, the high altitude glaciers in the Mont Blanc massif are known to be warming. If the base of these glaciers nears the melting point, they could slide on their beds and become unstable, causing massive ice avalanches.
Observations of internal temperatures of the Whymper Serac were made for the first time in 1997, with thermistors installed in boreholes (Figure 15). At that time, the recorded ice temperatures where all below 0°C. These measurements were repeated in October 2020 (Figure 16).
Preparation of boreholes on the Whymper Serac in 1997 (photo: FMS archive).
Preparation of the working site in 2020 (photo: FMS).
According to the new observations, the upper part of the glacier, near the rimaye, seems temperate (0°C), while the downstream part, near the front, remains at a negative temperature.
In comparison with the observations of 1997, it seems that the ice has warmed significantly, but it has to be considered that the glacier does not have the same geometric configuration (especially after the substantial collapse of 1998) and that the topography of the current glacier is, undoubtedly, very different from that of 1997. It should also be noted that the glacier, which is located on a very steep slope, is strongly fractured and that some of the temperature profiles (in 1997 and in 2020) have been disturbed by the presence of crevasses. Nevertheless, the warming of this glacier ice over the past 23 years is clearly evident. In order to collect more data, a new measurement campaign has been planned for 2021.
A helicopter-borne ground-penetrating radar (GPR) survey was conducted on the Planpincieux - Grandes Jorasses glacial complex in 2013, and 16 GPR traces homogeneously distributed on the glacial surface were acquired.
Due to the difficulties caused by the rough glacier surface and numerous crevasses, which often caused the scattering of electromagnetic waves and obscured the reflections, there was considerable noise in the radar data. However, in the areas of high-profile densities, it was possible to estimate the ice thickness quite reliably.
In the framework of the stability assessment of the lowest part of the Planpincieux Glacier, GPR measurements were repeated in 2020 with a novel dual polarization system. In total, 12 km of profile data were acquired. The data quality allowed the identification of the bedrock for 7 km of profile data. The ice thickness of the Planpincieux - Grandes Jorasses glacial complex ranges between 10 and 100 m, while in the unstable lower part of the Planpincieux Glacier, the thickness varies between 20 and 60 m. By comparing the two measurement campaigns (2013–2020), it can be stated that the two data sets do not provide any evidence of a significant change in the ice thickness during this period. In addition to the glacial thickness data, important information regarding altitude and rough morphology of the bedrock was obtained.
Digital Elevation Models (DEMs) obtained during LiDAR and regional terrestrial laser scanning surveys and from specific photogrammetric surveys by drone and helicopter (Figure 17) enable the monitoring of the morphological evolution of the glacier surface. Furthermore, consecutive surveys can be analyzed through DEM of Difference (DoD) calculation, allowing the estimation of the surface elevation change between different periods. This can allow, for example, the measurement of the current instable ice volume or the ice mass lost or gained by the glacier.
Point cloud rendering of the Whymper Serac obtained from a drone survey.
Since 2019, numerous surveys of the topography have been carried out in order to identify and quantitatively determine the morphological changes of the two glaciers.
In 2009, a numerical simulation of ice avalanches from the Whymper Serac was carried out using the RAMMS::Avalanche [24] and RAMMS::RKE Rock-Ice models developed by the Institute for Snow and Avalanche Research (SLF).
In 2012 and 2020, these activities were repeated for the Planpincieux Glacier, using new software versions and updated terrain models.
In parallel, Fondazione Montagna sicura, in collaboration with the University of Vienna and University of Graz, carried out the same simulations using the r.randomwalk [25] and r.avaflow [26] models.
The results obtained from the two different methodologies are comparable and confirm the robustness of the analyses (Figure 18), which, for the calibration of the parameters, were based on the back calculation of ice avalanches from the Planpincieux Glacier that were mapped between 2017 and 2020.
Back calculation from the RAMMS model (left) and r.avaflow model (right) of an ice collapse mapped in 2017.
The results of the simulations were then translated into risk scenarios and safety concepts based on the unstable volumes of the glacier.
The Val Ferret valley was included as a PAR of the GreenRisk4Alps project since it is both a famous tourist location at the foot of Mont Blanc and a high-risk area for all types of mass movement processes. Risk management in this area includes, in addition to the construction of structural measures, both site-specific knowledge and forecasting capabilities of the Aosta Valley’s CLVs and Functional Center. More than 45% of the PAR’s total wood coverage plays a “direct object protection” role, proving to be effective not only in reducing soil erosion but also in preventing avalanche release and mitigating rockfall impact in runout zones. Nonetheless, glacier-related hazards develop well above timberline in high mountain environments and in areas that are difficult to access, making both technical and ecosystem-based measures partially or completely ineffective. Monitoring and early warning systems are therefore essential, and remote sensing methods that are able to measure many parameters without accessing the glaciers often represent the most suitable solution. For these reasons, the Grandes Jorasses and Planpincieux glacial complex is a reference area for natural risk assessment and management, where different close-range remote sensing techniques can be used and tested in an open-air laboratory that improves our knowledge of new technologies while increasing our understanding of the recent evolution of alpine glacial environments.
A special thanks to everyone who contributed to the production of the materials used for the drafting of this text.
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\n\n1. RETRACTIONS
\n\nA Retraction of a Chapter will be issued by the Academic Editor, either following an Author’s request to do so or when there is a 3rd party report of scientific misconduct. Upon receipt of a report by a 3rd party, the Academic Editor will investigate any allegations of scientific misconduct, working in cooperation with the Author(s) and their institution(s).
\n\nA formal Retraction will be issued when there is clear and conclusive evidence of any of the following:
\n\nPublishing of a Retraction Notice will adhere to the following guidelines:
\n\n1.2. REMOVALS AND CANCELLATIONS
\n\n2. STATEMENTS OF CONCERN
\n\nA Statement of Concern detailing alleged misconduct will be issued by the Academic Editor or publisher following a 3rd party report of scientific misconduct when:
\n\nIntechOpen believes that the number of occasions on which a Statement of Concern is issued will be very few in number. In all cases when such a decision has been taken by the Academic Editor the decision will be reviewed by another editor to whom the author can make representations.
\n\n3. CORRECTIONS
\n\nA Correction will be issued by the Academic Editor when:
\n\n3.1. ERRATUM
\n\nAn Erratum will be issued by the Academic Editor when it is determined that a mistake in a Chapter originates from the production process handled by the publisher.
\n\nA published Erratum will adhere to the Retraction Notice publishing guidelines outlined above.
\n\n3.2. CORRIGENDUM
\n\nA Corrigendum will be issued by the Academic Editor when it is determined that a mistake in a Chapter is a result of an Author’s miscalculation or oversight. A published Corrigendum will adhere to the Retraction Notice publishing guidelines outlined above.
\n\n4. FINAL REMARKS
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