The values of heat source parameters.
\r\n\t
",isbn:"978-1-83881-922-4",printIsbn:"978-1-83881-921-7",pdfIsbn:"978-1-83881-923-1",doi:null,price:0,priceEur:0,priceUsd:0,slug:null,numberOfPages:0,isOpenForSubmission:!0,hash:"dcfc52d92f694b0848977a3c11c13d00",bookSignature:"Dr. Fiaz Ahmad and Prof. Muhammad Sultan",publishedDate:null,coverURL:"https://cdn.intechopen.com/books/images_new/10454.jpg",keywords:"Agricultural Engineering, Technologies, Application, Sustainable Agriculture, Information Technology in Agriculture, Food Security, Renewable Energies, Precision Farming, Smart Agriculture, Farm Mechanization, Robotics, Post Harvest Technologies",numberOfDownloads:null,numberOfWosCitations:0,numberOfCrossrefCitations:null,numberOfDimensionsCitations:null,numberOfTotalCitations:null,isAvailableForWebshopOrdering:!0,dateEndFirstStepPublish:"November 25th 2020",dateEndSecondStepPublish:"December 23rd 2020",dateEndThirdStepPublish:"February 21st 2021",dateEndFourthStepPublish:"May 12th 2021",dateEndFifthStepPublish:"July 11th 2021",remainingDaysToSecondStep:"25 days",secondStepPassed:!0,currentStepOfPublishingProcess:3,editedByType:null,kuFlag:!1,biosketch:"Dr. Ahmad is a researcher in the field of agricultural mechanization and agricultural equipment engineering, in-charge of Farm Machinery Design Laboratory at Bahauddin Zakariya University, with expertise in modeling and simulation. He applied for two patents at the national level.",coeditorOneBiosketch:"Renowned researcher with a focus on developing energy-efficient heat- and/or water-driven temperature and humidity control systems for agricultural storage, greenhouse, agricultural livestock and poultry applications including HVAC, desiccant air-conditioning, adsorption, Maisotsenko cycle (M-cycle), and adsorption desalination.",coeditorTwoBiosketch:null,coeditorThreeBiosketch:null,coeditorFourBiosketch:null,coeditorFiveBiosketch:null,editors:[{id:"338219",title:"Dr.",name:"Fiaz",middleName:null,surname:"Ahmad",slug:"fiaz-ahmad",fullName:"Fiaz Ahmad",profilePictureURL:"https://mts.intechopen.com/storage/users/338219/images/system/338219.jpg",biography:"Fiaz Ahmad obtained his Ph.D. (2015) from Nanjing Agriculture University China in the field of Agricultural Bioenvironmental and Energy Engineering and Postdoc (2020) from Jiangsu University China in the field of Plant protection Engineering. He got the Higher Education Commission, Pakistan Scholarship for Ph.D. studies, and Post-Doctoral Fellowship from Jiangsu Government, China. During postdoctoral studies, he worked on the application of unmanned aerial vehicle sprayers for agrochemical applications to control pests and weeds. He passed the B.S. and M.S. degrees in agricultural engineering from the University of Agriculture Faisalabad, Pakistan in 2007. From 2007 to 2008, he was a Lecturer in the Department of Agricultural Engineering, Bahauddin Zakariya University, Multan-Pakistan. Since 2009, he has been an Assistant Professor in the Department of Agricultural Engineering, BZ University Multan, Pakistan. He is the author of 33 journal articles. He also supervised 6 master students and is currently supervising 5 master and 2 Ph.D. students. In addition, Dr. Ahmad completed three university-funded projects. His research interests include the design of agricultural machinery, artificial intelligence, and plant protection environment.",institutionString:"Bahauddin Zakariya University",position:null,outsideEditionCount:0,totalCites:0,totalAuthoredChapters:"0",totalChapterViews:"0",totalEditedBooks:"0",institution:{name:"Bahauddin Zakariya University",institutionURL:null,country:{name:"Pakistan"}}}],coeditorOne:{id:"199381",title:"Prof.",name:"Muhammad",middleName:null,surname:"Sultan",slug:"muhammad-sultan",fullName:"Muhammad Sultan",profilePictureURL:"https://mts.intechopen.com/storage/users/199381/images/system/199381.jpeg",biography:"Muhammad Sultan completed his Ph.D. (2015) and Postdoc (2017) from Kyushu University (Japan) in the field of Energy and Environmental Engineering. He was an awardee of MEXT and JASSO fellowships (from the Japanese Government) during Ph.D. and Postdoc studies, respectively. In 2019, he did Postdoc as a Canadian Queen Elizabeth Advanced Scholar at Simon Fraser University (Canada) in the field of Mechatronic Systems Engineering. He received his Master\\'s in Environmental Engineering (2010) and Bachelor in Agricultural Engineering (2008) with distinctions, from the University of Agriculture, Faisalabad. He worked for Kyushu University International Institute for Carbon-Neutral Energy Research (WPI-I2CNER) for two years. Currently, he is working as an Assistant Professor at the Department of Agricultural Engineering, Bahauddin Zakariya University (Pakistan). He has supervised 10+ M.Eng./Ph.D. students so far and 10+ M.Eng./Ph.D. students are currently working under his supervision. He has published more than 70+ journal articles, 70+ conference articles, and a few magazine articles, with the addition of 2 book chapters and 2 edited/co-edited books. Dr. Sultan is serving as a Leading Guest Editor of a special issue in the Sustainability (MDPI) journal (IF 2.58). In addition, he is appointed as a Regional Editor for the Evergreen Journal of Kyushu University. His research is focused on developing energy-efficient heat- and/or water-driven temperature and humidity control systems for agricultural storage, greenhouse, livestock, and poultry applications. His research keywords include HVAC, desiccant air-conditioning, evaporative cooling, adsorption cooling, energy recovery ventilator, adsorption heat pump, Maisotsenko cycle (M-cycle), wastewater, energy recovery ventilators; adsorption desalination; and agricultural, poultry and livestock applications.",institutionString:"Bahauddin Zakariya University",position:null,outsideEditionCount:0,totalCites:0,totalAuthoredChapters:"2",totalChapterViews:"0",totalEditedBooks:"0",institution:{name:"Bahauddin Zakariya University",institutionURL:null,country:{name:"Pakistan"}}},coeditorTwo:null,coeditorThree:null,coeditorFour:null,coeditorFive:null,topics:[{id:"8",title:"Chemistry",slug:"chemistry"}],chapters:null,productType:{id:"1",title:"Edited Volume",chapterContentType:"chapter",authoredCaption:"Edited by"},personalPublishingAssistant:{id:"252211",firstName:"Sara",lastName:"Debeuc",middleName:null,title:"Ms.",imageUrl:"https://mts.intechopen.com/storage/users/252211/images/7239_n.png",email:"sara.d@intechopen.com",biography:"As an Author Service Manager my responsibilities include monitoring and facilitating all publishing activities for authors and editors. From chapter submission and review, to approval and revision, copyediting and design, until final publication, I work closely with authors and editors to ensure a simple and easy publishing process. I maintain constant and effective communication with authors, editors and reviewers, which allows for a level of personal support that enables contributors to fully commit and concentrate on the chapters they are writing, editing, or reviewing. I assist authors in the preparation of their full chapter submissions and track important deadlines and ensure they are met. I help to coordinate internal processes such as linguistic review, and monitor the technical aspects of the process. As an ASM I am also involved in the acquisition of editors. Whether that be identifying an exceptional author and proposing an editorship collaboration, or contacting researchers who would like the opportunity to work with IntechOpen, I establish and help manage author and editor acquisition and contact."}},relatedBooks:[{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:"Theophanides",surname:"Theophile",slug:"theophanides-theophile",fullName:"Theophanides Theophile"}],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:"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:"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:"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:"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:"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:"878",title:"Phytochemicals",subtitle:"A Global Perspective of Their Role in Nutrition and Health",isOpenForSubmission:!1,hash:"ec77671f63975ef2d16192897deb6835",slug:"phytochemicals-a-global-perspective-of-their-role-in-nutrition-and-health",bookSignature:"Venketeshwer Rao",coverURL:"https://cdn.intechopen.com/books/images_new/878.jpg",editedByType:"Edited by",editors:[{id:"82663",title:"Dr.",name:"Venketeshwer",surname:"Rao",slug:"venketeshwer-rao",fullName:"Venketeshwer Rao"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"4816",title:"Face Recognition",subtitle:null,isOpenForSubmission:!1,hash:"146063b5359146b7718ea86bad47c8eb",slug:"face_recognition",bookSignature:"Kresimir Delac and Mislav Grgic",coverURL:"https://cdn.intechopen.com/books/images_new/4816.jpg",editedByType:"Edited by",editors:[{id:"528",title:"Dr.",name:"Kresimir",surname:"Delac",slug:"kresimir-delac",fullName:"Kresimir Delac"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"3621",title:"Silver Nanoparticles",subtitle:null,isOpenForSubmission:!1,hash:null,slug:"silver-nanoparticles",bookSignature:"David Pozo Perez",coverURL:"https://cdn.intechopen.com/books/images_new/3621.jpg",editedByType:"Edited by",editors:[{id:"6667",title:"Dr.",name:"David",surname:"Pozo",slug:"david-pozo",fullName:"David Pozo"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}}]},chapter:{item:{type:"chapter",id:"58427",title:"Residual Stress Analysis of Laser Remanufacturing",doi:"10.5772/intechopen.72749",slug:"residual-stress-analysis-of-laser-remanufacturing",body:'\nRemanufacturing was defined as a process of returning the used product to its original performance. And, it is required that performance specification of the remanufactured product should be equivalent to or even better than that of the new one. Remanufacturing engineering generally refers to the related techniques or engineering activities to remanufacture the waste products, which regards product life cycle theory as instructions and performance upgrading as goals, with rules of energy-saving environment conservation-good quality-high efficiency by using advanced processing techniques [1]. It can bring great economic and social benefits on sources and environment to the world and has become an important way for sustainable society development [1–3].
\nFigure 1 shows the main procedures of used equipment remanufacturing process, which generally involves many steps such as disassembling, cleaning, detecting and assessing of the used components, remanufacturing, examining and reassembling of the remanufactured equipment. It also reveals that remanufacturing is supported by a series of relevant techniques during the whole process.
\nGeneral procedures of mechanism remanufacturing.
The remanufacturing forming procedure is of great importance to the quality of the remanufactured parts, which is also an obvious characteristic to distinguish remanufacturing production from manufacturing. As an advanced remanufacturing technology, laser remanufacturing can restore geometrical size and upgrade performance of the worn components with high productivity and little distortion, using laser cladding, laser welding, laser sintering or other laser-related processing methods [4, 5]. It has shown great benefits to the society for its successful applications over the last decade. More and more institutes, enterprises and industry sectors show great attentions to laser remanufacturing.
\nHowever, there are still some challenges for application of laser remanufacturing, especially residual stress-related problems such as brittle fracture, fatigue failure, stress corrosion cracking and buckling deformation [6]. As a research focus in recent years, residual stress has been experimentally measured by various damage detection methods such as hole drilling and indentation strain, as well as several non-destructive detection methods such as ultrasonic, X-ray diffraction and neutron diffraction methods. However, the experimental data are limited to thoroughly characterize the region distribution of residual stress. Hence, simulation method based on finite element model (FEM) is necessary to estimate the 3D residual stress field of the laser-remanufactured pieces. In this chapter, it introduces some researches on residual stress of laser remanufacturing metal pieces with cases of high-strength aluminium alloy, cast iron and high-strength steel, respectively.
\nSolidification cracking and stress corrosion cracking frequently occur in high-strength aluminium alloys, on the account of their relatively large linear expansion coefficients and high stress corrosion cracking susceptibility [7, 8]. Narrow gap laser welding (NGLW) is considered as one of the most effective ways to repair the cracks, for its lower heat input, less repairing deformation and better repairing quality, comparing with the conventional electric arc or plasma arc welding method [9–12]. However, residual stress of NGLW is also a vital factor for repairing quality of cracks and has been one of the research focuses of NGLW [13–15]. The aim of this work is to present distributions and evolution of residual stress during multipass NGLW processing.
\nThe base material sample in this case was 7A52 aluminium alloy plates with dimensions of 50 × 50 × 20 mm3, and the filler wire was ER 5356 feed wire. A parallel I-type groove was applied, with gap width 3 mm and groove depth 18 mm, as shown in Figure 2a.
\nSketch of the geometric model: (a) narrow gap groove and (b) mesh generation.
The six-pass NGLW was conducted by a 4 kW IPG fibre laser system with welding parameters: laser power 3.20 kW, welding speed 0.48 m/min and wire feed speed 2.15 m/min. A K-type thermocouple was used to detect temperatures during the six-pass NGLW processing, which was located in the heat-affected zone (HAZ) about 5 mm from the groove sidewall and 9 mm from the plate top surface.
\nMSC.Marc 2016.0.0 software was exploited to simulate the six-pass NGLW processing without regard to the molten pool flow and droplet transfer behaviour. One-half of the symmetric geometric model was adopted as shown in Figure 2b. The values of material thermo-physical property such as thermal conductivity thermal expansion coefficient and specific heat were estimated by the thermodynamic software JMatPro as given in Figure 3.
\nThermo-physical values of 7A52 and 5356 aluminium alloys: (a) specific heat and thermal conductivity and (b) elasticity modulus and thermal expansion coefficient.
In order to more accurately describe the combined thermal effects of molten drop and laser irradiation on the base metal, a hybrid heat source model was adopted by combining double-ellipsoid heat source and Gauss cylindrical heat source, as shown in Figure 4.
\nSketch of the hybrid heat source model for NGLW.
The heat flux distribution in front half part (qf) and latter half part (qr) of the double ellipsoid could be, respectively, described as follows:
\nwhere af, ar, b and c are the geometric parameters, vw and t are the welding velocity and time and ff and fr are the distribution coefficient of heat flux determined by af and ar.
\nThe heat flux values (qc) in Gauss cylindrical are characterized by Gaussian distribution in the radial direction and exponential decay along the depth, expressed as follows:
\nwhere R and H are the effective radius and height of Gauss cylindrical, respectively, and β is the energy attenuation coefficient.
\nHere, the adopted values of heat source parameters are given in Table 1 on the basis of previous optimization by experimental observations to molten pool, measurements of joint on its cross section and comparisons between the simulated and experimental results.
\nParameter | \naf | \nar | \nff | \nfr | \nb | \nc | \nR | \nH | \nβ | \n
---|---|---|---|---|---|---|---|---|---|
Values | \n1.5 mm | \n4.0 mm | \n0.55 | \n1.45 | \n1.5 mm | \n2.5 mm | \n0.4 mm | \n4.0 mm | \n0.15 | \n
The values of heat source parameters.
The heat transfer phenomena in NGLW process is governed by the three-dimensional heat conduction equation for unsteady state:
\nwhere ρ, λ and CP are the density, thermal conductivity and specific heat, T is the temperature, Qi is the internal heat source intensity and ∆H is the latent heat of fusion and crystallization. In this case, equivalent specific heat method was used to deal with ∆H, assuming that values of CP had abrupt changes between the solidus and liquidus temperatures.
\nThe symmetrical plane was assumed as adiabatic condition, while on other planes, heat transfer from metal substrate to atmosphere or backing plate occurred by means of thermal convection and radiation, and the thermal boundary condition can be defined as
\nwhere λ is the thermal conductivity, T0 is the atmosphere temperature, hc is the convective heat transfer coefficient, σ is the Stephan-Boltzmann constant and ε is the emissivity.
\nAs for the mechanical boundary conditions, the y-direction displacement of all nodes was fixed on the symmetrical plane to keep the balance of joint, while the nodes on the bottom plane and side edge were fixed in z direction to prevent rotational movement.
\nFigure 5 shows the comparison of calculated and measured temperature curves from the third pass during NGLW processing, which presents good agreement between them. The peak temperature of the calculated curve was 308.2°C, which was close to the measured 301.5°C. And the heating or cooling rates of the measured curve are slightly lower due to thermal inertia of thermocouple.
\nCalculated and measured temperature curves from the third pass.
Figure 6 shows the evolution of calculated transverse stress σy distribution in the middle of the first, third and fifth pass during NGLW processing. There is almost no stress existing in molten pool for melting of metal substrate. However, stress of its vicinity appears as compressive stress as a result of thermal expansion effect, which in turn leads to a tensile transverse stress at its distant zone. By comparing absolute value of the transverse stress and its concentration region in different weld passes, the existence of stress accumulation phenomenon can be confirmed during the multipass NGLW process.
\nDistribution of transverse stress during the (a) first pass, (b) fifth pass, (c) and (d) third pass NGLW.
Figure 7 shows the 3D distributions of the numerically predicted transverse residual stress, longitudinal residual stress, vertical residual stress and von Mises equivalent residual stress in the joint. The concentration region of high residual stress is predominately presented in the weld zone or HAZ near the fusion line, where the latter part has higher values of von Mises equivalent stress than the front part for the gradual accumulations of distortion and stress, as shown in Figure 7d.
\n3D residual stress distributions: (a) transverse residual stress σy, (b) longitudinal residual stress σx, (c) vertical residual stress σz and (d) von Mises equivalent stress.
The residual stress distributions along the weld centre line EF and its vertical line BC, as marked in Figure 1b, are shown in Figure 8. Along the centre line of weld, both the transverse and longitudinal residual stresses show stable tensile stress characteristics with average values of 45.5 and 141.4 MPa, without regard to its unstable front and latter part. During the welding, rapid fusion and solidification appear along welding direction, accompanied by unbalanced expansion and shrinkage behaviours, resulting in higher longitudinal residual stress than the transverse residual stress. Nevertheless, the distribution of residual stress in its vertical direction is more complicated, as presented in Figure 8b. With increase of distance from the weld centre, values of transverse and longitudinal residual stress rapidly decline at the fusion line, and then the longitudinal residual stress decreases gradually until it turns into compressive residual stress from tensile stress, while the transverse residual stress begins to increase and then descends again, maintaining tensile residual stress all through.
\nResidual stress distributions along (a) line EF and (b) line BC marked in Figure 1b.
QT 500 nodular casts iron as an industrial basic material is widely used in ship engines, crankshafts and machine tools [16–18]. As for laser cladding remanufacturing the cast iron pieces, due to the high carbon content, brittle phases are easily generated near the interface between the clad and substrate which causes residual stress during remanufacturing process. Therefore, study on residual stress and its control measures is vital to successful remanufacturing of cast iron components [19–21]. Two common laser pass-forming methods, parallel stacking forming and cross stacking forming, are chosen for the laser cladding process, as shown in Figure 9. A kind of Ni-Cu alloy power with element content of 0.03 wt.%C, 2.0 wt.%Si, 1.1wt.%B, 0.5wt.%Fe and 20.0 wt.%Cu and the balance Ni was selected as the cladding material, whose particle size scale was 20–106 μm.
\nSketch of the laser passes: (a) parallel stacking and (b) cross stacking.
Thermal stress after cast iron laser cladding mainly comes from shrinkage of the clad layers during cooling process. Larger expansion coefficient difference between the substrate and the clad always caused larger residual stress after processing, which is usually a direct reason to the layer cracking. The cast iron parts are often large-scale castings, which can be considered as a fully constrained state around the forming layer.
\nDouble-ellipsoid heat source model and Gauss body heat source model are often used to simulate the welding process, but the process of laser cladding is different with welding process; these heat source models are not suitable for simulating the cladding. Coupling of uniform body heat source (the energy density is same in different points of the heat source) and Gauss surface heat source was adopted in this experiment simulation process. The simulation uses ANSYS finite element software. Firstly, the stress evolution process under parallel stacking forming and cross stacking forming passes was simulated. Considering the actual remanufacturing process, the model is under one side constraint or fully constrained state. Figure 10 is the temperature distribution at 2 and 5 s after multilayer laser cladding process, and Figure 11 shows the temperature cycle curve of the fusion zone and the heat-affected zone. It can be seen that the clad layer and the heat-affected zone undergo repeated thermal cycles, which easily results in stress concentration.
\nTemperature distribution at (a) 2 and (b) 5 s.
Temperature distribution of different zones: (a) the fusion zone and (b) the HAZ (c) location of the selected nodes.
Figure 12 shows the nephogram of the longitudinal stress, the deformation and macroscopic stress state in remanufacturing process. Ends of the sample in x direction are restrained. It can be seen that the stress is mainly concentrated around the constraint parts and the layers. Figure 13 is the curve of the longitudinal stress of a node in the layer and a node in the substrate, and the node’s location can be seen in Figure 12. The layer mainly presents the tensile stress state, while the substrate is mainly in the state of compressive stress. For the actual remanufacturing process, the constraints should be avoided or removed as far as possible.
\nNephogram of stress and deformation: (a) longitudinal stress, (b) deformation and (c) macroscopic stress.
Stress curves of the (a) selected node in the layer and (b) selected node in the substrate.
In order to obtain the residual stress distribution in the surface and interior of the clad layer, X-ray diffraction method was used for measuring the accumulation of residual stress in the clad layer. The electrolytic etching method was used to peel clad layer from the top surface to the internal layer, and the thickness of the peeling layer is 60 μm. Residual stress parallel or vertical to the cladding line was tested, respectively, at a certain point, and the schematic diagram of the test is shown in Figure 14.
\nSchematic diagram of residual stress tests.
Figure 15 shows residual stress distribution in different scanning passes in the clad layer. It can be found that residual stress increases slowly from the surface to inside of the layers formed by cross stacking method. The state of stress is tensile stress with the highest value +300 MPa. The residual stress of the clad layer formed by the parallel path is fluctuated from the surface to the interior, and the fitting curve shows a downward trend. The residual stress at the top of the clad layer reaches the highest tensile stress, reaching +380 MPa, and the lowest residual stress is 50 MPa inside the clad layer. It can be seen that the residual stress of the cross path cladding is smaller than that of the parallel path in the range of 340 μm depth from the surface, and beyond this range, residual stress changes in opposite direction. Residual stress distribution vertical to cladding line direction of the cladding is shown in Figure 15b. It can be found that residual stress from the surface to the interior in the clad layer in two kinds of forming methods is increased, but the residual stress in the layers formed in cross path is smaller than that of parallel path at different depths. It can be seen that the cross path forming is beneficial to reduce the thermal stress of the clad layer in the vertical direction.
\nResidual stress in different scanning passes: (a) parallel and (b) vertical to cladding line.
The thermal cycle curve of the cross stacking forming shows irregular and overlapping effect, and the interval between two adjacent temperature peaks is relatively large. Therefore, there is no apparent periodic heat accumulation in the clad layer, and the heat dispersion effect is obvious. Therefore, characteristics of the temperature field with relatively small temperature gradient caused smaller shrinkage difference of the clad layer, and the thermal stress in the clad layer decreased. The thermal cycling curves of parallel path forming show apparent periodic thermal cumulative effect and heat accumulation of the clad layers, then large temperature gradient exists between high-temperature area of molten pool and the ambient clad layer and the shrinkage deformation and the stress of layers increases. Therefore, the cross path forming is beneficial to the thermal stress control of the clad layer.
\nFigure 16 shows residual stress distribution in the clad layer with different laser powers. It shows that the residual stress differs obviously in the parallel direction and vertical direction when the power increased from 800 to 1200 W. The residual stress decreases apparently in the layers parallel to cladding direction when depth increases. The residual stress in the surface reaches 120 MPa. In depth of 60 μm layer, the tensile stress begins to change into the compressive stress, and in depth of 600 μm, the residual stress reached −300 MPa in the clad layer. In contrast, stress decreases slowly when the power is 800 W.
\nResidual stress curves in different laser powers: (a) parallel and (b) vertical to cladding line.
In the vertical direction of the cladding line, with the increase of layer depth, residual tensile stress of clad layer increases from the surface to the interior when the power reaches 1200 W. The curve slope becomes larger, which means that the stress increases persistently with the increase of the depth. At the depth of 600 μm, the residual tensile stress of the clad layer reaches 600 MPa, approaching the tensile strength of the clad layer, and the cracking tendency of the deposited clad layer increases. Therefore, the stress distribution characteristics of the clad layer under 1200 W are poor, and the cracks may exist in the clad layer. This also verifies that the low-power cladding process has good quality control effect on the remanufacturing of cast iron castings.
\nAccording to the analysis of temperature field at the laser power of 800 and 1200 W, with the increase of laser power, the peak temperature of the thermal cycle curve increases significantly, and the temperature gradient of molten pool and gradient around the area increases. Reduction of cooling time causes cooling velocity to increase rapidly and results in the increasing of elastic-plastic deformation of cladding under residual stress. After solidification, the residual stress distribution along different directions is shown in Figure 16.
\nTo sum up, temperature field of laser cladding during cladding process has an important influence on the residual stress of the laser cladding. The results of the simulation and the actual test show that for the remanufacturing process of cast iron, it can be to helpful to reduce the overall residual stress by using cross path method, and lower heat input causes lower residual stress. These two methods result in the homogenization of the expansion and contraction of the layers during cladding process; therefore, the deformation is smaller, and the residual stress is relatively low. Therefore, from the point of view of controlling the residual stress of the clad layer, using low power and cross path method are used to control the residual stress of the clad layer.
\nDuring the complex thermal cycling of laser cladding, the high-strength steel, solid phase transition, such as eutectoid reaction, solid solution reaction, austenite transition and martensitic transition usually take place. Solid-state phase transition, which is accompanied by specific volume change, transition plasticity and some other effects, will affect the stress field and final residual stress distribution.
\nThe occurrence of solid-state phase transition may have a certain impact on laser cladding or other welding processes under certain conditions. In some cases, the effect is even dominant. Since transition-induced plasticity increases the martensitic transition temperature, the martensitic transition has a significant effect on distribution of residual stress [22]. Ohta [23] studied the effect of solid phase transition on the evolution of residual stress and analysed the influence of diffusion phase transition and non-diffusion phase transition on residual stress. Materials with low phase transition point will result in lower residual stresses; the effect of solid-state phase transition on mechanical properties, solid phase transition volume effect and solid-state phase transition plasticity is the main factors affecting the stress evolution [24].
\nFor steel, it is a hotspot to consider the solid-state phase transition effect in the process of laser cladding thermal-machine simulation. However, the actual situation is complex and still has some work to be done [25]. Firstly, the coupling interaction is very complicated since the stress has a great effect on the phase transition temperature and phase transition kinetics, which in turn affects the evolution of stress. Secondly, the tempering effect accompanying the thermal cycling will affect the physical properties and phase transition properties of the material. Then, many work lacks systematic and reliable physical data, especially computer simulation, in which the systematic and reliability of the data is a very important factor. Moreover, the research results are mostly limited to the welding process [25, 26].
\nThe laser cladding is a processing with multi-parameter, complex nonlinearity and strong coupling and has a wide variety of scanning strategies; the scanning strategy is in direct relation to the thermal cycle of the laser cladding process, which has great influence on the stress, strain and microstructure of the remanufacturing part. Based on a few simplification and assumptions, computer simulation can try all kinds of process parameters and provides the temperature and stress data of remanufacturing part at any point and any time for the analysis of stress, microstructure and properties evolution.
\nAustenite is set as the initial phase in the solidification process. As temperature decreases, the martensitic phase transition starts at Ms (the martensite starting temperature) and finishes at Mf (the martensite finishing temperature). The volume fraction of martensite phase (fM) can be shown as [27]
\nwhere fγ0 is the initial austenitic volume percentage and fγ0Φ(T) is the ratio of austenite at a specific temperatures α is the kinetics coefficient of phase change, and can be obtained by experiments.
\nPhase transition plasticity refers to the plastic strain of the material under the external load which is much less than yield strength. It mainly comes from the Greenwood-Johnson mechanism and the Magee mechanism. According to the classic work of Inoue, Leblond and Fisher, considering that during the laser cladding processing the longitudinal residual stress value is close to that of yield strength, the expression of the stress increment should be revised to
\nwhere \n
The expression above is complex, and in practice the related parameters are difficult to obtain. A simplified equation is put forward [25]:
\nwhere k is easily obtained by experiments.
\nIt is assumed that the initial and final austenitic ratio is f’γ0 and 100% when the temperature rises to Ac1 and Ac3, respectively; the percentage of austenite phase increases linearly as temperature rises. Once the temperature is lower than Ms, austenite will partially or totally transform into martensite during the subsequent cooling period. The martensite tempering and formation of interdendritic eutectic phase during solidification are neglected.
\nIn this work, under the condition of single-pass deposition, three kinds of situations are analysed in comparison: the two are phase transition (one considering stress influence) and the other one without phase transition. We obtained the following characteristics by experiments: expansion coefficient of martensite state is about 18.75 × 10−6/°C (room temperature) and rises up to ~19 × 10−6°C (at above 600°C); Ms = 160°C, Ac1 = 600°C and Ac3 = 900°C; the volumetric change strain is 0.0067352; kinetic coefficient of the phase transition during the cooling period is ~0.02347; and parameter of transition plasticity is 1.165 × 10−4. Molten pool convection is simulated indirectly by elevated thermal conductivity coefficient (twice as large as that of room temperature) and the double-ellipsoid heat source. Latent heat (283 J/g) is taken into consideration when melting and solidification take place. The emissivity (ε) is defined to be 0.5, and the convection coefficient (hc) is 30 W/m2 K. Initial temperature is set at 25°C (room temperature). Finally, the deposition process is regarded as quasi-steady process, and the materials are assumed isotropic [25].
\nIn the same piece of substrate, under the same experimental conditions, technological parameters and using different material powders (with phase transition and without phase transition, respectively) as shown in Table 2, the hole drilling method is used for measurement of residual stress of different powders in one position.
\n\n | C | \nCr | \nNi | \nMo | \nMn | \nNb | \nSi | \nB | \nCu | \nFe | \n
---|---|---|---|---|---|---|---|---|---|---|
1 | \n0.13 | \n12.8 | \n4.7 | \n— | \n— | \n— | \n1.0 | \n1.4 | \n— | \nBal | \n
2 | \n0.03 | \n— | \nBal | \n— | \n— | \n— | \n2 | \n1.1 | \n20 | \n0.5 | \n
3 | \n0.03 | \n17.5 | \n14 | \n2.3 | \n2.0 | \n— | \n1.0 | \n— | \n— | \nBal | \n
4 | \n0.1 | \n15 | \n10 | \n— | \n— | \n— | \n1.0 | \n1.0 | \n— | \nBal | \n
5 | \n0.03 | \n13.8 | \n4.5 | \n1.0 | \n0.7 | \n0.35 | \n0.5 | \n— | \n— | \nBal | \n
6 | \n0.12 | \n15.4 | \n4.2 | \n1.4 | \n0.6 | \n— | \n1.4 | \n0.8 | \n— | \nBal | \n
7 | \n0.05 | \n— | \nBal | \n— | \n— | \n— | \n2.7 | \n1.8 | \n— | \n0.4 | \n
The ingredients of the used laser cladding alloying powders.
In this case, FV520B is martensitic precipitation-hardening steel with excellent strength and good welding performance and is used as the substrate. These samples (Figure 17) are clad layers of different materials, and the scanning strategy is arch deposition (as shown in Table 2). The technology parameters of the laser cladding process are as follows: energy power is 1.8 kW; scanning rate is 8 mm/s; width of a single track is 3 mm; and lapping rate is 0.5.
\nThe laser cladding samples of different alloying powders.
The residual stress results are shown in Table 3; it can be seen that:
For samples with solid-state phase transition, the first principal stress values are both low; #5 and #6 samples show compressive stress, and #1 sample is in tension stress state, whose value is at about 12.7% of the yield stress in the room temperature.
For most samples with no solid-state phase transition, the first principal stress of tension stress state and the stress value are high.
For materials with solid-state phase transition, the higher solid-state phase transition temperature means higher residual stress obtained.
\n | Ms (°C) | \nYs (MPa) | \nσ1 (MPa) | \nσ2 (MPa) | \nσe (MPa) | \n
---|---|---|---|---|---|
1 | \n250 | \n1280 | \n163.44 | \n−32.06 | \n181.60 | \n
2 | \nNone | \n— | \n88.86 | \n−8.11 | \n73.26 | \n
3 | \nBelow RT | \n— | \n— | \n— | \n— | \n
4 | \nBelow RT | \n720 | \n430.07 | \n111.04 | \n386.69 | \n
5 | \n158 | \n920 | \n−196.91 | \n−307.47 | \n269.75 | \n
6 | \n190 | \n1150 | \n−67.52 | \n−169.83 | \n148.11 | \n
7 | \nNone | \n530 | \n349.26 | \n187.61 | \n302.75 | \n
The result of the residual stress (RT, room temperature).
Figure 18 shows the residual stress distribution under the condition of single-pass deposition. Firstly, the stress distribution is nearly the same in the area away from the cladding bead. For the case with phase transition considered (Figure 18b), it is obvious that the stresses are lower in the clad bead as well as the adjacent region. Moreover, the interface between the cladding and substrate shows a lower stress level than that of the clad layer and substrate. The maximum tensile stress is observed at about a few millimetres from the surface of the clad layer. Nonetheless, when the phase transition is ignored (Figure 18a), the residual stresses in the cladding bead increase obviously, which are near the yield strength; the maximum tensile stress is found in the interface between the substrate and the clad layer. When phase transition is taken into account, the cases with and without considering the stress effect on phase transition temperature (Figure 18c and d, respectively) show a similar residual stress level and distribution [25].
\nStress distribution of single-layer laser clad sample: (a) ignore phase transition (b) considering phase transition, and ignore the stress effect on phase transition temperature (c) considering both phase transition and stress effect on phase transition temperature.
Figure 19 shows longitudinal residual stress evolution (z direction, along the laser travel) of the midpoint in a clad layer. The simulation results are in contrast to the results obtained by experimental determination. When the phase transition is ignored, the residual longitudinal stress is close to the yield strength (around 1200 MPa). When considering the phase transition, as the temperature decreases, the maximum longitudinal stress is around 600 MPa and finally stabilized at around 200 MPa. When the stress influence on phase transition temperature is considered, the residual longitudinal stress is closer to the experimental results (394 MPa) than the other two cases. Generally speaking, phase transition has an obvious effect on the residual stresses, making it a more accurate simulation result.
\nLongitudinal stress of a single-pass laser clad from calculation and experiment determination.
Laser remanufacturing is an advanced repairing method to restore the damaged parts based on laser processing, such as laser cladding and laser welding. To avoid obvious distortion and severe residual stress concentration, it is necessary to carry out residual stress analysis by numerical simulation and experimental methods. For high-strength aluminium alloy parts remanufactured by multipass NGLW process, welding passes have obvious effects on the distribution of residual stress, and its accumulation phenomenon would be exacerbated with the increase of welding passes. From the point of view of controlling the residual stress, low laser power and cross path forming strategies were suggested for their important influences on the residual stress in the laser clad layer of nodular cast iron pieces. For high-strength steel with solid-state phase transition remanufactured by laser cladding, the phase transition from austenite to martensite during the cooling process had a positive influence to reduce the magnitude of residual stresses, and a lower residual stress can be obtained using alloying powder materials with lower solid-state phase transition temperature.
\nThe work was supported by the key programme of the National Key Research and Development of China (Grant No. 2016YFB1100205), NSFC programme (Grant No.51705532) and Beijing Science and Technology projects (Grant No. Z161100004916009, Z161100001516007).
\nIn the last years, the problem with crude oil depletion has arisen. Intensive research has been carried out to find out alternative to fossil fuels. Alternative fuels are derived from resources different from petroleum. When used in internal combustion engines (ICE), these fuels generate lower air pollutants compared to petrol fuel, and a majority of them are more economically beneficial compared to fossils fuels. They are also renewable. The most common fuels that are used as alternative fuels are natural gas, propane, methanol, ethanol, and hydrogen. Regarding engine operating with blended fuels, a lot of papers have been written about these blended fuels; but a small number of works have compared some of these fuels together in the same engine [1, 2, 3, 4]. Low contents of ethanol or methanol have been added to gasoline since at least the 1970s, when there was a reduction in oil supplies and scientists began searching for alternative energy carriers in order to replace petrol fuels. In the beginning, ethanol and methanol were thought to be the most attractive alcohols to be added to gasoline. Ethanol and methanol can be manufactured from natural products or waste materials, whereas gasoline fuel which is a nonrenewable energy resource cannot be manufactured [5, 6]. An important feature is that methanol and ethanol can be used without requiring any significant changes in the structure of the engine. Being part of the various alcohols, ethanol and methanol are known as the most suitable fuels for spark-ignition (SI) engines.
The use of blended fuels is crucial since many of these blends can be used in engines with the aim to improve its performance, efficiency, and emissions. The oxygenates are one of the most important fuel additives to improve fuel efficiency (organic oxygen-containing compounds). A few oxygenates have been used as fuel additives, such as ethanol, methanol, methyl tertiary butyl alcohol, and tertiary butyl ether [7]. The process of using oxygenates makes more oxygen available in the combustion process and has a great potential to reduce SI engine exhaust emissions.
Regarding the combustion process, the flash point and autoignition temperature of methanol and ethanol are higher than pure gasoline, which makes it safer for storage and transportation. The latent heat of ethanol of evaporation is three to five times higher than pure gasoline; this leads to increase the volumetric efficiency because temperature of the intake manifold is lower. The heating value of ethanol is lower than gasoline. Consequently, 1.6 times more alcohol fuel is needed to achieve the exact same energy output. The stoichiometric air-fuel ratio of ethanol is around two-third of the pure gasoline; therefore, for complete combustion, the needed amount of air is lesser for ethanol [8]. Ethanol has several advantages compared to gasoline, e.g., lowering of unburned HC emissions, CO, and much better antiknock characteristics [9]. Ethanol and methanol have a lot higher octane number compared to pure gasoline fuel [10]. This enables higher compression ratios of engines and, as a result, increases its thermal efficiency [11]. The production of methanol can be from natural gas at no great cost and is easy to blend with gasoline fuel. These properties of methanol make it as an attractive additive. Methanol is aggressive to some materials, like plastic components and some of the metals in the fuel system. When using methanol it is necessary that precautions had to be taken when handling it [12].
There are many publications with different blends of alcohols and gasoline fuel. For example, Palmer [13] examined the influence of blends of ethanol and gasoline in spark-ignition engine. The obtained results pointed out that ethanol addition (10%) leads to 5% increase in the engine power and 5% octane number increase for each 10% ethanol added. The result showed that 10% of ethanol addition to gasoline fuel lead to reduction the emissions of CO up to 30%. In another study, Bata et al. [9] examined different blends of ethanol and gasoline and discovered that ethanol reduced the UHC and CO emissions. The lowered CO emissions are caused by the oxygenated characteristic and wide flammability of ethanol. Other researchers [14] studied that the potentialities for ethanol production are equivalent to about 32% of the total gasoline consumption worldwide, when used in 85% ethanol in gasoline for a passenger vehicle. In another study, Shenghua et al. [15] examined a gasoline engine with various percentages of methanol blends (from 10 to 30%) in gasoline. The results showed that engine torque and power decreased, whereas the brake thermal efficiency improved with the increase of methanol percentage in the fuel blend. Other authors [16] have studied the influence of methanol-gasoline blends on the gasoline engine performance. The results showed that the highest brake mean effective pressure (BMEP) was obtained from 5% methanol-gasoline blend. In another study, Altun et al. [17] researched the influence of 5 and 10% methanol and ethanol blending in gasoline fuel on engine performance and emissions. The best result in emissions showed blended fuels. The HC emissions of E10 and M10 are reduced by 13 and 15% and the CO emissions by 10.6 and 9.8%, respectively. An increased CO2 emission for E10 and M10 was observed. The methanol and ethanol addition to gasoline showed an increase in the brake-specific fuel consumption (BSFC) and a decrease in break thermal efficiency compared to gasoline.
It can be seen in the literature survey that the exhaust emissions for ethanol-gasoline and methanol-gasoline blends are lower than that of pure gasoline fuel [9, 13, 14, 17]. The engine performance and exhaust emissions with ethanol-gasoline blends resemble those with methanol-gasoline blends.
From the reviewed literature, a conclusion was made that the exhaust emission and engine performance of various blends of methanol and ethanol in gasoline engines have not been investigated sufficiently. Therefore, the objective of this work is to investigate the effects of methanol-gasoline and ethanol-gasoline fuel blends on the performance and exhaust emissions of a gasoline engine under various engine speeds, comparing them with those of pure gasoline.
The simulation tools are the most used in recent years owing to the continuous increase in computational power. The use of engine simulations enables optimization of engine combustion, geometry, and operating characteristics toward improving specific fuel consumption and exhaust emissions and reducing engine development time and costs. Consequently, it can be expected that the use of engine simulations during engine construction will continue to increase. Engine modeling is a fruitful research area, and therefore many laboratories have their own engine thermodynamic models with varying degrees of complexity, scope, and ease to use [18].
Computer simulation is becoming an important tool for time and cost efficiency in engine’s development. The simulation results are challenging to be obtained experimentally. Using computational fluid dynamics (CFD) has allowed researchers to understand the flow behavior and quantify important flow parameters such as mass flow rates or pressure drops, under the condition that the CFD tools have been properly validated against experimental results. Many processes in the engine are three-dimensional; however, it requires greater knowledge and large computational time. Thus, simplified one-dimensional simulation is occasionally used. Hence, simulating the complex components by means of a three-dimensional code and modeling the rest of the system with a one-dimensional code are the right choice to save computational time, i.e., the ducts. This way, a coupling methodology between the one-dimensional and the three-dimensional codes in the respective interfaces is necessary and has become the aim of numerous authors [19, 20, 21].
The aim of the present chapter is to develop the one-dimensional model of four-stroke port fuel injection (PFI) gasoline engine for predicting the effect of methanol-gasoline (M0–M50) and ethanol-gasoline (E0–E50) addition to gasoline on the exhaust emissions and performance of gasoline engine. For this, simulation of gasoline SI engine (calibrated) as the basic operating condition and the laminar burning velocity cor relations of methanol-gasoline and ethanol-gasoline blends for calculating the changed combustion duration was used. The engine power, specific fuel consumption, and exhaust emissions were compared and discussed [22, 23].
The one-dimensional SI engine model is created by using the AVL BOOST software and has been employed to examine the performance and emissions working on gasoline, ethanol-gasoline, and methanol-gasoline blends.
In Figure 1, PFIE symbolizes the engine, while C1–C4 is the number of cylinders of the SI engine. The cylinders are the main element in this model, because they have many very important parameters to settle: the internal geometry, bore, stroke, connecting rod, length and compression ratio, as well as the piston pin offset and the mean crankcase pressure. The measuring points are marked with MP1–MP18. PL1–PL4 symbolizes the plenum. System boundary stands for SB1 and SB2. CL1 represents the cleaner. R1–R10 stands for flow restrictions. CAT1 symbolizes catalyst and fuel injectors—I1–I4. The flow pipes are numbered 1–34.
Schematic of the gasoline PFI engine model.
The calibrated gasoline engine model was described by Iliev [23], and its layout is shown in Figure 1 with engine specification shown in Table 1.
Engine parameters | Value |
---|---|
Bore | 86 (mm) |
Stroke | 86 (mm) |
Compression ratio | 10.5 |
Connection rod length | 143.5 (mm) |
Number of cylinder | 4 |
Piston pin offset | 0 (mm) |
Displacement | 2000 (cc) |
Intake valve open | 20 BTDC (deg) |
Intake valve close | 70 ABDC (deg) |
Exhaust valve open | 50 BBDC (deg) |
Exhaust valve close | 30 ATDC (deg) |
Piston surface area | 5809 (mm2) |
Cylinder surface area | 7550 (mm2) |
Number of stroke | 4 |
Engine specification.
Table 2 presents a comparison between the properties of gasoline, ethanol, and methanol. As shown in Table 2, compared with gasoline and ethanol, methanol has a higher elemental oxygen content and a lower heating value, molecular weight, elemental carbon, hydrogen content, and stoichiometric air/fuel ratio (AFR).
Properties | Gasoline | Methanol | Ethanol |
---|---|---|---|
Chemical formula | C8H15 | CH3OH | C2H5OH |
Molecular weight | 111.21 | 32.04 | 46.07 |
Oxygen content (wt%) | — | 49.93 | 34.73 |
Carbon content (wt%) | 86.3 | 37.5 | 52.2 |
Hydrogen content (wt%) | 24.8 | 12.5 | 13.1 |
Stoichiometric AFR | 14.5 | 6.43 | 8.94 |
Lower heating value (MJ/kg) | 44.3 | 20 | 27 |
Heat of evaporation (kJ/kg) | 305 | 1.178 | 840 |
Research octane number | 96.5 | 112 | 111 |
Motor octane number | 87.2 | 91 | 92 |
Vapor pressure (psi at 37.7 OC) | 4.5 | 4.6 | 2 |
Destiny (g/cm3) | 0.737 | 0.792 | 0.785 |
Normal boiling point (OC) | 38–204 | 64 | 78 |
Autoignition temperature (OC) | 246–280 | 470 | 365 |
Comparison of fuel properties.
In this research, two-zone model of Vibe was chosen for the combustion simulation and analysis. The combustion chamber was divided into two regions: unburned gas region and burned gas regions [17]. For the burned charge and unburned charge, the first law of thermodynamics is applied:
where
Moreover, the sum of the zone volumes must be equal to the cylinder volume, and the sum of the volume changes must be equal to the cylinder volume change:
The amount of burned mixture at each time setup is obtained from the Vibe function. For all other terms, for instance, wall heat losses, etc., models similar to the single-zone models with an appropriate distribution on the two zones are used [24].
In AVL BOOST, the model of formation on NOx is based on AVL List Gmbh [24], which incorporates the Zeldovich mechanism [25]. The rate of NOx production was obtained using Eq. (5):
where
In the above equation,
The NOx formation model in AVL Boost is based on Onorati et al. [26]:
In Eq. (6),
The unburned HC has different sources. A complete description of HC formation still cannot be given, and the achievement of a reliable model within a thermodynamic approach is definitely prevented by the fundamental assumptions and the requirement of reduced computational times. Still, a phenomenological model which accounts for the main formation mechanisms and is able to capture the HC trends as function of the engine operating parameter may be proposed. The following important sources of unburned HC can be identified in SI engines [21]:
During the intake and compression stroke, fuel vapor is absorbed into the oil layer and deposits on the cylinder walls. The following desorption occurs when the cylinder pressure decreases during the expansion stroke and complete combustion cannot take place anymore.
A fraction of the charge enters the crevice volumes and is not burned since the flame quenches at the entrance.
Occasional complete misfire or partial burning takes place when combustion quality is poor.
Quench layers on the combustion chamber wall which are left as the flame extinguishes prior to reaching the walls.
The flow of fuel vapor into the exhaust system during valve overlap in gasoline engines.
The first two mechanisms and in particular the crevice formation are considered to be the most important and need to be accounted for in a thermodynamic model. Partial burn and quench layer effect cannot be physically described in a quasi-dimensional approach, but may be included by adopting tunable semiempirical correlations.
The formation of unburned HC in the crevices is described by assuming that the pressure in the cylinder and in the crevices is the same and that the temperature of the mass in the crevice volumes is equal to the piston temperature.
The mass in the crevices at any time is described by Eq. (7):
In Eq. (7),
The second important source of HC is the presence of lubricating oil in the fuel or on the walls of the combustion chamber. During the compression stroke, the fuel vapor pressure increases so, by Henry’s law, absorption occurs even if the oil was saturated during the intake. During combustion the concentration of fuel vapor in the burned gases goes to zero so the absorbed fuel vapor will desorb from the liquid oil into the burned gases. Fuel solubility is a positive function of the molecular weight, so the oil layer contributed to HC emissions depending on the different solubility of individual hydrocarbons in the lubricating oil.
The assumptions made in the development of the HC absorption/desorption are the following:
Fuel is constituted by a single hydrocarbon species, completely vaporized in the fresh mixture.
The oil film temperature is at the same as the cylinder wall.
Traverse flow across the oil film is negligible.
Oil is represented by squalane (C30H62), whose characteristics resemble those of the SAE5W20 lubricant.
Diffusion of the fuel in the oil film is the limiting factor, for the diffusion constant in the liquid phase which is 104 times smaller than the corresponding value in the gas phase.
The radial distribution of the fuel mass fraction in the oil film can be determined by solving the diffusion Eq. (8):
In Eq. (8),
The present research focused on the performance and emission characteristics of the methanol and ethanol-gasoline blends. Various concentrations of the blends 0% methanol (ethanol) M0 (E0), 5% methanol (ethanol) M5 (E5), 10% methanol (ethanol) M10 (E10), 20% methanol (ethanol) M20 (E20), 30% methanol (ethanol) M30 (E30), 50% methanol (ethanol) M50 (E50), and 85% methanol (ethanol) M85 (E85) by volume were analyzed.
The results of the brake power and specific fuel consumption for ethanol-gasoline blended fuels at different engine speeds are shown on Figures 2 and 3.
Influence of ethanol-gasoline blended fuels on brake power.
Influence of ethanol-gasoline blended fuels on brake-specific fuel consumption.
The brake power is one of the important factors that determine the performance of an engine. The variation of brake power with speed was obtained at full load conditions for E5, E10, E20, E30, E50, and pure gasoline E0. The ethanol content in the blended fuel increased, and the brake power decreased for all engine speeds. The gasoline brake power was higher than E5–E50 for all engine speeds. The ethanol’s heat of evaporation is higher in comparison to gasoline fuel, providing air-fuel charge cooling and increasing the density of the charge. The blended fuel causes the equivalence ratio of blend approaches to stoichiometric condition which can lead to a better combustion. However, the ethanol heating value is lower compared to gasoline, and it can neutralize the previous positive effects. Consequently, a lower power output is obtained.
Figure 3 shows the changes of the BSFC for ethanol-gasoline blends under various engine speeds. The figure shows that the BSFC increased as the ethanol percentage increased. Heating value and stoichiometric air-fuel ratio are the smallest for these two fuels, which means that for specific air-fuel equivalence ratio, more fuel is needed. The highest specific fuel consumption is obtained at E50 ethanol-gasoline blend.
Moreover, there is a slight difference between the BSFC when using pure gasoline and when using blends (E5, E10, and E20). The lower energy content of blended fuels causes some increment in BSFC of the engine.
Figure 4 shows the influence of methanol-gasoline blended fuels on engine brake power. The variation of brake power with speed was obtained at full load conditions for M5, M10, M20, M30, M50, and pure gasoline M0. When the methanol content in the blended fuel was increased (M10, M20, and M30), there was not a significant increase in engine brake power.
Influence of methanol-gasoline blended fuels on brake power.
The engine brake power may be due to the increase of the indicated mean effective pressure for higher methanol content blends. The methanol’s heat of evaporation is higher compared to that of gasoline, thus providing air-fuel charge cooling and increasing the density of the charge. Therefore, a higher power output is obtained. The engine brake power was higher in operation with gasoline in comparison to M50 for all engine speeds.
Figure 5 shows the variations of the BSFC for methanol-gasoline blended fuels under various engine speeds. As shown in this figure, the BSFC increased as the methanol percentage increased. This can be described with heating value, and stoichiometric air-fuel ratio is the smallest for these two fuels, which means that for specific air-fuel equivalence ratio, more fuel is needed. The specific fuel consumption of M50 methanol-gasoline blend was highest compared to those of gasoline for all engine speeds.
Influence of methanol-gasoline blended fuels on engine brake power.
Furthermore, there is a small difference between the BSFC when using gasoline and when using methanol-gasoline blended fuels (M5–M30). As engine speed increased reaching 2000 rpm, the BSFC decreased reaching its minimum value.
The results of the brake power and specific fuel consumption for ethanol- and methanol-gasoline blended fuels at different engine speeds are presented in Figures 6 and 7.
Effect of blended fuels on engine brake power.
Influence of blended fuels on engine fuel consumption.
When there was an increase in the ethanol content in the blended fuel, the brake power decreased for all engine speeds. The brake power of gasoline fuel was higher than those of E5–E50. The heating value of ethanol is lower than pure gasoline fuel, and the heating value of the blends decreases with the increase of the ethanol percentage. Consequently, a lower power output is obtained [22, 23].
By increasing the percentage of methanol in the blends (M5 and M10), the brake power slightly increased, which can be explained by better combustion efficiency of oxygenated fuels. By increasing the methanol content in the blends (M30 and M50), the engine brake power decreased for all engine speeds. The blended fuel heating value decreases with the increase of the percentage of methanol. This results in a lower power output. The gasoline brake power was higher compared to blend M50.
Figure 7 shows the changes of the BSFC for blended fuels under different engine speeds. The BSFC increased as the ethanol and methanol percentage increased. The reason has been known—the heating value and stoichiometric air-fuel ratio are the smallest for this fuel, which means that more fuel is needed for specific air-fuel equivalence ratio. The highest specific fuel consumption is obtained at E50 (M50) blended fuel.
What is more, there is small difference between the BSFC when using pure gasoline and blended fuels (E5 (M5), E10 (M10), and E20 (M20)). The lower energy content of ethanol blended fuels makes some increment in BSFC.
The result of the ethanol-blended fuels on CO emissions is shown in Figure 8.
Influence of ethanol-gasoline blended fuels on CO emissions.
A conclusion, which can be made by Figure 8, is that when ethanol content increases, the CO emission decreases. The reason for this could be explained with the enrichment of oxygen owing to the ethanol, in which an increase in the proportion of oxygen will promote the further oxidation of CO during the engine exhaust process. One of the other significant reasons for this reduction is that ethanol (C2H5OH) has less carbon than gasoline (C8H18).
The result of the ethanol gasoline blends on HC emissions is shown in Figure 9. The figure shows that when ethanol percentage increases, the HC concentration decreases. The HC emission decreases with the increase of the relative air-fuel ratio. The decrease of HC can be explained similarly to that of CO concentration described above.
Influence of ethanol-gasoline blended fuels on HC emissions.
The effect of the ethanol gasoline blends on NOx emissions for various engine speeds is shown in Figure 10. When the combustion process is closer to stoichiometric, flame temperature increases. As a result, the NOx emissions are increased.
Influence of ethanol-gasoline blended fuels on NOx emissions.
The effect of the methanol-gasoline blends on CO emissions for various engine speeds can be seen in Figure 11. When methanol percentage increases, the CO concentration decreases. This can be explained with the enrichment of oxygen because of the methanol and less carbon of methanol than gasoline.
Influence of methanol-gasoline blended fuels on CO emissions.
The effect of the methanol-gasoline blends on HC emissions is visible in Figure 12. When methanol percentage increases, the HC concentration decreases. The concentration of HC emissions decreases with the increase of the relative air-fuel ratio. The reason for the decrease of HC concentration resembles that of ethanol.
Influence of methanol-gasoline blended fuels on HC emissions.
The effect of the methanol-gasoline blends on NOx emissions can be seen in Figure 13. When methanol percentage increases, the NOx concentration increases. When combustion process is closer to stoichiometric, flame temperature increases and the NOx emissions increase as well.
Influence of methanol-gasoline blended fuels on NOx emissions.
The effect of the ethanol- and methanol-gasoline blends on CO emissions can be viewed in Figure 14. By increasing the methanol and ethanol content in the blended fuel, the CO emission decreases. The reason can be the enrichment of oxygen because of the ethanol and methanol, in which an increase in the proportion of oxygen will promote the further oxidation of CO during the engine exhaust process. Another major reason for this reduction is that ethanol (C2H5OH) and methanol (CH3OH) have less carbon than gasoline (C8H18). The lowest CO emissions are obtained with blended fuel containing methanol (M50).
Influence of ethanol- and methanol–gasoline blended fuels on CO emissions.
The effect of the ethanol- and methanol-gasoline blends on HC emissions is visible in Figure 15. When there is an increase in the ethanol and methanol percentage, the HC concentration decreases.
Influence of blended fuels on HC and NOx emissions.
When the relative air-fuel ratio increases, the concentration of HC emissions decreases. The reason for the decrease in HC emissions is similar to that of CO described above. The comparison between the decrease in HC emissions and the blended fuels indicates that methanol is more effective than ethanol. The lowest HC emissions are obtained with methanol-blended fuel (M50). When more combustion is complete, it will result in lower HC emissions.
Figure 15 shows the influence of the blended fuels on NOx emissions. It is noticeable that when methanol and ethanol percentage increases up to 30% E30 (M30), the NOx emission increases, after which it decreases with increasing the percentage of the methanol (ethanol).
The reason is that the improved combustion results in increased temperature in combustion chamber. The higher methanol (ethanol) content in the blends lowers the temperature in combustion chamber. The lower temperature is due to:
Latent heat of evaporation of alcohols, which decreases the temperature in combustion chamber during the vaporization.
The more triatomic molecules are produced: the higher the gas heat capacity and the lower the combustion gas temperature will be. However, the low temperature in combustion chamber can also lead to an increment in the unburned combustion product.
The purpose of the present chapter is to demonstrate the influence of ethanol and methanol addition to gasoline on spark-ignition engine performance and emission characteristics. The summarized results from this study are the following:
With the increase of the percentage of ethanol in the blended fuel, the engine brake power decreased for various engine speeds.
With the increase of the percentage of methanol in the blends M5 and M10, the brake power slightly increased, and with the increase of the percentage of methanol in the blends M30 and M50, the brake power decreased.
As the ethanol (methanol) percentage increased, the BSFC increased. The blended fuels show higher BSFC and lower engine brake power than pure gasoline. Furthermore, there is a slight difference between the BSFC in comparison of gasoline and gasoline blended fuels (E5, E10, and E20 and M5, M10, and M20).
When there is an increase in ethanol and methanol percentage, the CO and HC concentration decreases. The lowest CO and HC emissions are obtained with blended fuel containing methanol (M50).
Increasing the percentage of ethanol and methanol leads to a significant increase in NOx emissions.
When there is an increase in the ethanol and methanol percentage up to 30% E30 (M30), there is an increase in the NOx concentration, followed by a decrease, after which it decreases with increasing ethanol (methanol) percentage. The lowest NOx emissions are obtained with gasoline.
The present chapter has been written with the Project No 2018-RU-07’s financial assistance. We are also eternally grateful to AVL-AST, Graz, Austria, for granting the use of AVL BOOST under the university partnership program.
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