Matrix elements from the spatial discretization.
\r\n\tThe present book intends to provide to the reader a comprehensive overview of the state of art in empathy studies, embracing the different theoretical points of view and illustrating the advanced research such as the application of new technologies to promote perspective-taking. The critical aspects and the future directions of the study on empathy will also be presented.
",isbn:"978-1-80356-612-2",printIsbn:"978-1-80356-611-5",pdfIsbn:"978-1-80356-613-9",doi:null,price:0,priceEur:0,priceUsd:0,slug:null,numberOfPages:0,isOpenForSubmission:!0,isSalesforceBook:!1,isNomenclature:!1,hash:"4c1042dfe15aa9cea6019524c4cbff38",bookSignature:"Ph.D. Sara Ventura",publishedDate:null,coverURL:"https://cdn.intechopen.com/books/images_new/11443.jpg",keywords:"Theoretical Model, Skill, Perspective Taking, Training Programs, Practical Implications, Advanced Research, Future Directions, Virtual Reality, Augmented Reality, New Trends, Assistive Technology",numberOfDownloads:null,numberOfWosCitations:0,numberOfCrossrefCitations:null,numberOfDimensionsCitations:null,numberOfTotalCitations:null,isAvailableForWebshopOrdering:!0,dateEndFirstStepPublish:"April 1st 2022",dateEndSecondStepPublish:"June 8th 2022",dateEndThirdStepPublish:"August 7th 2022",dateEndFourthStepPublish:"October 26th 2022",dateEndFifthStepPublish:"December 25th 2022",dateConfirmationOfParticipation:null,remainingDaysToSecondStep:"a month",secondStepPassed:!0,areRegistrationsClosed:!1,currentStepOfPublishingProcess:3,editedByType:null,kuFlag:!1,biosketch:"Passionate researcher in the application of new technologies to psychological treatments, neuro-rehabilitation, human behavior, and the evolution of the human-computer interaction. In 2017 Dr. Ventura won a competitive grant (Santiago Grisolia) at the University of Valencia at LABPSITEC group, where she was awarded her Ph.D. degree, supervised by Prof. Rosa Baños at the University of Valencia, and co-directed by Prof. Giuseppe Riva of the Catholic University of Milan.",coeditorOneBiosketch:null,coeditorTwoBiosketch:null,coeditorThreeBiosketch:null,coeditorFourBiosketch:null,coeditorFiveBiosketch:null,editors:[{id:"227763",title:"Ph.D.",name:"Sara",middleName:null,surname:"Ventura",slug:"sara-ventura",fullName:"Sara Ventura",profilePictureURL:"https://mts.intechopen.com/storage/users/227763/images/system/227763.jpg",biography:"Sara Ventura gained a B.Sc in Psychology at the University of Padua (Italy) in 2013 and an M.Sc. in Ergonomic Psychology at the Catholic University of Milan (Italy) in 2015. In 2016, she carried out a postgraduate training at Universidad Nacional Autónoma de Mexico (Mexico) at the Ciberpsychology lab, working on a rehabilitation protocol for people with acquired brain injury through Virtual Reality. In 2020, Sara gained the Ph.D. in Clinical Psychology at University of Valencia (Spain) working with the LabPsitec group and focusing her research on the study of embodiment and empathy with the support of Virtual Reality. Actually, she is working both with Alma Mater Studiorum – University of Bologna (Italy), and the University of Valencia (Spain) on the fields of embodiment, stroke rehabilitation, empathy and patient care. Her research interests mainly focus on the adoption of new technologies, particularly Virtual/Augmented Reality and Artificial Intelligence for the psycho-social wellbeing with clinical and non-clinical populations, the study of human-computer interaction, and the user experience. She is the author of several scientific papers and various presentations at national and international conferences.",institutionString:"University of Valencia",position:null,outsideEditionCount:0,totalCites:0,totalAuthoredChapters:"1",totalChapterViews:"0",totalEditedBooks:"0",institution:{name:"University of Valencia",institutionURL:null,country:{name:"Spain"}}}],coeditorOne:null,coeditorTwo:null,coeditorThree:null,coeditorFour:null,coeditorFive:null,topics:[{id:"21",title:"Psychology",slug:"psychology"}],chapters:null,productType:{id:"1",title:"Edited Volume",chapterContentType:"chapter",authoredCaption:"Edited by"},personalPublishingAssistant:{id:"455410",firstName:"Dajana",lastName:"Jusic",middleName:null,title:"Mrs.",imageUrl:"https://mts.intechopen.com/storage/users/455410/images/20500_n.jpeg",email:"dajana.j@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. 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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:"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"}}]},chapter:{item:{type:"chapter",id:"49912",title:"Computatonal Analysis of System and Design Parameters of Electrodeposition for Marine Applications",doi:"10.5772/62376",slug:"computatonal-analysis-of-system-and-design-parameters-of-electrodeposition-for-marine-applications",body:'Corrosion has been a menace, for decades, to global development, especially in oil and gas assets, mostly in coastal and marine environments. Maintenance is a better option than replacement as this prevents loss of time and cuts cost. In order to improve the quality and minimize the time taken for the process of maintenance with the consideration of the fleeting environmental regulations, new anticorrosion coatings are being developed, giving cognizance to improved service life, better protective performance, and improved mechanical properties in aggressive marine conditions. These coatings have a minimum thickness that gives tolerance for turbulence effect of seawater in order to withstand degradation. As a result of fluid turbulence on asset regions close to sea level, there is excessive deposition of salt content on the coating surface due to saltwater splashes; corrosion is of high level of proneness if pores are present in the coatings. Localized chemical attacks take place, resulting in catastrophic failure of oil transport vessels during service.
Porosity is an important issue to consider in examining the life expectancy and performance of coated structures as it may exist due to dissolution of prime elements from the coating into the environment. For instance, the effect of the dissolution may be the formation of galvanic cell between substrate and electroless nickel coating on steel immersed in an acidic environment; hydrogen evolution takes place, increasing the pore dimension and speeding up localized corrosion of the substrate. Porosity is a critical factor to be considered in the design of coatings as many researchers have implemented and provided various methods to curb its existence.
Firstly, electrodeposition offers a variety of appropriate techniques for the fabrication of tailored metallic and composite coatings, which include powder metallurgy, nitriding, metal spraying, and vacuum deposition. It is, simply, the use of current flow to mitigate the desired cation of a material in a solution for coating of the material to be attained as a uniform, thin film on a conductive metal substrate. It is a successful way of dealing with the issue of porosity in coatings because this technique is compatible with the deposition of a wide scope of materials, such as nickel, zinc, cadmium, aluminum, and their respective composites in severe marine environments. Secondly, electrodeposition offers the flexibility of controlled processing of these corrosion-resistant coatings. However, expertise is highly appreciated in the full knowledge of the manipulation of the system and design parameters employed for optimum deposition characteristics. Computational analysis of the effect of these parameters and their interactions on the deposition quality can be achieved based on various designs of experiments (DoE). These DoE offer the possibility of understanding the cause-and-effect of the parameters on the final product and finding the interactions between variables and running effective experiments in a cost-effective manner. They also provide the opportunity of building mathematical models to support the correlation between product properties and significant processing parameters, establishing a platform for future consideration.
Corrosion occurs fastest in the marine environment as a result of the high concentration of salt content in the atmosphere. The contained salt increases the susceptibility of marine assets to degradation.
In relation to chemical perspective, seawater is a solution of salts dissolved in water. Typically, the salinity of an ocean can be measured at a value between 3% and 4% [1]. The concentration of [Cl−] ions in water determines the degree of salinity [2]. Evaporation of water can probably pose as a determinant of the occurring differences in local salinity, which, in turn, increases the degree of salinity of seawater [3]. When water exchange is restricted in a certain region of the sea, a free water inflow into the sea will lead to a drop in the salinity of seawater [4, 5].
Another factor that affects the salinity of seawater is temperature; as the temperature decreases, concentration of dissolved salt decreases, that is, there is a drop in solubility [6, 7]. This drop in solubility reduces the conductivity of the seawater, increases its electrolyte resistivity [8], and encourages the dissolution of oxygen. With an increase in the oxygen dissolution, corrosion rate becomes greater [2].
Influence of corrosion rates on marine equipment, assets, ships, and oil platforms can be influenced by geographical factors. The combined effect of salinity and temperature can be seen in various oceans, which eventually lead to variation in corrosion rates. Oceans situated at regions close to the North and the South poles are prone to ice formation due to low temperature. Brine is trapped within crystals, thereby depleting the level of salinity. Oceans close to the equator possess higher degree of salinity as this can have a higher influence on the corrosion rate of machinery.
Corrosion is referred to as the degradation of a material’s properties and performance as a result of the material’s reactivity with its environment. Most of the time, the reaction turns out to be inexorable. This enables the rapid consideration of research and development, seeking for better paths of eradicating its effect. Polymers and ceramics undergo degradation in the form of aging and selective dissolution, respectively. Corrosion is majorly associated with metallic structures, especially structures that are in the vicinity of marine regions. Ships, oil platforms, and vessel assets are the major victims of corrosion based on the highly induced attack by seawater. With increasing industrialization, the cost of battling corrosion has consistently been rising as the years go by. Based on the thermodynamic phase for corrosion, it is of little wonder that the attributed cost with corrosion has skyrocketed. Many studies conducted for more than three decades have revealed that the yearly expenditure on corrosion control is close to 3.1% of the Gross National Product (GNP) of the country. This works out to a value above $275 billion annually [9]. In 2001, the United States experienced an uprising in its cost of corrosion control to a shocker of over $600 billion, representing about 5% of the GNP of the country. This could be interpreted in such a way that for every family of four, the net annual expenditure on fighting corrosion is $8,000. In addition, corrosion seems to be the nation’s most significant cause of financial calamity; it costs more than natural disasters and automobile accidents [10]. Figure 1 shows the result of the study on corrosion cost distribution of the United States carried out by the United States Federal Highway Authority in collaboration with the National Association of Corrosion Engineers (NACE) in 2001.
Corrosion Cost Distribution [
Corrosion is defined as the deterioration of a component that occurs due to its interactive reactivity with the environment. When a metal comes in contact with a liquid, an electrochemical reaction is set up involving a transfer of electrons across the metal/liquid boundary. Conservation of energy is observed as metals return to their original state; the energy required for corrosion to occur is the energy responsible for converting the ores to metals. This energy responsible for the conversion is a free energy known as Gibb’s free energy. When an atom leaves the crystal structure of a metal, it exceeds the interatomic forces binding the atoms together in the crystal matrix. The surface atom experiences lower attractive forces compared with the internal atoms, a significant increase in temperature can enable the outer atom to leave the structure, leaving its electrons behind [12]. This is why corrosion is rapid with increased temperature in a corrosive medium. The equation below illustrates the electron transfer of a metallic atom leaving the surface of the metal, an oxidation reaction:
where ‘M’ symbolizes the metal and ‘
As the atom leaves the surface, it immediately appears as an ion in the liquid. With subsequent dissolution of the metal atoms to form ions, the weight of the bulk material reduces, creating a rough metal surface. The metal ion reacts with dissolved oxygen present in the liquid to form the metal oxide. Most times, the corrosive medium appears stagnant, thereby leaving the oxide product on the corroded surface; in the case of iron, the oxide formed is known as ‘rust’. The oxidation and reduction mechanism is shown in Figure 2. Even though it appears that corrosion takes a generic routine, there are different forms of corrosion as a result of the localization, nature of corrosive medium, and chemistry of the metal itself. Corrosion is independent of distinct electrodes, as it may occur at micro scale. Numerous anodic and cathodic sites may appear on a single surface where oxidation and reduction reactions take place [13].
Corrosion Mechanism of Iron to Produce Rust [
Useful information of the history of corrosion of a failed material can be carefully acquired if adequate observation is made. Based on the appearance of the corroded component, there are eight different forms of corrosion.
Uniform or general corrosion is the most common form of corrosion. It is frequently attributed with electrochemical reactions, which prevails in a homogeneous manner across the surface of the component that is exposed to the atmosphere. As corrosion proceeds, the component reduces in dimension as failure becomes imminent. This form of corrosion is known to be the most disastrous form of corrosion, which may lead to a massive deterioration of metal. As its name appears, it affects a wide span region unlike the other forms of corrosion that are localized in nature. Therefore, its effect may be predicted experimentally [15]. It can be controlled by cathodic protection or by applying coatings and paints and also by introducing dimensional allowance (Figure 3).
(a) Schematic Representation of Uniform Corrosion and (b) a Weathering Steel [
The degradation of coating structures for surface protection usually results in uniform corrosion. Characteristics observed on such surfaces are muting of shiny metallic surfaces, discoloration of iron-based materials, and rough impressions by corrosive media. Surface degradation is the result of a negatively altered coating system and consideration should be given to repair; otherwise, it may lead to worse situations of corrosion attacks [16].
Crevice corrosion is a localized form of corrosion attack. It exists with metals and alloys, which have unstable passive films in the presence of chloride and hydrogen ions. It is common with nonferrous metals, such as aluminum, zinc, titanium, and copper, but not with stainless steels. This form of corrosion attack occurs at crevices, joints, riveted regions, flanges, or isolated regions beneath where debris deposits can be found. Sand and dust deposits with scales and products from degradation can act as preferred sites where corrosive electrolyte can be reintroduced with high level of challenge [17]. For crevice corrosion to take place, the opening must be spacious for accommodating and retaining the electrolyte. Though there are no specific dimensions for crevices, this form of corrosion can be present where there are gaps of few micrometers. The presence of high level of chromium can reduce the possibility of crevice corrosion [18]. Also, considering flanges and joints, compromise must be made in terms of the opening because extremely narrow openings may lead to galvanic or cell corrosion, whereas a larger opening will allow accumulation of electrolyte that initiate crevice corrosion (Figure 4).
(a) Crevice Corrosion on a Steel Flange [
This form of corrosion is observed at the microlevel of metals situated at grain boundaries. Rapid attack of neighboring grain boundaries will eventually lead to cracking of the grain structures. Sometimes, elements and phases may be precipitated out of the grains and migrate to the grain boundaries as a result of processes, such as aging; these migrated precipitates can be a mismatch with the grain boundary composition, in this manner, causing a depletion of intergranular bonds. Also, adjacent grains may lose significant elements such as chromium to chromium-rich grain boundary precipitates, leaving those regions deficient of chromium and susceptible to corrosion in a corrosive medium (Figure 5).
Intergranular Corrosion [
Pitting corrosion has been one of the most expensive forms of corrosion because of its unpredicted manner of attack. It is a form of confined degradation which yields effects in the form of pits and holes. This form of corrosion may exist in stainless steels whether in acidic or neutral chlorides. An example of such media is the seawater. Pitting corrosion concentrates on the weakest link, which is a region where passive layer might not be tough enough. The mechanism is very rapid as a damaged surface experiences successive deterioration within a short period of time (Figure 6).
Pitting is dependent on temperature, the chloride concentration, and pH value. A low-corrosive environment will aid a natural repair of a steel grade pit in the presence of oxygen. Natural seawater causes more harm in terms of pitting than any other chloride-contained corrosive media as a result of its biological interaction. This requires the prevention of microorganism growth by applying biocides in seawater piping systems [20].
Pitting Corrosion [
Erosion corrosion is a rapid rate of corrosion action on metals and alloys as a result of relative movement of corrosive fluid to the metal of alloy surface. With rattling flow of the fluid, the inner surface of a pipe or a tube can be washed resulting in erosion and then a leakage. This form of corrosion attack can occur due to bad internal design of component, for instance, a protruding inclusion which was meant to be trimmed can perturb a laminar flow of the fluid. Subsequent washing of a localized region on the inner surface by high-velocity fluid flow can initiate erosion corrosion. Pitting may result from a collaboration of erosion and corrosion. For erosion corrosion to be mitigated, pipe diameters should be increased while turbulent flow will be eliminated. Also, highly affected regions, such as elbows and bends, should be designed with thicker materials [16] (Figure 7).
Erosion Corrosion on Inner Surface of an Elbow Pipe [
The other three forms of corrosion are hydrogen embrittlement, stress corrosion cracking, and galvanic corrosion. Hydrogen embrittlement can be experienced in acidic media where the hydrogen ions act on the surface of the metal in contact; stress corrosion cracking occurs with materials having a combination of stress-induced conditions under corrosive environment; and galvanic corrosion takes place between two dissimilar metals in contact.
It can be seen that necessary ways of protecting metals from the actions of corrosive media, mostly in the marine environment, should be looked into. This will drastically reduce the massive amount of expenditure on corrosion control, especially in industries such as marine, defense, and oil and gas sectors.
There are two major forms of corrosion that dominate the morphology of degradation: galvanic corrosion and electrolytic corrosion.
Surface improvement of engineering components is expedient in order to protect these components from premature failure and effect of corrosion during service [23]. Surface engineering can be described as a broad set of technologies that focuses on the design and modification of surface characteristics of a component. Surface engineering can be seen in two major ways for improved surface characteristics and bulk material, that is, surface coating and surface modification, whereas surface shape design is a secondary type of surface engineering. Surface coating processes entail deposition of layers in the form of molten or semi molten material on the surface of a metal (substrate). Part of the prime reasons for surface coatings is to enhance the surface behavior rather than altering the bulk composition of the material. There are various techniques of applying surface coatings, which include chemical and physical vapor deposition (C & PVD), thermal and plasma spraying, cladding, and electrodeposition [24]. With surface modification, processes like surface hardening and alloying performed either by flame, laser, electron beam, induction, or elevated energy treatments (ion implementation, carburizing, and nitriding) are considered. Surface modification processes are employed to regulate surface tribological properties. Sometimes, there can be a combination of surface coating and surface modification, as in the case of laser cladding [25]. The major challenge of these techniques is the presence of porosity as this coating defect lays a strong platform for localized forms of corrosion, even a general form of corrosion.
Electrodeposition can also be referred to as electroplating since electroplating occurs during the process of electrodeposition. This is a process that makes use of electric current to mitigate cations from a preferred solution and migrate them as a thin-film coat on a conductive plate immersed in an electrolyte. Deposition of coatings can be achieved to obtain an improved surface with an excellent protective coating via electroplating technique [23]. The deposition of the material (the cations receive electrons at the cathode and are reduced to their metal atoms) as coatings on the conductive plate can be influenced by various factors, that is, the nature of cathode, electrolyte (salt bath), electric current, and so on (Figure 8).
Electrodeposition Setup.
Porosity is among the major causes of discontinuities in electrodeposited coatings. The other causes are elevated internal stresses, corrosion, and wear of the plated surface. Porosity may be deleterious as its presence can expose coated surfaces to corrosive media, deplete mechanical properties, and affect density and diffusion properties in a negative manner. Pores that are created based on increase in temperature during service can lower the adhesion ability of a deposited layer. The presence of pores in metal coatings that readily produce thin passive film may be given less consideration compared with noble metals. For instance, if zinc is coated on a steel substrate, the zinc will be sacrificial by cathodically protecting the steel substrate. Furthermore, the presence of pores can allow the degraded products to accumulate on the substrate surface, leading to a localized form of corrosion, such as pitting. A material that contains pores or voids formed during electrodeposition is prone to deterioration of properties. The mechanical feature of a pore-contained coating is identified by a massive reduction in toughness and strength with increasing pore volume as ductility is also decreased.
Electroless plating can be defined as a method of application of protective coatings: a chemical reduction–oxidation reaction that takes place as metallic ions are automatically aided in reduction. This can be achieved in an aqueous solution, which includes a reducing agent while deposition is made without flow of electrons [26]. Deposition of coating and its quality in electroless plating can be influenced by various variables that include the bath concentration, bath volume, acidity or basicity of the bath, bath history, temperature, and bath additives [27]. Electroless plating has better yield and quality with better surface profile compared with conventional electrolytic deposition; this is due to the flexibility of manipulating process parameters to obtain tailored coating dimensions. Figure 9 illustrates this benefit.
Schematic Illustration of Advantage of Electroless Plating [
For optimized system of uniform deposition and elimination of porosity in deposited coatings, the knowledge of the influence of deposition parameters is essential. Current density, time, nature of electrolyte, voltage, salt bath composition, temperature, stirring rate, pH value, and substrate type are the prime parameters that influence the deposition rate. Adequate control of the relationship between these parameters will yield an optimized deposition. Current density is inversely proportional to time while, theoretically, conductivity of the substrate varies inversely as the time [29]. This shows that a mathematical relationship can be achieved for various electrodeposition setup depending on the aforementioned parameters.
The current density is the rate of movement of a certain quantity of electricity per unit volume within the electrolyte. It is observed that the larger the current density, the more nucleation sites are widely distributed across the cathode [30]. Also, the current density favors the formation of larger grain sizes, with a lower strength compared with smaller grain sizes. Therefore, large current density produces more nuclei sites, whereas small current density will support small grain growth; adjusting the current density will tailor the quality and the properties of the coating to a better performance [31].
The time taken for deposition of uniform coating can be affected by other variables, for instance, a larger current density will reduce the time of deposition or an electrolyte with higher molecular mass will slow down the deposition process and increase the deposition time. The stability of an electroless bath strongly influences the time of coating deposition. Higher stability of bath may lead to increase in time of deposition, and thus a steady renewal and replacement will be achieved with respect to unit area [32].
Properties of the substrate, such as the conductivity, surface profile, and chemical composition, can determine the substrate’s affinity for rapid coating deposition [33]. High electrical conductivity will ensure faster movement of electrons to the cathode (substrate) surface where reduction will occur. The more the supply of electrons, the faster the rate of metal deposition.
The bath composition is dependent on the molecular mass of the additives and also their viscosities. Bath age, bath formulation, bath temperature, and stability are integrated in the net influence on coating deposition rate. Usually, a rise in the bath temperature has a way of inducing an increase in metal solubility and stability in the electrolyte, resulting to a surge in transport number and conductivity of the specie, though the viscosity of the electrolyte is reduced [34]. Another bath parameter that influences deposition rate is the stirring rate; a higher stirring rate will lead to increase in bath agitation and decreased time to attain adsorption on the cathode surface, hence deposition rate is increased.
Predictive models, simulations, and theories have been developed over the years to subdue the hardship of material development. Response surface model, artificial neural networks, and Taguchi models are forms of predictive models which have been used to achieve optimal deposition. These models have also eliminated the use of trial and error methods in experiments. The high cost of resources, time, and labor has been taken care of by the advent of DoE. DoEs make experiments easier by linking data compilation with experimental procedures to analysis of results and prediction of optimized output and performance.
Generally, several experiments are often needed to be carried out considering different plating parameters and plating functionality in order to achieve a model. An artificial neural network model was developed for the deposition of copper and then compared with regression model. Validation was performed by conducting another set of experiment with the same conditions and then comparison was made. The validation showed a percentage error, which was less than 1.0 [34].
Studies are ever increasing on the development of new and effective techniques on electrodeposition. Oil platform assets, ship vessels, and marine components will be able to function at optimized performance for longer duration without the cost and labor of repair and maintenance. Coatings that will be able to be highly unresponsive to severe marine environments and adhere strongly to underlying substrate are becoming more achievable irrespective of harsh cascading of saltwater splashes on ship hulls, propeller panels, and rig supports. Consequently, the global cost on corrosion control will diminish to an appreciable extent, though it will still be a serious discussion ever-present in the world of engineering and technology.
The mathematical models representing the nuclear reactor physics are based mainly on two theoretical areas: neutron transport theory and neutron diffusion theory, where it is necessary to remark that neutron diffusion theory is really a simplification of the neutron transport theory.
\nNumerical methods are used to solve the partial differential equations representing the nuclear reactor physics, and these methods are derived from discretization techniques. For numerical solutions in any scientific area, computational tools have been developed including software and hardware. In the past, the former computer processing was the sequential execution of computer commands, meaning to say that program tasks are carried out one after one. Modern computational tools have been developed for parallel processing, executing several tasks concurrently.
\nThe computing branch dealing with the system architecture and appropriate software related to the simultaneous execution of computer instructions and applications is known as parallel computing science. Former developments in parallel computing were made in the late 1950s, following the construction of supercomputers throughout the 1960s and 1970s. Nowadays, clusters are the workhorse of scientific computing and are the dominant architecture in data centers.
\nSince the late 1950s, the performance of safety analyses was essential in the nuclear industry, in research reactors, but mainly safety analyses of nuclear power plants for commercial purposes. Scientific computing calculations were vital to these safety analyses, but with important limitations in computer/computing capabilities. At the beginning, the objective was to give a solution to partial differential equation models based on neutron diffusion or neutron transport with technology and methods available in those years. Numerical techniques were used first with finite differences and finite element approaches, and gradually up to now, with nodal finite element methods (NFEMs). Despite the numerical method employed, the computer code user faces the problem of solving extremely large algebraic systems challenging hardware/software capabilities. Generation of results for any reactor simulation in considerable short times is a desirable achievement for computer code users [1].
\nRecent developments of high-performance computer equipment and software have made the use of supercomputing in many scientific areas possible. The appropriate selection of parallel computing software, like newly developed linear algebra libraries, to be used in a specific project may result in a suitable platform to simulate nuclear reactor states with relatively prompt results.
\nThroughout the world, several research projects in the last decade have been developed with the main objective of making full tridimensional (3D) coupling simulations of nuclear reactor cores, leaving aside the obsolescence of the point kinetics theory. Most of the modern nuclear reactor simulators are based on neutron transport theory, or on neutron diffusion theory, to obtain detailed 3D results. As light water is used for cooling/moderating light water reactors (LWRs), a comprehensive analysis of the reactor core physics must include thermal-hydraulic phenomena, so that modern simulations perform reactor calculations with thermal-hydraulic feedback coupled with neutron kinetics calculations.
\nAll the discussions included in this chapter are centered in a simulator for light water reactors. The computer code AZtlan KInetics in Neutron Diffusion (AZKIND) is part of the neutronic codes selected for their implementation in the AZTLAN Platform1 project in which neutron transport and neutron diffusion codes are being developed in Mexico. A (TH) model has been implemented recently and coupled with the neutronic (NK) model, and both models are based on HPC implementations.
\nAlthough there has been growing interest in the transport-based core neutronics analysis methods for a more accurate calculation with high-performance computers, it is yet impractical to apply them in the real core design activities because their performance is not so practical on ordinary desktop or server computing machines. For this reason, most of the neutronics codes for reactor core calculations are still subject to the two-step calculation procedure, which consists of (1) homogenized group neutron parameters generation and (2) neutron diffusion core calculation.
\nIn the core calculation steps that are the main concern of this work, nodal codes based on the diffusion theory have been used to determine the neutron multiplication factor and the corresponding core neutron flux (or power) distribution. Practically, almost all nuclear reactor simulation codes employ the two-group approach involving only fast and thermal neutron energy groups for the applications to light water reactors (LWR). However, numerical calculations with the two-group structure are not appropriate in the analysis of cores loaded with mixed oxide fuels or analysis of fast breeder reactors, since the neutron spectrum is influenced more by the core environment, requiring much more energy groups than only two groups.
\nAs settled in Ref. [2], even using a high-performance computer, a direct core calculation with several tens of thousands of fuel pins is difficult to perform in its heterogeneous geometry model form, using fine groups of a prepared reactor cross-section library. The Monte Carlo method can handle such a core calculation (see also the Serpent code), but it is not easy to obtain enough accuracy for a local calculation or small reactivity because of accompanying statistical errors, besides the large calculation times. Instead of using neutron transport computer codes, the nuclear design calculation is performed in two steps: (1) lattice calculation in a two-dimensional infinite arrangement of fuel rods or assemblies for the generation of homogenized lattices jointly with their corresponding homogenized cross-sections and (2) core calculation in a three-dimensional whole core, with a neutron diffusion code using the information of the previous step.
\nAs shown in Figure 1 [2], the lattice calculation prepares few-group homogenized cross sections which maintain the energy dependence (neutron spectrum) of nuclear reactions, and these reduce the core calculation cost in terms of time and memory. The final core design parameters are not concerned with continuous energy dependence, but spatial dependence, such as power distribution, is important to avoid high local neutron fluxes or high absorbing materials causing significant neutron flux gradients, mainly when safety analyses are performed upon the final proposed core designs.
\nTypical lattice calculation process flow for light water reactors [
In the core calculations with space-dependent data (cross sections and neutron flux), the effective cross sections are processed, with a little degradation in the accuracy as possible, by using the results from the multi-group lattice calculation. Lattice code calculation and codes are not discussed here.
\nThere are two processes followed for lattice calculation. One is the homogenization to lessen the space-dependent information and the other is group-collapsing to reduce the energy-dependent information as shown in Figure 2. The fundamental idea of both methods is to preserve neutron reaction rate. The next step is to consider the conservation of reaction rate in the energy group
Homogenization and group collapsing of cross sections [
The number of few groups depends on reactor type and computation code. Two or three groups are adopted for the NK- and TH-coupled core calculation of LWRs and much more groups (18, 33, etc.) are used for the core calculation of LMFRs (Liquid Metal Fast Reactors). Currently, revised methods exist for the improvement of cross-sections generation using computer codes dedicated to lattice calculation for few-groups approach, like in Ref. [3], where three topics are involved: (1) improved treatment of neutron-multiplying scattering reactions; (2) group constant generation in reflectors and other non-fissile regions, leading to the use of discontinuity factors in neutron diffusion codes; and (3) homogenization in leakage-corrected criticality spectrum, in which several leakage corrections are used to attain criticality, accounting for the non-physical infinite-lattice approximation. Another improvement was done in Monte Carlo codes [4], implementing reliable multi-group cross-sections calculations for collapsed flux spectrum. Ref. [4] focuses on calculating scattering cross sections, including the group-to-group scattering.
\nThe following sections contain, as a matter of example, summarized explanations of the AZKIND nuclear reactor simulator in which the reactor physics is based on neutron diffusion theory.
\nFor
In addition to boundary conditions for neutron fluxes, initial conditions must be satisfied by neutron fluxes and neutron precursor functions. Parameters involved in the above equations are described in [5].
\nThe spatial discretization of Eqs. (1) and (2) is strongly connected with the discretization of a nuclear reactor core of volume Ω. Representing the neutron flux and the precursor concentrations in terms of base functions defined over Ω, it is possible to write
\nwhere
where \n
Matrix | \nType | \nDimension | \nElements | \n
---|---|---|---|
\n\n | \nMass | \n\n\n | \n|
\n\n | \nMass | \n\n\n | \n|
\n\n | \nStiffness | \n\n\n | \n|
\n\n | \nMass | \n\n\n | \n|
\n\n | \nMass | \n\n\n | \n|
\n\n | \nMass | \n\n\n | \n|
\n\n | \nMass | \n\n\n | \n
Matrix elements from the spatial discretization.
As fully explained in [6] and summarized in [1], a simple NFE element is characterized by the fact that for each node, the function unknowns to be determined are the (00) Legendre moment (average) of the unknown function over each face of the node and the (000) Legendre moment over the node volume. Figure 3(a) shows a physical domain Ω graphically represented after generating an
Discretization of reactor volume Ω and a local node Ωe. (a) Domain Ω. (b) Physical local node Ωe.
In the NFE method RTN-0, the normalized zero-order Legendre polynomials defined over the unit cell Ω
The matrix elements are quantified introducing the following nodal basis functions [7]:
\nwhere \n
An extensive discussion on nodal diffusion methods can be found in Ref. [7] for space discretization using simplification approaches for calculating the moments over a node.
\nOnce the spatial discretization is done, the
where \n
For time integration, parameters
Once the formulation to be used for time integration is established, the
For a known vector \n
Once the computer model to solve the reactor kinetics Eqs. (1) and (2) is able to provide the neutron flux profile, the next objective is to know the power distribution in the reactor configuration. It is necessary to be aware that the neutron flux is by itself the shape of the power distribution in multiplicative materials. The numerical methods presented in previous sections to solve Eq. (9) produce an algorithm capable to obtain the neutron flux profile for a reactor steady state. The calculated neutron flux has the following property over the domain Ω: \n
Theoretically, it would be best to determine the flux level which resulted in a critical reactor \n
where \n
where \n
In a more general way, for a reactor volume
Therefore, using the reference total thermal power specified by the code user, the flux normalization factor can be written as
\nwhere the factors “kappa-fission” are \n
In summary, once the NK model is used to generate the neutron flux distribution in the reactor core, expression (12) can be used to calculate the thermal power being generated along all the nodes in a thermal-hydraulic channel of area \n
The description contained in this section is based on a work published by Ceceñas in Ref. [9] about a TH model developed for boiling water reactors. The TH model was modified from a point kinetics approach with an extension of the NK model to 3D and implemented in the development of AZKIND.
\nThe treatment of neutron kinetics in [9] has been improved by coupling a 3D solution of the neutron diffusion equations with an arrangement of TH channels in parallel. Each channel independently contemplates three regions: (1) one phase, (2) subcooled boiling, and (3) bulk boiling. The objective was to implement a detailed model of a nuclear reactor core, which is somehow perturbed to simulate NK-TH coupling. These perturbations are obtained when the power generated in a group of channels changes and thus affecting the TH state of each channel.
\nThe original [9] TH model is based on a generic channel, which is adapted by transferring to it the operational data as flow area, generated power, axial power profile, and subcooling, among other parameters. Each channel is associated with a number of nuclear fuel assemblies and an axial power profile. Although the neutron model is a two-dimensional model for the radial power profile in each
For the implementation in AZKIND of the TH model of Ceceñas, the grouping of fuel assemblies was maintained for generating a reduced number of TH channels; operational data are also used. The main difference is that the NK model recursively computes the axial power profile for each channel, and this thermal power is the updated source of power for TH model. Therefore, a “new” thermal-hydraulic condition is generated, and it is used by the NEMTAB model to update the nuclear data to generate new thermal power profiles with the NK model. The process is iterative, and it stops when the convergence is met. Convergence is achieved when updated conditions do not change in both NK and TH models.
\nThe NK-TH coupling in AZKIND performs core calculations as described above to obtain a steady-state reactor core condition. For transient conditions in a time interval
The TH model comprises the solution of the mass, momentum, and energy conservation equations in the three regions contemplated by the channel: (1) one phase, (2) subcooled boiling, and (3) bulk boiling. The system receives heat through a non-uniform source whose profile is axially defined plane by plane. This axial use of the power profile allows the inclusion of a wide range of axial profiles, from relatively flat to profiles with their peak value at some axial point in each channel in the core.
\nIn the following subsections, there are several expressions for which the corresponding parameters are defined in Refs. [10, 11].
\nThe heat transfer and temperature distribution in the fuel and cladding can be calculated by a simple model where the heat diffusion equation is solved in one dimension (radial) for a fuel rod, since the conduction in axial direction is small compared to the radial one, it can be neglected. An energy balance per unit length yields
\nwhere \n
The conservation equations of mass, energy, and momentum are applied in this case to a flow of water along a vertical channel, where the dynamics of the fluid heated by the wall of the fuel is modeled. Conservation equations can be expressed as [10]
\nIn this work, the conservation equations are solved by the Integral Moment method [11], according to which it is assumed that the refrigerant is incompressible but thermally expandable, and the density is a function of enthalpy at a constant pressure
\nNeglecting terms related to pressure changes and wall friction forces, the energy equation is simplified as
\nwhere the axial flow variation can be obtained by
\nThis equation provides the flow variations with respect to an average value imposed as a boundary value or provided by the dynamics of the coolant recirculation system. Three regions are defined by which the coolant circulates as it ascends into the channel: a one-phase region, a subcooled boiling region, and a bulk boiling region. The first region begins at the bottom of the channel, where the coolant enters with known enthalpy and ends at the point of separation of the bubbles
where, in this case, the parameters ks and
ks = 0.71 + 1.2865 × 10−3
The total pressure drop in the channel is made up of the contributions of each region. Every term in each region includes the contribution by acceleration, gravity, and friction. For the channel arrangement, the steady state is obtained by iterating the coolant flow rate of each channel to obtain the same pressure drop for all of them. This iteration consists of a correction to the flow defined by the deviation of the pressure drop of the channel with respect to the average of all the channels:
\nwhere \n
It is observed that even though the pressures are equaled, the value of the pressure drop in the core is not imposed as a boundary condition. Convergence is achieved when the following relationship is met: \n
Although reference [12] has important issues to be considered in the development of an NK-TH-coupled model, those issues are not repeated here, but taken into account. The most direct way of coupling NK module and TH module, as implemented in AZKIND, consists simply in that axially both NK mesh and TH mesh have the same partition, making possible to assign an NK node at position
As it can be seen in Figure 4, before initiating the NK-TH feedback process, the initial nuclear parameters and kinetics parameter (XS) are loaded from files constructed in NEMTAB format, previously generated by means of a lattice code. Then, following the reading of the nuclear reactor burn-up state and thermo-physics initial conditions, the XS parameters are obtained from the Nemtab multi-dimensional tables by means of interpolation calculations.
\nThe NK-TH feedback process in AZKIND.
The process continues as follows. The corresponding neutron flux is calculated in the NK module with the
The important variables sent to the NK module are the fuel temperature (
HPC was implemented in AZKIND with the support of the linear algebra solvers library PARALUTION [13]. This open-source library is optimized for parallel computing process using graphics processing units (GPUs). For the numerical solution of an algebraic system \n
To demonstrate the HPC implementation in AZKIND, as described in Ref. [1], very large matrices were constructed for fine spatial discretization of arrangements of nuclear fuel assemblies of an LWR. Fine discretization means that each fuel assembly was subdivided in a mesh of size 10 × 10. As an example, an arrangement of 6 × 6 fuel assemblies consists of a square with 36 fine-discretized fuel assemblies. The corresponding algebraic system for each fuel arrangement was solved with parallel processing performed by the
Assemblies array: Matrix dimension ( | \n1 × 1 126,200 1,332,400 | \n2 × 2 492,800 5,305,600 | \n4 × 4 1,947,200 21,174,400 | \n6 × 6 4,363,200 47,606,400 | \n10 × 10 12,080,000 132,160,000 | \n
---|---|---|---|---|---|
Serial | \n24 | \n124 | \n372 | \n994 | \n2471 | \n
GTX 860 M | \n2.1 | \n7.9 | \n31.3 | \nNo memory | \nNo memory | \n
Tesla K20c | \n1.3 | \n4.0 | \n16.6 | \n40.1 | \nNo memory | \n
GTX TITAN X | \n1.0 | \n2.6 | \n10.4 | \n26.7 | \n95.4 | \n
Parallel processing time (seconds) in different architectures [1].
Assemblies | \n1 × 1 | \n2 × 2 | \n4 × 4 | \n6 × 6 | \n10 × 10 | \n
---|---|---|---|---|---|
GTX 860 M | \n11 | \n16 | \n12 | \n– | \n– | \n
Tesla K20c | \n18 | \n31 | \n22 | \n24 | \n– | \n
GTX TITAN X | \n24 | \n48 | \n36 | \n37 | \n26 | \n
Speedup comparison (S) [1].
Figure 5 [1] shows the distribution of nuclear fuel assemblies in the core of a boiling water reactor. Excepting the blue-shaded zone, colors are for different types of fuel assemblies. In the plane
A map of fuel assemblies in an LWR [
As described in [1], a reactor power transient was simulated as the capability to remove neutrons was highly increased in the perturbed assembly shown in Figure 5. An increase as step function in the neutrons removal capability during 3 s is implemented in the perturbed assembly, after that the perturbation finishes and the transient lasts for two more seconds, giving a reactor power reduction. The time step used in this simulation was 0.1 s. Figure 6 shows the power behavior over time, departing from a normalized value of 1.0 and reducing the power reactor to almost 80% of its original value. This reactor power transient was simulated with the AZKIND code, running on the three different GPUs listed in Tables 2 and 3. The right side of Figure 6 shows the time spent by AZKIND in a logarithmic scale, running in a sequential mode (Serial bar) and the times spent by each GPU card.
\nSimulation of a reactor power transient—serial and parallel processing.
A simple example was prepared to show the capability of the AZKIND code running with NK-TH coupling, and the thermal-hydraulic effect on power distribution is compared to the power distribution resulted from the NK model running standalone.
\nThis example was prepared for a two energy group, that is, fast neutrons and thermal neutrons. In LWR, the nuclear fissions of the fuel atoms are mainly coming from the thermal neutrons present in the reactor core. The effect observed in Figure 7 is that the TH feedback induces an increase in the thermal neutrons population and so increasing power. As the coolant/moderator enters the reactor core through the bottom part of the reactor and the core is beginning the production cycle, the core design allows more power generation in the first third of the core active fuel. Also, as it was expected, in the map of fuel assemblies of the reactor core, the location of the fuel assembly with the highest generation of thermal power remained unchanged with the insertion of TH feedback.
\nAxial power peaking profile location.
In the last two decades, there have been significant advances in the development of nuclear reactor codes for 3D simulation with coupling NK-TH, supported with new modeling techniques and modern computing capabilities in software and hardware. Some examples of these advances are listed subsequently:
DYNSUB: Pin-based coupling of the simplified transport (SP3) version of DYN3D with the sub-channel code SUBCHANFLOW. See [16, 17]. The new coupled code system allows for a more realistic description of the core behavior under steady state and transient conditions. DYNSUB has successfully been applied to analyze the behavior of one eight of a PWR core during an REA transient by a pin-by-pin simulation consisting of a huge number of nodes. Some insights are pointed out on the convergence process with a detailed coupling solution modeling neighbor sub-channels and modeling adjacent assembly channels.
DYN3D: The code comprises various 3D neutron kinetics solvers, a thermal-hydraulics reactor core model, and a thermo-mechanical fuel rod model, see [18]. The following topics are delineated in the reference: the latest developments of models and methods, a status of verification and validation; code applications for selected safety analyses; multi-physics code couplings to thermal-hydraulic system codes, CFD, and sub-channel codes as well as to the fuel performance code TRANSURANUS.
TRACE/PARCS: See [19]. The study of the coupling capability of the TRACE and PARCS codes by analyzing the “Main Steam Line Break (MSLB) benchmark problem,” consisting of a double-ended MSLB accident assumed to occur in the Babcock and Wilcox Three Mile Island Unit 1. The model TRACE/PARCS generated data showing that these codes have the capability to predict expected phenomena typical of this transient and the related NK-TH feedback.
COBAYA3: See [20]. This reference describes a multi-physics system of codes including the 3D multi-group neutron diffusion codes, ANDES and COBAYA3-PBP, coupled with the sub-channel thermal-hydraulic codes COBRA-TF, COBRA-IIIc, and SUBCHANFLOW, for the simulation of LWR core transients. Implementation of the PARALUTION library to solve sparse systems of linear equations was done. It features several types of iterative solvers and preconditioners which can run on both multi-core CPUs and GPU devices without any modification from the interface point of view. By exploring this technology, namely the implementation of the PARALUTION library in COBAYA3, the code can decrease the solution time of the sparse linear systems by a factor of 5.15 on GPU and 2.56 on a multi-core CPU using standard hardware.
CNFR: See [21]. This reference summarizes three methods, implemented for multi-core CPU and GPU, to evaluate fuel burn-up in a pressurized light water nuclear reactor (PWR) using the solutions of a large system of coupled ordinary differential equations. The reactor physics simulation of a PWR with burn-up calculations spends long execution times, so that performance improvement using GPU can imply in a better core design and thus extended fuel life cycle. The results with parallel computing exhibit speed improvement exceeding 200 times over the sequential solver, within 1% accuracy.
The state of the art in the topic of nuclear reactor simulations shows significant advances in the development of computer codes. A wide range of applications focusing, besides on improving nuclear safety, on more efficient analyses to improve fuel cycles/depletion have been found in a recent study. A considerable “saving time” factor in obtaining nuclear reactor analyses has been observed.
\nOne important part of a nuclear reactor simulator is the benchmarking process to demonstrate reliability and repeatability in the simulation of real cases, for which data from reactor operation or comprehensive data from experiments are well documented. In this sense, extensive documentation is necessary for theoretical basis, numerical techniques and tools, and validation of both codes and simulation models.
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His studies in robotics lead him not only to a PhD degree but also inspired him to co-found and build the International Journal of Advanced Robotic Systems - world's first Open Access journal in the field of robotics.",institutionString:null,institution:{name:"TU Wien",country:{name:"Austria"}}},{id:"441",title:"Ph.D.",name:"Jaekyu",middleName:null,surname:"Park",slug:"jaekyu-park",fullName:"Jaekyu Park",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/441/images/1881_n.jpg",biography:null,institutionString:null,institution:{name:"LG Corporation (South Korea)",country:{name:"Korea, South"}}},{id:"465",title:"Dr.",name:"Christian",middleName:null,surname:"Martens",slug:"christian-martens",fullName:"Christian Martens",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Rheinmetall (Germany)",country:{name:"Germany"}}},{id:"479",title:"Dr.",name:"Valentina",middleName:null,surname:"Colla",slug:"valentina-colla",fullName:"Valentina Colla",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/479/images/358_n.jpg",biography:null,institutionString:null,institution:{name:"Sant'Anna School of Advanced Studies",country:{name:"Italy"}}},{id:"494",title:"PhD",name:"Loris",middleName:null,surname:"Nanni",slug:"loris-nanni",fullName:"Loris Nanni",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/494/images/system/494.jpg",biography:"Loris Nanni received his Master Degree cum laude on June-2002 from the University of Bologna, and the April 26th 2006 he received his Ph.D. in Computer Engineering at DEIS, University of Bologna. On September, 29th 2006 he has won a post PhD fellowship from the university of Bologna (from October 2006 to October 2008), at the competitive examination he was ranked first in the industrial engineering area. He extensively served as referee for several international journals. He is author/coauthor of more than 100 research papers. He has been involved in some projects supported by MURST and European Community. His research interests include pattern recognition, bioinformatics, and biometric systems (fingerprint classification and recognition, signature verification, face recognition).",institutionString:null,institution:null},{id:"496",title:"Dr.",name:"Carlos",middleName:null,surname:"Leon",slug:"carlos-leon",fullName:"Carlos Leon",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"University of Seville",country:{name:"Spain"}}},{id:"512",title:"Dr.",name:"Dayang",middleName:null,surname:"Jawawi",slug:"dayang-jawawi",fullName:"Dayang Jawawi",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"University of Technology Malaysia",country:{name:"Malaysia"}}},{id:"528",title:"Dr.",name:"Kresimir",middleName:null,surname:"Delac",slug:"kresimir-delac",fullName:"Kresimir Delac",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/528/images/system/528.jpg",biography:"K. Delac received his B.Sc.E.E. degree in 2003 and is currentlypursuing a Ph.D. degree at the University of Zagreb, Faculty of Electrical Engineering andComputing. His current research interests are digital image analysis, pattern recognition andbiometrics.",institutionString:null,institution:{name:"University of Zagreb",country:{name:"Croatia"}}},{id:"557",title:"Dr.",name:"Andon",middleName:"Venelinov",surname:"Topalov",slug:"andon-topalov",fullName:"Andon Topalov",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/557/images/1927_n.jpg",biography:"Dr. Andon V. Topalov received the MSc degree in Control Engineering from the Faculty of Information Systems, Technologies, and Automation at Moscow State University of Civil Engineering (MGGU) in 1979. He then received his PhD degree in Control Engineering from the Department of Automation and Remote Control at Moscow State Mining University (MGSU), Moscow, in 1984. 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This chapter aims for those who need to teach Kalman filters to others, or for those who do not have a strong background in estimation theory. Following a problem definition of state estimation, filtering algorithms will be presented with supporting examples to help readers easily grasp how the Kalman filters work. Implementations on INS/GNSS navigation, target tracking, and terrain-referenced navigation (TRN) are given. In each example, we discuss how to choose, implement, tune, and modify the algorithms for real world practices. Source codes for implementing the examples are also provided. 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The effects of average inlet velocity and cavitation number on entropy production have been presented and discussed. The results obtained show that the discharge coefficient is weakly influenced by the length of the orifice and the radius of the wedge has a large effect on the intensity and distribution of cavitation along the injection nozzle. 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To that end, knowledge employees play an important role in raising their voices with feedback and ideas. This chapter explores the role of knowledge employees in digital startups and how top management can ensure an organizational ecosystem where knowledge employees can flourish and contribute to the competitive advantage of the firms. 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Missing person cases consume resources from the already overstretched resources of Police Forces. Such cases predominantly come from at-risk groups such as children in care, people suffering from depression, or elderly people suffering from dementia. This chapter reviews current practices used for missing person cases and describes a decision support model based on Bayesian networks.",book:{id:"11068",title:"Contemporary Issues in Information Systems - a Global Perspective",coverURL:"https://cdn.intechopen.com/books/images_new/11068.jpg"},signatures:"Denis Reilly"},{id:"82158",title:"A Usability Analysis of the DAO Concept Based on the Case Study of a Blockchain Game",slug:"a-usability-analysis-of-the-dao-concept-based-on-the-case-study-of-a-blockchain-game",totalDownloads:7,totalDimensionsCites:0,doi:"10.5772/intechopen.105347",abstract:"Games based on a blockchain exist in all variants and facets, mostly as single- or multiplayer games. This chapter deals with the implementation of a multiplayer strategy game (Connect Four), using blockchain technology for decentralized data storage and the entire game logic. The focus is on the use of the decentralized autonomous organization (DAO) principle, for coordination and voting within the teams. The chosen game just stands as an example; other games or gamification approaches in which users can take decisions collaboratively can be used here. A web application was implemented acting as a central interface between players and the blockchain. Hence, it was possible for players to compete against each other in teams and to collectively decide the next move by participating in a roundly voting. With the help of a standardized questionnaire, answered by each player after each match, possible impacts of the voting mechanism on the usability were determined.",book:{id:"11552",title:"Gamification - Analysis, Design and Development",coverURL:"https://cdn.intechopen.com/books/images_new/11552.jpg"},signatures:"Lars Karbach, Mortiz Korte, Nils Orbat, Daniel Muschiol and March Jansen"},{id:"82351",title:"Building an Online Ecosystem for English Teaching and Learning in the Times of Covid-19 Pandemic and Beyond",slug:"building-an-online-ecosystem-for-english-teaching-and-learning-in-the-times-of-covid-19-pandemic-and",totalDownloads:5,totalDimensionsCites:0,doi:"10.5772/intechopen.105651",abstract:"Online education is a revolutionary trend of educational technology today, particularly, since the Covid-19 pandemic, online classes have become the cornerstone of modern education. The rapid growth of online learning is challenging the relevance of traditional higher education systems. Although many efforts from educational institutions to promote online teaching and learning have rapidly increased, progress is far from sufficient, especially when a model of an online learning system needs further examination in practice after the pandemic. This chapter reports a case study at the center of foreign languages in Vietnam, addressing the need to develop an online learning ecosystem during the pandemic crisis and beyond. Characteristics of an online learning ecosystem are presented and basic steps of developing an online ecosystem for English teaching and learning are discussed in detail. The suggested ecosystem should cover the five key components including teaching environment, relationships between teachers and learners and between learners and learners, teaching methods, teaching content and resources, and teaching evaluation. 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The whole process of submitting an article and editing of the submitted article goes extremely smooth and fast, the number of reads and downloads of chapters is high, and the contributions are also frequently cited.",author:{id:"55578",name:"Antonio",surname:"Jurado-Navas",institutionString:null,profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bRisIQAS/Profile_Picture_1626166543950",slug:"antonio-jurado-navas",institution:{id:"720",name:"University of Malaga",country:{id:null,name:"Spain"}}}}]},series:{item:{id:"14",title:"Artificial Intelligence",doi:"10.5772/intechopen.79920",issn:"2633-1403",scope:"Artificial Intelligence (AI) is a rapidly developing multidisciplinary research area that aims to solve increasingly complex problems. In today's highly integrated world, AI promises to become a robust and powerful means for obtaining solutions to previously unsolvable problems. 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Prior to his appointment at Stellenbosch University, he has been at the University of Pretoria, Department of Computer Science (1998-2018), where he was appointed as South Africa Research Chair in Artifical Intelligence (2007-2018), the head of the Department of Computer Science (2008-2017), and Director of the Institute for Big Data and Data Science (2017-2018). 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He is a full professor of signal processing and pattern recognition and is head of the Signals and Communications Department at ULPGC, teaching from 2001 on subjects on signal processing and learning theory. His research lines are biometrics, biomedical signals and images, data mining, classification system, signal and image processing, machine learning, and environmental intelligence. He has researched in 52 international and Spanish research projects, some of them as head researcher. He is co-author of 4 books, co-editor of 27 proceedings books, guest editor for 8 JCR-ISI international journals, and up to 24 book chapters. He has over 450 papers published in international journals and conferences (81 of them indexed on JCR – ISI - Web of Science). He has published seven patents in the Spanish Patent and Trademark Office. He has been a supervisor on 8 Ph.D. theses (11 more are under supervision), and 130 master theses. He is the founder of The IEEE IWOBI conference series and the president of its Steering Committee, as well as the founder of both the InnoEducaTIC and APPIS conference series. He is an evaluator of project proposals for the European Union (H2020), Medical Research Council (MRC, UK), Spanish Government (ANECA, Spain), Research National Agency (ANR, France), DAAD (Germany), Argentinian Government, and the Colombian Institutions. He has been a reviewer in different indexed international journals (<70) and conferences (<250) since 2001. He has been a member of the IASTED Technical Committee on Image Processing from 2007 and a member of the IASTED Technical Committee on Artificial Intelligence and Expert Systems from 2011. \n\nHe has held the general chair position for the following: ACM-APPIS (2020, 2021), IEEE-IWOBI (2019, 2020 and 2020), A PPIS (2018, 2019), IEEE-IWOBI (2014, 2015, 2017, 2018), InnoEducaTIC (2014, 2017), IEEE-INES (2013), NoLISP (2011), JRBP (2012), and IEEE-ICCST (2005)\n\nHe is an associate editor of the Computational Intelligence and Neuroscience Journal (Hindawi – Q2 JCR-ISI). He was vice dean from 2004 to 2010 in the Higher Technical School of Telecommunication Engineers at ULPGC and the vice dean of Graduate and Postgraduate Studies from March 2013 to November 2017. He won the “Catedra Telefonica” Awards in Modality of Knowledge Transfer, 2017, 2018, and 2019 editions, and awards in Modality of COVID Research in 2020.\n\nPublic References:\nResearcher ID http://www.researcherid.com/rid/N-5967-2014\nORCID https://orcid.org/0000-0002-4621-2768 \nScopus Author ID https://www.scopus.com/authid/detail.uri?authorId=6602376272\nScholar Google https://scholar.google.es/citations?user=G1ks9nIAAAAJ&hl=en \nResearchGate https://www.researchgate.net/profile/Carlos_Travieso",institutionString:null,institution:{name:"University of Las Palmas de Gran Canaria",institutionURL:null,country:{name:"Spain"}}},editorTwo:null,editorThree:null},{id:"23",title:"Computational Neuroscience",coverUrl:"https://cdn.intechopen.com/series_topics/covers/23.jpg",isOpenForSubmission:!0,editor:{id:"14004",title:"Dr.",name:"Magnus",middleName:null,surname:"Johnsson",slug:"magnus-johnsson",fullName:"Magnus Johnsson",profilePictureURL:"https://mts.intechopen.com/storage/users/14004/images/system/14004.png",biography:"Dr Magnus Johnsson is a cross-disciplinary scientist, lecturer, scientific editor and AI/machine learning consultant from Sweden. \n\nHe is currently at Malmö University in Sweden, but also held positions at Lund University in Sweden and at Moscow Engineering Physics Institute. \nHe holds editorial positions at several international scientific journals and has served as a scientific editor for books and special journal issues. \nHis research interests are wide and include, but are not limited to, autonomous systems, computer modeling, artificial neural networks, artificial intelligence, cognitive neuroscience, cognitive robotics, cognitive architectures, cognitive aids and the philosophy of mind. \n\nDr. Johnsson has experience from working in the industry and he has a keen interest in the application of neural networks and artificial intelligence to fields like industry, finance, and medicine. \n\nWeb page: www.magnusjohnsson.se",institutionString:null,institution:{name:"Malmö University",institutionURL:null,country:{name:"Sweden"}}},editorTwo:null,editorThree:null},{id:"24",title:"Computer Vision",coverUrl:"https://cdn.intechopen.com/series_topics/covers/24.jpg",isOpenForSubmission:!0,editor:{id:"294154",title:"Prof.",name:"George",middleName:null,surname:"Papakostas",slug:"george-papakostas",fullName:"George Papakostas",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002hYaGbQAK/Profile_Picture_1624519712088",biography:"George A. Papakostas has received a diploma in Electrical and Computer Engineering in 1999 and the M.Sc. and Ph.D. degrees in Electrical and Computer Engineering in 2002 and 2007, respectively, from the Democritus University of Thrace (DUTH), Greece. Dr. Papakostas serves as a Tenured Full Professor at the Department of Computer Science, International Hellenic University, Greece. Dr. Papakostas has 10 years of experience in large-scale systems design as a senior software engineer and technical manager, and 20 years of research experience in the field of Artificial Intelligence. Currently, he is the Head of the “Visual Computing” division of HUman-MAchines INteraction Laboratory (HUMAIN-Lab) and the Director of the MPhil program “Advanced Technologies in Informatics and Computers” hosted by the Department of Computer Science, International Hellenic University. He has (co)authored more than 150 publications in indexed journals, international conferences and book chapters, 1 book (in Greek), 3 edited books, and 5 journal special issues. His publications have more than 2100 citations with h-index 27 (GoogleScholar). His research interests include computer/machine vision, machine learning, pattern recognition, computational intelligence. \nDr. Papakostas served as a reviewer in numerous journals, as a program\ncommittee member in international conferences and he is a member of the IAENG, MIR Labs, EUCogIII, INSTICC and the Technical Chamber of Greece (TEE).",institutionString:null,institution:{name:"International Hellenic University",institutionURL:null,country:{name:"Greece"}}},editorTwo:null,editorThree:null},{id:"25",title:"Evolutionary Computation",coverUrl:"https://cdn.intechopen.com/series_topics/covers/25.jpg",isOpenForSubmission:!0,editor:{id:"136112",title:"Dr.",name:"Sebastian",middleName:null,surname:"Ventura Soto",slug:"sebastian-ventura-soto",fullName:"Sebastian Ventura Soto",profilePictureURL:"https://mts.intechopen.com/storage/users/136112/images/system/136112.png",biography:"Sebastian Ventura is a Spanish researcher, a full professor with the Department of Computer Science and Numerical Analysis, University of Córdoba. Dr Ventura also holds the positions of Affiliated Professor at Virginia Commonwealth University (Richmond, USA) and Distinguished Adjunct Professor at King Abdulaziz University (Jeddah, Saudi Arabia). Additionally, he is deputy director of the Andalusian Research Institute in Data Science and Computational Intelligence (DaSCI) and heads the Knowledge Discovery and Intelligent Systems Research Laboratory. He has published more than ten books and over 300 articles in journals and scientific conferences. Currently, his work has received over 18,000 citations according to Google Scholar, including more than 2200 citations in 2020. In the last five years, he has published more than 60 papers in international journals indexed in the JCR (around 70% of them belonging to first quartile journals) and he has edited some Springer books “Supervised Descriptive Pattern Mining” (2018), “Multiple Instance Learning - Foundations and Algorithms” (2016), and “Pattern Mining with Evolutionary Algorithms” (2016). He has also been involved in more than 20 research projects supported by the Spanish and Andalusian governments and the European Union. He currently belongs to the editorial board of PeerJ Computer Science, Information Fusion and Engineering Applications of Artificial Intelligence journals, being also associate editor of Applied Computational Intelligence and Soft Computing and IEEE Transactions on Cybernetics. Finally, he is editor-in-chief of Progress in Artificial Intelligence. He is a Senior Member of the IEEE Computer, the IEEE Computational Intelligence, and the IEEE Systems, Man, and Cybernetics Societies, and the Association of Computing Machinery (ACM). Finally, his main research interests include data science, computational intelligence, and their applications.",institutionString:null,institution:{name:"University of Córdoba",institutionURL:null,country:{name:"Spain"}}},editorTwo:null,editorThree:null},{id:"26",title:"Machine Learning and Data Mining",coverUrl:"https://cdn.intechopen.com/series_topics/covers/26.jpg",isOpenForSubmission:!0,editor:{id:"24555",title:"Dr.",name:"Marco Antonio",middleName:null,surname:"Aceves Fernandez",slug:"marco-antonio-aceves-fernandez",fullName:"Marco Antonio Aceves Fernandez",profilePictureURL:"https://mts.intechopen.com/storage/users/24555/images/system/24555.jpg",biography:"Dr. Marco Antonio Aceves Fernandez obtained his B.Sc. (Eng.) in Telematics from the Universidad de Colima, Mexico. 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