Relationship between SAE levels and types of sensors.
\r\n\t
",isbn:"978-1-83969-221-5",printIsbn:"978-1-83969-220-8",pdfIsbn:"978-1-83969-222-2",doi:null,price:0,priceEur:0,priceUsd:0,slug:null,numberOfPages:0,isOpenForSubmission:!0,hash:"ec438b5e4be44dc63870c1ace6a56ed2",bookSignature:"Dr. Marcos Roberto Tovani Palone",publishedDate:null,coverURL:"https://cdn.intechopen.com/books/images_new/10710.jpg",keywords:"Orofacial Cleft, Cleft Lip, Surgery, Cleft Palate, Oral Surgical Procedures, Orthodontics, Dental Treatment, Comprehensive Dental Care, Speech Therapy, Speech-Language Pathology, Pediatric Treatment, Therapy",numberOfDownloads:null,numberOfWosCitations:0,numberOfCrossrefCitations:null,numberOfDimensionsCitations:null,numberOfTotalCitations:null,isAvailableForWebshopOrdering:!0,dateEndFirstStepPublish:"February 3rd 2021",dateEndSecondStepPublish:"March 3rd 2021",dateEndThirdStepPublish:"May 2nd 2021",dateEndFourthStepPublish:"July 21st 2021",dateEndFifthStepPublish:"September 19th 2021",remainingDaysToSecondStep:"3 days",secondStepPassed:!0,currentStepOfPublishingProcess:3,editedByType:null,kuFlag:!1,biosketch:"Dr. Marcos Roberto Tovani Palone received his Ph.D. from Ribeirão Preto Medical School, University of São Paulo, Brazil. He has published more than 70 papers in reputed journals.",coeditorOneBiosketch:null,coeditorTwoBiosketch:null,coeditorThreeBiosketch:null,coeditorFourBiosketch:null,coeditorFiveBiosketch:null,editors:[{id:"221178",title:"Dr.",name:"Marcos Roberto",middleName:null,surname:"Tovani Palone",slug:"marcos-roberto-tovani-palone",fullName:"Marcos Roberto Tovani Palone",profilePictureURL:"https://mts.intechopen.com/storage/users/221178/images/system/221178.jpg",biography:"Marcos Roberto Tovani Palone completed his MSc from the Hospital for Rehabilitation of Craniofacial Anomalies, University of São Paulo, Brazil, and his PhD in Experimental Pathology from Ribeirão Preto Medical School, University of São Paulo, Brazil. He is DDS, and specialist in pediatric dentistry, syndromes and craniofacial anomalies, and health management. His main research interests are pediatric pathology, orofacial clefts, dentistry, general medicine, and public health. He has published more than 70 papers in reputed journals and has been serving as an editorial board member of BMC Public Health, Biomolecules, and Electronic Journal of General Medicine.",institutionString:"University of Sao Paulo",position:null,outsideEditionCount:0,totalCites:0,totalAuthoredChapters:"0",totalChapterViews:"0",totalEditedBooks:"0",institution:{name:"University of Sao Paulo",institutionURL:null,country:{name:"Brazil"}}}],coeditorOne:null,coeditorTwo:null,coeditorThree:null,coeditorFour:null,coeditorFive:null,topics:[{id:"16",title:"Medicine",slug:"medicine"}],chapters:null,productType:{id:"1",title:"Edited Volume",chapterContentType:"chapter",authoredCaption:"Edited by"},personalPublishingAssistant:{id:"345821",firstName:"Darko",lastName:"Hrvojic",middleName:null,title:"Mr.",imageUrl:"//cdnintech.com/web/frontend/www/assets/author.svg",email:"darko@intechopen.com",biography:null}},relatedBooks:[{type:"book",id:"6550",title:"Cohort Studies in Health Sciences",subtitle:null,isOpenForSubmission:!1,hash:"01df5aba4fff1a84b37a2fdafa809660",slug:"cohort-studies-in-health-sciences",bookSignature:"R. 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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"}}]},chapter:{item:{type:"chapter",id:"57258",title:"Sound Waves in Complex (Dusty) Plasmas",doi:"10.5772/intechopen.71203",slug:"sound-waves-in-complex-dusty-plasmas",body:'\nTransport properties of complex dusty plasma have been very actively investigated in the laboratory and by computer simulations, and wave properties have played a dominated role in both system monitoring and optimization. Waves in complex system have a dynamical role for understanding the behaviours of individual particles in different applications, for example, instabilities and wave propagation during flow. The subject of complex liquids or dusty plasmas containing micron-size charged condensed particles has recently been actively investigated in the physics and chemistry of plasmas, ionized gases, and the space environment, environmental sciences, semiconductor plasma processing industries, nuclear energy generation and materials research. Dust in atmosphere and in the entire universe is in different shapes and sizes. Mostly, it is in solid form and also in liquid and gaseous forms. Dust particle coexists with plasma and then forms dusty (complex) plasma. In the plasma when an electron is displaced from their equilibrium position, the electrostatic forces due to ions pull the electron back. Due to small mass of electron, overshoot the ions and oscillation in plasma is started.
\nThere are four states of matter: liquid, solid, gas and plasma. The Irving Langmuir, an American physicist, defines plasma first time as “plasma is a quasi-neutral gas of the charged particles that show collective behaviors.” He received Noble prize in 1927 first time using the term “plasma” [1]. The quasi neutral means “when the number of ions is equal to the number of electrons then gas becomes electrically neutral (ni ≈ ne)”. Collective behaviour means that charged particle collides with each other due to electric field and coulomb potential. Applications of plasma in industries, science and technology and in our daily life are very significant [2, 3].
\nIn 1922, the term plasma is used and defined by an American scientist Irving Langmuir. In 1930, a few isolated researchers, each motivated by some specific practical problems, began the study of what is now called plasma physics. In 1940, Hanes Alfven developed theory of hydrodynamic waves and gives the future prospectus of waves in astrophysical plasma (now called Alfven’s waves). In 1950, research on magnetic fusion energy was started based on plasma physics in the USA, Soviet and Britain. The end decade of 1960 Russian Tokomak configuration empirically developed and created plasma with different parameters. In 1970 and 1980, the performance of tokomak was improved at the end of twentieth century and fusion break-even nearly achieved in tokomak. In the 1980s, many new applications of plasma in different fields of science and technology appeared. In the 1990s, study on dusty plasma was started (when the dust particles are immersed in plasma, they change the properties of plasma, which is known as dusty plasma) [4, 5].
\nPlasma classified on the basis of temperature of electrons, ions and degree of ionization as well as density.
\nTi, Te and Tg are the temperatures of ions, electron and gas respectively, if the plasmas satisfy Te > > Ti > > Tg condition is called cold plasma. The pressure of the cold plasma is low than collisions between electrons, ions and gases molecule that are not frequent. In this plasma, non-thermal symmetry between energies of gas molecules and electrons are not existed. Temperature of electrons is higher than ions and has greater energy than ions. Similarly, ions have greater energy than gas molecules. In this type of plasma, the effect of magnetic field is very weak and also can be ignored; only charge particles are affected by electric field. It is created in the laboratory on positive column of the glow discharge tube. Applications of cold plasma in food processing, sterilization of tooth, hand and self-decontaminating filter. It is used as a convenient descriptor to distinguish one-atmosphere. Examples of cold plasma are fluorescent lamps and neon signs [6].
\nIf the plasma having very low temperature approximately 100 mK to 10 μK, density 2×109cm3, interaction between particles is strong and the thermal energy of the particles is low compared with Coulomb energy between interacting particles known as ultracold plasma. It is created by pulsed lasers, laser-cooled atoms and photo ionization [7].
\nIf the plasmas have very high temperature, the collision between interacting particles is very frequent also thermal equilibrium obtains, if plasma satisfies Te\n ≅ T\n\ni, this condition is known as hot plasma or thermal plasma. It is created in laboratory with high gas pressure in the discharge tube. Hot plasma acquired local thermodynamic equilibrium (LTE). Examples of hot plasma are atmospheric arcs, sparks and flame. In the hot plasma, the thermal equilibrium occurs between the gas molecules and electrons when electron collides with the gas molecules at a high pressure in the discharge tube.
\nIt is defined by a plasma parameter which is known as Coulomb coupling and denoted by \n
When dust particles are immersed in the plasma, then the properties of plasma become complex known as complex (dusty) plasma. The dust particles are charged and much larger than electrons, ions and neutral atoms, and their size varies from millimetre to nanometre. The properties of dust particles are investigated in different fields of research such as plasma physics, plasma chemistry, ionized gases, material research and astrophysics and also in space physics. Dust particle changes different properties of plasma dominate in current carrier, form liquid and crystalline states. Comets, exposed dusty surfaces, planetary rings and zodiacal dust cloud are the examples of dusty plasma [10–12].
\nDust in atmosphere and in the entire universe is in different shapes and sizes. Mostly, it is in solid form and also presents in liquid and gaseous forms. If dust particle coexists with plasma, it forms dusty (complex) plasma. Dust particle acquires charge and is affected by electric and magnetic fields. Temperature on the dust particle is approximately 10 K, and electric potential varies from 1 to 10 V is mostly negatively potential. Charge on dust particle depends on the flow of electrons and ions. It can be grown or inserted in low-temperature plasma at low degree of ionization in the laboratory. It becomes a dominant component of dusty plasma for transports of energy, momentum and mass. In these days, dusty plasma becomes more significant in the field of research of science and technology by investigating its transport properties in the laboratory. Currently, complex plasma becomes more interesting field of research. The major ratio of plasma in the universe is dusty plasma. It is a huge occurrence in magneto and ionosphere of earth, atmosphere of stars, sun, solar wind, galaxies cosmic radiations, planetary rings comet tails and nebulae. Dusty plasma also known as complex plasma has electrons, ions, neutral and dust particles components [12].
\nDust particles in plasma gain the electric charge, which they obtained in the plasma. The charge on dust particle can range from zero to hundreds of thousands of electrons depending on the size and shape of the dust particle. When a dust grain immersed in an ionized medium, soon it gains a charge when its surface is contact with plasma. This is valid for small floating objects like dust particles, also for electrode surfaces and macroscopic objects inserted into the discharge tube. This charge determines by balancing positive ion and the electron current. These currents must be equal in a steady state. Due to high mobility of the electrons as compared to ions, the surface collects negative charges, which attracts positive ions and repel the electrons until an immobile state is reached. Therefore, by equating fluxes between electrons and ions, the charge on particle can be obtained. This charge is responsible for confinement and long lifetimes of particles in plasmas. Due to interactions between these particles, waves, instabilities and other collective phenomena are produced.
\nDust particles are much larger than electrons and ions, and its size varies from 10−3 m to 10−9 m. A typically used particle may have a diameter of 3.50±0.05 μm and a mass ~3×10−11 kg. Such particles are named mono disperse. It has any shape, mostly spherical, and also easily observed in laboratory without any microscope. It is made by conducting or dielectric material [12, 13].
\nDust particles that collect the plasma particles such as electrons and ions on their surface become an interacting particle. In the dusty plasma energy, momentum and mass are transferred through dust particle. Dust particle may be in liquid, solid and gaseous forms.
\nPlasma is widely used in the field of science, technology and in our daily life. Plasma science also affects human life in different fields and ways. It plays a very important role in the sterilizing of medical instruments, laser, developments of fusion energy, intense particle beams, plasma processing, lightening, development fusion energy controlling, high-power energy sources, high-power radiations sources and water purification plant [3]. X-ray and ultraviolet electron beams and radiations, which are emitted by plasma-centered sources, have a diversity of applications in different fields.
\nIn textile industries, plasma used for surface treatment shows a large number of advantages. Different plasma treatments were used to modify the surface of fabrics, such as cold plasma treatments, which modify the large and reliable systems in fabrics. Three effects are found on the textile surfaces during plasma treatments: (1) cleaning effect, (2) increase of micro roughness and (3) produce deliquescent surfaces. The plasma polymerization is a process in which solid polymeric materials are deposited on textile substrate with required properties; currently, this technique is under development, and in this technique only upper surface is modified. This treatment also has optimistic effects on printing and dying of wool and has proved to be successful in shrink-resist. Improvement of surface wetting technique in artificial polymers is done with treatment in oxygen-, air- and NH3 plasma. Hydrophilic treatment used as a dirt-repellent and antistatic finish [Sparavigna, 2008]. Plasma treatment is environmental friendly in which only upper layer of surface is modified without changing its bulk properties. Vacuum plasma is used on a small scale as is used in reel-to-reel machinery at the industrial scale; it plays a vital role to enhance a number of properties, including liquid repellency, the specific surface functionality and improved wet ability.
\nThe surfaces of optics and contact lenses can be cleaned by plasma treatment. In this process, thin layer of the organic impurity is removed almost from all surfaces. Plasmas are used for the manufacturing of semiconductor chips in discharge tube, and the dust particles are essentially formed from the reactive gases used for the creation of plasmas. These dust particles are used for the manufacturing of semiconductor chips and give the significant results for the conduction of electric current. In the aerospace industries, plasma treatment of composite and polymer materials are commonly used. This treatment has advantages in adhesive and cleaning bonding of the dissimilar materials. In the automotive industries, adhesion of the exterior and interior parts is increased by the plasma treatment apparatus and plasma treatment procedures without any heat effect that commonly facing with flames. The Henniker is well known as a great leader in this field because he controlled plasma systems with low cost for almost in every common automotive development mission [2, 3, 11].
\nIn the plasma, when an electron is displaced from their equilibrium position, the electrostatic forces due to ions pull the electron back. Due to small mass of electron, overshoot the ions and oscillation in plasma are started. The plasma oscillation leads to plasma frequency which is denoted by ωpe\n. When the amplitude of oscillation is small, then oscillation forms a sinusoidal waveform. Mathematically, oscillation is represented as:
\nwhere n is the density, \n
The phase velocity is \n
In this chapter, we classified plasma waves with respect to direction of motion of charge particles (electron, ion), which are perpendicular or parallel to the direction of propagation. The longitudinal waves consist of electrostatic (ion and electron) waves and transverse wave consists of electromagnetic (ion and electron) waves, mostly transverse component and sometime longitudinal component also.
\nPlasma oscillation, also known as Irving Langmuir waves, is discovered by American scientists Irving Langmuir and Lewi Tonks in 1920, and the frequency of plasma oscillation is high due to low electron density in the conducting materials such as metal and plasma. It is found in interstellar gas clouds and earth atmosphere. Mathematically, it is written as \n
The upper hybrid oscillation is a resonance phenomenon in plasma which the electric field perpendicular to the magnetic field and waves propagates across the static magnetic field: \n
It is a type of longitudinal waves in magnetized plasma that propagates due to compression mode and its direction along the magnetic field lines. The particle velocity, sound pressure, sound intensity and particle displacement are the important quantities of acoustic waves. It exhibits the interference, diffraction and reflection phenomenon. In general, ion acoustic wave formula is [1, 14], \n
In this section, we introduce the molecular dynamic simulation technique and its theoretical background that was needed in this work. We introduced a system of mathematical model that used and applied in the computer simulations and also explained steps for molecular dynamics simulation codes. In addition, it also explains the simulation parameters and techniques. Yukawa potential was first time proposed by a scientist Hideki Yukawa in 1930. Through this potential, charged particles do interact, which is defined as
\nwhere Q is the charge on dust particle interacts with other charge particle, ε\n0 is the permittivity of free space, r is the distance of interacting particle and λD\n is Debye screening length.
\nThe particle current or momentum current for single atomic (molecule) species in MD unit is given as:
\nwhere vj\n and rj\n are the velocity and position of jth particle. By using the Fourier transformation, Eq. (4) should be written as:
\nThe correlation function of the current vector component is defined as:
\nFor the isotropic fluid under the consideration of symmetry, Eq. (6) can be expressed in terms of longitudinal current correlation and transverse current correlation in the relative direction of k, where k is the wave vector.
\nBy setting \n
\nEq. (7) split into longitudinal current and transverse current by using the Fourier transformation as:
\nInserting the value of πz\n in Eq. (8), we have
\nLongitudinal current correlation function explains the direction of propagates along Z-axis in the positive and negative directions of Z-axis with wave number. This equation also specifies about longitudinal motion of charge particle.
\nUsing the Fourier transformation for Eq. (7), the transverse current correlation is defined as:
\nwhere
\n\nEq. (12) states about the directions of propagation of electromagnetic waves with respect to wave number along the positive and negative X- and Y-axes. The transverse current correlations also describe us about transverse motion of charge particle in complex system or complex (dusty) plasma [15–17].
\nIn this section, we select a system of choice having number of particles N = 500–4000: these existing particles in a cube volume (V) interact with each other by pairwise Yukawa potential given in Eq. (3). In this chapter, the EMD simulation technique has been used to investigate the longitudinal current correlation and transverse current correlation that are given in Eqs. (9) and (10), respectively. The dimensions of simulation box are chosen as Lx, LY, LZ. The periodic boundary condition is used to minimize the surface size effect and applied to the simulation box. In our case, the main calculations are performed for N = 500 particles at κ = 1.4, 2, 3 and 4 with plasma coupling parameters Γ (temperature of the Yukawa system) varies from 1 to 300 and waves numbers k = 0, 1, 2 and 3. The simulation time step is taken as Δt = 0.005/ω\np that allows to compute the important data for sufficient long simulation run. The EMD method is reported for the current correlation of SCCD plasma over reasonable domain of plasma parameters of Debye screening (1 ≤ κ ≤ 4) and Coulomb coupling (1 ≤ Γ ≤ 300). Particle velocity thermostat is used to control the temperature of systems. The reported simulations are performed between 5.0 × 105/ω\np and 2.0 × 105/ω\np time units in the series of data recording of correlation functions [16, 17].
\nTo illustrate the behaviour of correlation function of SCCD plasma using EMDS, the time-dependent normalized longitudinal current correlation (CL(k,t) from Eq. (9) spectra is plotted against simulation time step (Δt), as shown in \nFigures 1\n–\n3\n, for waves number (k = 0, 1, 2 and 3), plasma parameter (Coulomb coupling) Γ = 1, 2, 50 and 100 and for screening parameter (κ = 1.4, 2 and 3), respectively. It is observed from four panels of \nFigure 1\n that the wavelength decreases and amplitude of longitudinal current increases with increasing waves numbers (k), as CL= 0.2875, 0.9220, 2.5143 and 3.7139 for waves numbers k = 0, 1, 2 and 3, respectively, at same values of Γ =1 and κ =1.4. It has been shown that the value of CL(k,t) decreases with increasing of Γ as CL(k,t) = 0.2875 (0.9220), 0.1179 (0.4369), 0.0044 (0.0148) and 0.0020 (0.0148) for Γ =1, 2, 50 and 100, respectively, at k = 0 (1) and for κ =1.4. In case of wave number k = 2(3), the value of CL is 2.5142(3.7139), 1.0661(2.0954), 0.0591(0.1020) and 0.0271(0.0463), respectively. The fluctuation of sound waves in SCCD plasma is observed from \nFigures 1\n–\n3\n that increases with decreasing the plasma temperature, and it is noted that the amplitude, fluctuation and wavelength for simulation time step (Δt) of longitudinal current correlation in SCCD plasma gradually decrease with increasing of κ. We have noted from simulation results that the propagation of sound waves in dusty plasma is frequently propagated at higher value of Γ =50 and 100 and at lower value of κ =1.4 and 2.
\nVariation of CL(k,t) as a function of simulation time step (Δt) of strongly coupled complex plasma at κ =1.4, N = 500, waves number k = 0, 1, 2 and 3 for (a) Γ =1, (b) Γ =2, (c) Γ =50 and (d) Γ =100.
Variation of CL(k,t) as a function of simulation time step (Δt) of strongly coupled complex plasma at κ =2, N = 500, waves number k = 0, 1, 2 and 3 for (a) Γ =1, (b) Γ =2, (c) Γ =50 and (d) Γ =100.
Variation of CL(k,t) as a function of simulation time step (Δt) of strongly coupled complex plasma at κ =3, N = 500, waves number k = 0, 1, 2 and 3 for (a) Γ =1, (b) Γ =2, (c) Γ =50 and (d) Γ =100.
\n\nFigures 4\n–\n6\n demonstrate that the simulation results are obtained for CT(k,t) of SCCD plasma using EMD simulation at k = 0, 1, 2 and 3, Γ =1, 2, 50 and 100 and κ =1.4, 2 and 4, respectively, at N = 500. The presented simulation results of CT(k,t) spectra are compared with increasing and decreasing sequence of Γ, κ and k. It is noted that the fluctuation, wavelength and amplitude of CT(k,t) (transverse or shear waves) in complex (dusty) plasma immediately depend on plasma temperature, Coulomb coupling and system size. We have observed from simulation results of transverse waves in dusty plasma that wavelength of these waves decreases with increasing wave number, plasma parameters, Coulomb coupling and number of particles. The amplitude of CT(k,t) increases with increasing wave number as CT = 0.2655, 0.9679, 2.0914 and 3.8960 for k = 0, 1, 2, and 3, respectively, at κ =1.4 shown in \nFigure 4(a)\n and decreases with increasing Γ as for k = 0(1), CT = 0.2655(0.9679), 0.1395(0.5607), 0.0074(0.0197) and 0.0040(0.0081) and for the case k = 2(3), CT = 2.0914(3.8960), 1.0590(1.8826), 0.0537(0.0929) and 0.0259(0.0366) at Γ =1, 2, 50 and 100, respectively, shown in four panels of \nFigures 4\n at κ =1.4 and N = 500. We have observed from 12 simulated results, which are displayed in \nFigures 4\n–\n6\n, that fluctuations in CT(k,t) increase with decreasing plasma temperature and decrease with increasing κ, N. We have concluded from 24 simulated results that amplitude and wavelength of transverse waves in SCCD plasma are directly proportional to the plasma temperature and inversely proportional to the screening strength. Shear waves are frequently propagated at higher value of Γ.
\nVariation of CT(k,t) as a function of simulation time step (Δt) of strongly coupled complex plasma at κ =1, N = 500, waves number k = 0, 1, 2 and 3 for (a) Γ =1, (b) Γ =2, (c) Γ =50 and (d) Γ =100.
Variation of CT(k,t) as a function of simulation time step (Δt) of strongly coupled complex plasma at κ =2, N = 500, waves number k = 0, 1, 2 and 3 for (a) Γ =1, (b) Γ =2, (c) Γ =50 and (d) Γ =100.
Variation of CT(k,t) as a function of simulation time step (Δt) of strongly coupled complex plasma at κ =4, N = 500, waves number k = 0, 1, 2 and 3 for (a) Γ =1, (b) Γ =2, (c) Γ =50 and (d) Γ =100.
The EMD simulation method is used to investigate the CL(k,t) and CT(k,t) for SCCD plasma over a wide range of plasma parameters κ, Γ, N and k (wave number). The first involvement of presented simulation is that it delivers and understands the propagation of waves in SCCD plasma. In general, the amplitude and frequency of waves with different modes are analysed. The presented simulation specifies that the waves are frequently propagated at intermediate and high value of Γ. These investigations show that the values of frequency and amplitude depend on Γ, κ, N and k. The EMD simulation investigation shows the plasma wave behaviors that are observed at low and intermediate values of Γ and does not show at higher value of Γ and at lower value of κ =1.4. It has been shown that the presented EMD method and earlier EMD techniques have comparable performance over the wide range of plasma points, both yielding reasonable results for correlation parameters. New simulations yield more reliable and excellent data for the CL(k,t) and CT(k,t) for a wider range of κ (= 1, 4) and Γ (= 1, 100), respectively. The existing simulation delivers more reliable data for the existence of waves and dynamical structure in SCCD plasma. These simulation results show that in the absence of structure in dusty plasma, the shear wave does not support. The sound wave frequently propagates at medium and higher values of Γ in SCCD plasma. It is suggested that the presented EMD technique based on the Ewald summation described here can be used to explore other ionic and dipolar materials. It is very interesting to note what other types of interaction potentials support correction parameters of strongly coupled plasmas and how its strengths depend on the range of new interaction potentials.
\nThe authors thank Z. Donkó (Hungarian Academy of Sciences) for providing his thermal conductivity data of Yukawa Liquids for the comparisons of our simulation results and useful discussions. We are grateful to the National High Performance Computing Center of Xian Jiaotong University and National Advanced Computing Center of National Center of Physics (NCP), Pakistan, for allocating computer time to test and run our MD code.
\nNICDPs | non-ideal complex (dusty) plasmas |
EMD | equilibrium molecular dynamics |
Γ | Coulomb coupling |
\nκ\n | Debye screening length |
CT(k,t) | transverse current correlation function |
MD | molecular dynamics |
LTE | local thermodynamic equilibrium |
SCP | strongly coupled plasma |
PBCs | periodic boundary conditions |
CL(k,t) | longitudinal current correlation function |
Evolution has always been a fundamental component of life and technology developments have always represented a fundamental step in human civilization evolution. In fact, it is possible to say that technology speeds up everyday life thanks to the continuous introduction of innovative techniques, new devices and new perspectives. This results in turn in huge modifications in human activities perception, due to the time involved, their safety and in general their degree of difficulty.
Among the various cases of the daily life, one of the most discussed and interesting issue of the last years is related to automated or self-driving vehicles (AVs). The introduction of such vehicles resulted in the fact that nowadays car company cannot think of just projecting the mechanical parts or the basic electronic used in the vehicle, but are also involved in the project of sensors, integration issues and in the development of the related software.
In this way the integration of electronic components has become a necessary part in the vehicle development, in order to obtain an increase in safety and in easiness of the driving experience (e.g. Anti-Breaking Systems, Hydraulic Break Assist, Electronic Stability Control, etc.), as well as a reduction of impacts such as air pollution, noise, fuel consumption. This resulted in a major cooperation between companies coming from different backgrounds and in the creation of partnerships and joint ventures among them.
These effects result in a renewed impulse in the research related to the vehicles, due to the growing mingling among the various sectors of the industry. The common goal is to develop new models or update the old ones to simulate real life situations and predict the future developments of the automotive and, in general, of transportation research field. Considering the field of transportation systems engineering, it is possible to state that right now the main focus is to achieve a deeper understanding of the hardware and software involvement in this new kind of vehicles. This, in turn, allows to obtain better models developments and more reliable scenario assessment. These issues are often accentuated by the impossibility of retrieving commercial standards, due to the fact that, in some cases, they are not yet available, or the vehicles are still prototypes.
New technologies for automated vehicles look very appealing, but, it may easily be anticipated that the time needed to turn the existing stock of traditional vehicles (TVs) into the new ones will last several years during which mixed traffic is expected, requiring ad hoc tools for analysis and design [1].
Moreover, the likely effects on congestion, and more generally traffic management and control, are not necessarily positive. Some of them are enlisted below, together with indications of needs of new models:
increase of max density, since AVs may be shorter on the average,
increase of capacity, since safety distance may be shorter,
decrease of speed dispersion, useful for more effective traffic control,
decrease of the number of vehicles on streets with shared vehicles, since the number of trips per vehicle increases, and a decrease of the number of vehicles parked along the sidewalk might be anticipated, leading to an increase of capacity.
increase of vehicle x km due to empty movements, if shared vehicles spread, due to longer paths and of change of effective origin and destination,
discrete space / time access for AVs vs. continuous access for TVs,
decrease of the number of users per car, if shared vehicles spread, leading to an increase of vehicle (car) demand flows,
change of modal split leading to an increase of car flows with respect to transit (need of new model split models)
increase of generated flows, due to increase of mobility index and reference population, likely including mobility-impaired people (need of new trip generation and distribution models)
On the other hand, some positive social impacts may also be anticipated such as
less pollution and noise (greater effect with electric powered vehicles),
increase of safety,
increase of generated flows, due to increase of mobility index and reference population,
easier integration with medium/long-distance transportation.
The main goals of this paper are:
discussing the most likely positive and negative effects of mixed flow expected in the near future, as briefly outlined above,
providing an analysis on sensors used in the automotive field,
analyzing the main classifying criteria,
reviewing the most effective tools already available for macroscopic analysis of multi vehicle type transportation systems.
A vehicle classification based on available sensors is surely helpful to support specification and calibration of models for transportation systems analysis, thus a sensor critical analysis is carried out in Section 2, supporting the discussion of classification issues in the same Section 2. Section 3 discusses the main tools for the analysis of transportation systems with mixed flow. Section 4 reports some conclusions.
During the last years, electronic systems assumed a growing importance in the development of assisted and automated systems. In particular, the research has been focused around two main topics:
the development of new algorithms and the improvement of the already existing ones, at the end of obtaining more compact, less consuming and, above all, faster implementations, in order to fit the needs of the automotive industry;
the development of “ad hoc” sensors capable of operating in the various environments and having performances suitable to detect, intervene and advice the driver in time, making it possible for the human or autonomous driver to take the best decision due to the particular conditions.
This work starts from the sensors evolution to give an insight of the different techniques and sensors families used in nowadays autonomous vehicles, in order to gain insight on the different issues related to their development and provide ideas useful to better integrate them into the traffic models, obtaining more fitting models and better results.
First of all, it is possible to define four types of sensors and related systems used in nowadays smart vehicles:
Image sensors and processors,
Radar sensors and systems,
Lidar sensors and systems,
Ultrasonic sensors and systems.
Moreover, the data coming in from the different sensors have to be considered as a whole, in order to have a redundant and robust system and to discriminate in the case of contrasting detections from different sensors. This exchange of information between the different systems is done using an internal network, such as a CAN bus, to avoid the instantiation of several sensors of the same type. Ideally, the aim of the data from and to these control units is to describe the status of the car and formulate the actions to be taken at any time. It is interesting to note, that with the new smart vehicles also systems to process and take into account the data coming from other vehicles have to be considered, causing a huge increase in the amount of data to be processed and, thus, an increase in the research for fast computing, stream processing systems and diagnostic protocols [2].
A careful examination of the various techniques used has to be developed, together with evaluation of the differences between the different types of sensors and how and when to use them, in order to achieve both a better understanding of their behavior and develop new models.
The use of image sensors in automotive field goes back to vision guided auto-parking systems which has been around for the past twenty years [3]. Nevertheless, some of the main techniques are still used nowadays; the main topic has shifted from parking problems to active detection during driving. Several studies have been conducted on these applications, e.g. dedicated to pedestrian crossing and signal recognition. In particular in this type of problems the image sensor has to acquire the images from the environment at resolutions and acquisition rates suitable for the detection problem. Together with the sensor also the image processing methods have to be considered. In particular, to enhance the performances of the sensors in terms of processed frame rate per second (fps) hardware accelerators closely coupled to the image sensor are used. The accelerators are mainly dedicated to filtering operations on the incoming images from the sensors, aiming to send to the processing units only the significant data to be taken into account in the detection. Several systems have been developed, like the ones dedicated to edge detection and recognition, segmentation and object recognition. However, the trend is shifting to the use of Artificial Neural Networks capable of calibrating the various detection parameters according to the incoming stimulus and their previous history. Neural Networks hardware accelerators have been developed in the last years to make the systems capable of operating in real-time. While the training of the Neural Networks to determine the optimal parameters is usually done offline, some parameters could also be varied on-the-fly to achieve a resilience of the system to false detections or to improve the driving performances.
The challenge is nowadays to develop faster Neural Networks systems operating together with the image sensors, in order to process higher amount of data while operating with higher resolution frames; on the other hand, the development of new dedicated algorithms could also allow a complexity reduction of the problem which, in turn, could cause lower latencies in the system and better detection performances, both from a correctness and a speed standpoint.
Radar systems are used in automotive to help solve problems like auto-parking, pre-crash sensing and adaptive cruise control, since one of the main characteristics of a radar system is the capability of inferring the velocity of the detected objects in its range. Radar systems used in automotive applications usually operate at 77 GHz frequency. The measurement time could be reduced to 10 ms allowing controlling the scene 100 times per second [4].
Radar systems are used together with other systems and results particularly efficient for detections at distances up to approximately 200 m, with a range resolution of 1 m [4]. These peculiarities make the radar system particularly appealing for mid-range applications in condition of non-optimal visibility, which could cause major problems to systems based on image sensors. However, radar systems have got poor object detection issues in medium ranges (30 m to 60 m) for angles larger than ±π/6; for that reason, they need to be supported by other detection systems, based on different technologies [5]. Moreover, it has to be highlighted that a typical radar system shows difficulties in taking into account targets having different azimuth values and, thus, more sophisticated data processing systems are needed, like tracking ones. These systems could show problems in the case of wrong modeling assumptions for the targets, causing detection problems, like target loss.
Lidar systems are capable of good performances in presence of adverse environmental conditions, such as fog, heavy rain or snow, but also in the case of scenes having low illumination levels.
However, also lidar systems show some drawbacks, due to the fact that in some cases the dynamic range of the lidar system could be exceeded. When it happens two extremely severe types of error occur: the loss of targets and the generation of ghost targets, which worsen the detection performances unacceptably in some cases [5].
Ultrasonic systems are mainly used for parking assist systems, where they are capable of insuring low costs and good performances. Several threshold systems are designed to achieve the correct detection of corners and edges, while other systems take care of the steering angle and wheel speed; all is coordinated by a network, like the CAN bus previously mentioned [6]. Moreover, this kind of systems demonstrated to work well in cooperation with laser parking systems or to substitute them [7].
Finally, it has to be highlighted that great importance has to be put on decision algorithms, which could be usually implemented in software and which could represent the bottleneck in some systems. In what follows we concentrate on the effect related to the sensors, assuming that the performances of the software are the same for all the considered systems in order to do not undermine the comparison results.
The schemes that is internationally used as standard for the classification of “Automated Vehicles” is the “SAE Levels” [8], written by “SAE INTERNATIONAL” (U.S.-based, globally active professional association and standards developing organization for engineering professionals in various industries) (Figure 1).
SAE levels.
As shown in Figure 1, a six levels taxonomy proposed; these six levels are collected in a meta-classification based on two macro categories (Human Driver e Automated Driving System) that identify the technology inside the vehicle and the type of driver.
To understand the differences between each SAE level, guidelines written by AdaptIVe (a project co-founded by European Commission) [9] have been taken into account. In the following a brief explanation for each SAE level is reported:
Level 0: no electronic device and human driver,
Level 1: some electronic devices but the driver is still human,
Level 2: more electronic devices for security, the driver is helped in some maneuvers, but it still has the main control of the vehicle,
Level 3: in a smart road, the vehicle is supervised by human driver that takes control of the vehicle in case of emergency,
Level 4: in a smart road, the vehicle is still supervised by the human driver, but the driver can make other actions,
Level 5: completed automated vehicle.
In current research and practice SAE levels are grouped into 2 classes: 012 and 345 as it can be seen within the Figure 1.
Still, several considerations about the relationships between kinds of sensors available on a vehicle and SAE Levels support a 3-class meta-classification useful to effectively support specification and calibration of models for transportation systems analysis.
Table 1 shows the relationship between SAE Levels and types of sensors, supporting the 2-class meta-classification in Figure 1: Human driving vs. Automated driving. It could be noticed indeed that there is a huge difference between the type of sensors installed in vehicles of each level. Note that the ultrasonic sensors are used only for parking maneuvers and not for drive assistance, thus they will not be further considered within the following.
Human driving | Automated driving | |||||
---|---|---|---|---|---|---|
SAE 0 | SAE 1 | SAE 2 | SAE 3 | SAE 4 | SAE 5 | |
Image sensors and processors | N | N | Y | Y | Y | Y |
Radar sensors and systems | N | Y | Y | Y | Y | Y |
Lidar sensors and systems | N | N | N | Y | Y | Y |
Ultrasonic sensors and systems | N | Y | Y | Y | Y | Y |
Relationship between SAE levels and types of sensors.
From these considerations, it is possible to see why many authors choose to use two macro classes, to group SAE Levels [10]:
Human Driver: as shown in Figure 1, the vehicle certified within SAE Level 0 to Level 2 is in this macro class. Even if they show some technological differences between each level, in this type of vehicles the driving function is still human related. Hence, the currently available models for transport systems analysis [11] can almost straightforwardly be applied to these vehicles;
Automated Driving System: the other levels are defined this class; the types of vehicles in this class are still at a prototypal stage, but it can easily be anticipated that new models need to be studied and developed.
This meta-classification is probably over-simplified: at a closer look, vehicles in the “Automated Driving System” class cannot be conceived as homogeneous, as shown below.
First, looking at the relationship between Automated Driving SAE Levels (3 to 5) and ranges of sensors, as shown in Table 2, it can easily find out that application ranges of sensors for SAE levels 3 and SAE Level 4 and quite different from those for SAE level 5.
SAE 3 | SAE 4 | SAE 5 | |
---|---|---|---|
Radar sensors and systems | 200 [m] | 200 [m] | 200 [m] |
Image sensors and processors | 250 [m] | 250 [m] | 200 [m] |
Lidar sensors and systems | 150 [m] | 150 [m] | 100 [m] |
Relationship between automated driving SAE levels (3 to 5) and ranges of sensors.
In addition to sensors application range, by studying the SAE Levels definitions [10], it’s possible to see that SAE Level 3 and SAE Level 4 vehicles still need a human driver compare to SAE Level 5. The human driver function within this level represents only a safeguard measure in emergency conditions or both SAE Levels: both presents different reaction times for the takeover, but the description used for the possible action that can be made by the driver during the travel are too permissive for the human driver and their reaction time. On the other hand, SAE Level 5 vehicles do not need a human driver at all, not even a steering wheel (as show in some prototype vehicle that have the possibility to hidden it), leaving all the control function to the vehicle. Starting from those considerations about the control of the vehicle, it’s possible to see that “Automated Driving System” class present a non-homogeneous control, but these criteria it’s not the only one.
Another criterion is represented by the “type of ownership”. Different from SAE Levels 0 to SAE Levels 2 that present a huge percentage of private owned vehicle; it’s possible to notice that:
SAE Level 3 and SAE Level 4 can be considered a technological evolution of traditional vehicles aiming at reducing effort of human drivers and will likely be mostly privately owned,
SAE Level 5 vehicles should be considered an evolution of taxi, other vehicles available on demand and public transport system and will likely be mostly not privately owned such as the so-called robotaxi).
Therefore different “user definitions” [11] must be considered in the two cases. Moreover, the new models needed to be studied and developed should be differentiated, at least with respect to parameters.
For all these reasons, a new approach to vehicle meta-classification should be formulated. Assuming that:
the sensors related software and involved algorithms are the same for each vehicle and level,
the sensors have their functional range as described in Table 2,
the sensors functional range as line of vision for the user,
the SAE Level Certification remains as fundamental definition for the type of vehicles,
the SAE Level can be used for an “ownership” definition by the user (Starting for SAE Level 0 to be private to arrive at SAE Level 5 as Shared Vehicle)
The new meta-classification proposed is the following including three classes:
Human Driver: the same definition as the previous 2-class meta-classification,
Advanced Driving System: this class includes the SAE Level 3 and SAE Level 4 vehicles; these vehicles can be modeled as the same type of vehicle for the “user definition”, since a human driver is needed to control the vehicle and the considered sensors have the same application range,
Automated Driver: this class includes the SAE Level 5 vehicles only.
This meta-classification allows the analysis of the following future scenarios:
short-term scenarios with both Human Driver and Advanced Driving System vehicle classes only,
medium-term scenarios as above with low percentage of Automated Driver class vehicles,
long-term scenarios with Advanced Driving Systems and Automated Driver classes only and no Human Driver class.
A change so great may be not technology-driven only, but also requires a carefully analysis of its several impact through well designed enhancements of tools of Traffic and Transportation Theory (TTT) already available to the transportation systems modelers and planners (see the comprehensive book by Cascetta [11]).
The analysis of transportation systems with several types of vehicles require a generalization of existing models and algorithms for travel demand assignment to transportation networks, as described in Cantarella and Di Febbraro [12], Cantarella et al. [13, 14]; the proposed approach can be applied to real size networks. It is briefly reviewed in the following.
Users are partitioned into o-d pairs they are traveling from/to, user categories (with common socio-economic and behavioral features) and types of used vehicle, such as traditional, connected, automated, autonomous, …; fossil fuel vs. electrical powered; privately owned vs. shared; …. Demand flows are assumed constant and known.
Transportation supply is modeled through a flow network, say a graph with a transportation cost and a flow associated to each arc. All costs are assumed measured by a common unit, usually travel time or money, through duly homogenization of different attributes, if the case. A route connecting an Origin Destination pairs is described by a path. (Presented results still hold if more general definitions of routes are used, such as hyperpaths).
In uncongested networks the arc flows depend on the arc costs, through the arc-flow function obtained as described below; its structure is shown in Figure 2.
The arc-flow function for assignment to uncongested networks.
The arc costs (c) may be different among the vehicle types to reflect different performances, and we assume that the arc cost per vehicle type are given by an affine transformation of the arc generic costs.
Then, the route costs (w) for each o-d pair, user category and vehicle type can be obtained from the corresponding arc total costs through an affine transformation from the arc space to the route space defined by the transpose of arc-route incidence matrix.
The utility function for each o-d pair, user category and vehicle type is almost always specified through an affine transformation of costs both in research analysis and in practical applications.
Route choice behavior for users of each o-d pair, user category and vehicle type m can be modeled by applying any discrete choice modeling theory, such the well-established Random Utility Theory. In this case the choice proportion of an alternative is given by the probability that its perceived utility is equal to maximum among all alternatives. When the perceived utility co-variance matrix is non-singular, probabilistic route choice functions are obtained.
Demand conservation flow relation for each o-d pair, user category, vehicle type assures that flows of all connecting routes (h) sum up to demand flow (d).
The arc flows (f) due to each o-d pair, user category and vehicle type can be obtained from the route flows through a linear transformation from the route space to the arc space defined by the arc-route incidence matrix. Having assumed that all arc flows are measured in TVs per time unit, the arc total flows are given by the sum over all o-d pairs, user categories and vehicle types.
Main input data of the arc flow function are arc costs, and demand flows. Vehicle types may be distinguished with respect to:
flow equivalence
mean number of users on board
cost equivalence
specific arc, cost, e.g. monetary cost (and VoT), access cost, …
route utility parameter and route choice function parameters
route choice function
The arc flow function is monotone non-increasing with respect to arc costs under mild assumptions. It can be computed for large scale applications through algorithms derived from network theory, avoiding explicitly path enumeration.
In congested transportation networks arc flows depend on arc costs, and user equilibrium assignment searches for mutually consistent arc flows and costs. Arc generic costs depend on the arc total flows through the arc cost function, which models user driving behavior at macroscopic level.
Equilibrium assignment can effectively be described through fixed-point (FP) models obtained combining the arc-flow function and the arc cost function. These models can be solved for large scale applications through algorithms based on the Method of Successive Averages, which avoid the use of matrix algebra and computation of derivatives. Their structure is shown in Figure 3.
Fixed point models for equilibrium assignment to congested networks.
Existence is guaranteed if both the arc flow function and the arc cost function are continuous (and the network is connected), applying Brouwer theorem. For a monotone decreasing arc flow function, if the arc cost function is monotone strictly increasing uniqueness is guaranteed. Uniqueness of arc flows also guarantees uniqueness of arc costs as well as route flows and costs, and of flows and cost per o-d pair, user category, vehicle type.
The evolution over time of arc flows and costs can effectively be described through day-to-day dynamic assignment models. The specification of these models requires an extension of models for the equilibrium assignment by including sub-models of.
user memory and learning: how users forecast the level of service that they will experience today, from experience and other sources of information, such as informative systems, about previous days;
user habit and inertia to change: how users make a choice today, possibly repeating yesterday choice to avoid the effort needed to take a decision, or reconsidering it according to the forecasted level of service.
The arc cost updating relation, modeling user memory and learning, gives the today forecasted route costs with respect to previous day costs. It extends the arc cost function. In the simplest instance, this relation can be specified by an exponential smoothing (ES) filter, say a convex combination of yesterday route forecasted costs and yesterday actual route costs, given by an affine transformation of the yesterday arc costs.
The arc flow updating relation, modeling user habit and inertia to change, gives the today arc flow with respect to forecasted costs and previous day flows. It extends the above arc flow function. In the simplest instance, this relation too can be specified by an exponential smoothing (ES) filter, say a convex combination of yesterday arc flows due to users who do not reconsider their yesterday choice and today arc flows due to users who reconsider their yesterday choice.
The arc flow updating relation can be combined with the arc cost updating relation to specify Deterministic Process (DP) models for day-to-day dynamic assignment. Their structure is shown in Figure 4.
Dynamic process models for day to day dynamic assignment to congested networks.
The fixed-point states of the DP model specified by the ES filters are equivalent to the equilibrium states as defined by FP model mentioned above). Applying techniques from the theory of discrete-time non-linear dynamic systems, the above DP models can be used to study the local stability of each fixed-point state, say whether it is an attractor. Moreover, a bifurcation analysis can be carried to single out which attractor is reached by the evolution over time when an input data and or a parameter is changed.
The DP model specification can be used as a base to specify time discrete Stochastic Process models, which may provide full statistical characterization.
All the above models can be extended to cope with Within-day Dynamics requiring highly non-linear specification. This kind of models are useful to describe while-trip re-routing due to interaction with information and/or control system as well as queuing phenomena.
At first this paper has reviewed the analysis of the most likely effects of the introduction of new type of vehicles including vehicles with different level of automation, differently powered, privately owned or shared, …. Then, this paper has discussed the main technological and modeling issues for the analysis of transportation systems with mixed flow.
Some issues are worth of further research work, such as:
specification of new models of Traffic Flow Theory to deal with congestion in mixed traffic,
new path choice models for travel demand assignment within the general framework of Transportation System Analysis,
parameter calibration from real data, or laboratory studies (survey, driving simulator, etc.).
The authors want to fulfill their gratitude Dr. Carmine Cappetta for the support and the patience in developing and publishing this chapter in regard to the analysis of the sensors shown within Section 2.
Authors wish to thank University of Salerno for financial support under local grants ORSA187110 and ORSA192198.
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