Comparison of different CSSO methods with direct SQP for an analytical optimization problem
\r\n\tIn sum, the book presents a reflective analysis of the pedagogical hubs for a changing world, considering the most fundamental areas of the current contingencies in education.
",isbn:"978-1-83968-793-8",printIsbn:"978-1-83968-792-1",pdfIsbn:"978-1-83968-794-5",doi:null,price:0,priceEur:0,priceUsd:0,slug:null,numberOfPages:0,isOpenForSubmission:!1,hash:"b01f9136149277b7e4cbc1e52bce78ec",bookSignature:"Dr. María Jose Hernandez-Serrano",publishedDate:null,coverURL:"https://cdn.intechopen.com/books/images_new/10229.jpg",keywords:"Teacher Digital Competences, Flipped Learning, Online Resources Design, Neuroscientific Literacy (Myths), Emotions and Learning, Multisensory Stimulation, Citizen Skills, Violence Prevention, Moral Development, Universal Design for Learning, Sensitizing on Diversity, Supportive Strategies",numberOfDownloads:null,numberOfWosCitations:0,numberOfCrossrefCitations:null,numberOfDimensionsCitations:null,numberOfTotalCitations:null,isAvailableForWebshopOrdering:!0,dateEndFirstStepPublish:"September 14th 2020",dateEndSecondStepPublish:"October 12th 2020",dateEndThirdStepPublish:"December 11th 2020",dateEndFourthStepPublish:"March 1st 2021",dateEndFifthStepPublish:"April 30th 2021",remainingDaysToSecondStep:"3 months",secondStepPassed:!0,currentStepOfPublishingProcess:4,editedByType:null,kuFlag:!1,biosketch:"Dr. Phil. Maria Jose Hernandez Serrano is a tenured lecturer in the Department of Theory and History of Education at the University of Salamanca, where she currently teaches on Teacher Education. She graduated in Social Education (2000) and Psycho-Pedagogy (2003) at the University of Salamanca. Then, she obtained her European Ph.D. in Education and Training in Virtual Environments by research with the University of Manchester, UK (2009).",coeditorOneBiosketch:null,coeditorTwoBiosketch:null,coeditorThreeBiosketch:null,coeditorFourBiosketch:null,coeditorFiveBiosketch:null,editors:[{id:"187893",title:"Dr.",name:"María Jose",middleName:null,surname:"Hernandez-Serrano",slug:"maria-jose-hernandez-serrano",fullName:"María Jose Hernandez-Serrano",profilePictureURL:"https://mts.intechopen.com/storage/users/187893/images/system/187893.jpg",biography:"DPhil Maria Jose Hernandez Serrano is a tenured Lecturer in the Department of Theory and History of Education at the University of Salamanca (Spain), where she currently teaches on Teacher Education. She graduated in Social Education (2000) and Psycho-Pedagogy (2003) at the University of Salamanca. Then, she obtained her European Ph.D. on Education and Training in Virtual Environments by research with the University of Manchester, UK (2009). She obtained a Visiting Scholar Postdoctoral Grant (of the British Academy, UK) at the Oxford Internet Institute of the University of Oxford (2011) and was granted with a postdoctoral research (in 2021) at London Birbeck University.\n \nShe is author of more than 20 research papers, and more than 35 book chapters (H Index 10). She is interested in the study of the educational process and the analysis of cognitive and affective processes in the context of neuroeducation and neurotechnologies, along with the study of social contingencies affecting the educational institutions and requiring new skills for educators.\n\nHer publications are mainly of the educational process mediated by technologies and digital competences. Currently, her new research interests are: the transdisciplinary application of the brain-based research to the educational context and virtual environments, and the neuropedagogical implications of the technologies on the development of the brain in younger students. Also, she is interested in the promotion of creative and critical uses of digital technologies, the emerging uses of social media and transmedia, and the informal learning through technologies.\n\nShe is a member of several research Networks and Scientific Committees in international journals on Educational Technologies and Educommunication, and collaborates as a reviewer in several prestigious journals (see public profile in Publons).\n\nUntil March 2010 she was in charge of the Adult University of Salamanca, by coordinating teaching activities of more than a thousand adult students. She currently is, since 2014, the Secretary of the Department of Theory and History of Education. Since 2015 she collaborates with the Council Educational Program by training teachers and families in the translation of advances from educational neuroscience.",institutionString:"University of Salamanca",position:null,outsideEditionCount:0,totalCites:0,totalAuthoredChapters:"0",totalChapterViews:"0",totalEditedBooks:"0",institution:{name:"University of Salamanca",institutionURL:null,country:{name:"Spain"}}}],coeditorOne:null,coeditorTwo:null,coeditorThree:null,coeditorFour:null,coeditorFive:null,topics:[{id:"23",title:"Social Sciences",slug:"social-sciences"}],chapters:null,productType:{id:"1",title:"Edited Volume",chapterContentType:"chapter",authoredCaption:"Edited by"},personalPublishingAssistant:{id:"301331",firstName:"Mia",lastName:"Vulovic",middleName:null,title:"Mrs.",imageUrl:"https://mts.intechopen.com/storage/users/301331/images/8498_n.jpg",email:"mia.v@intechopen.com",biography:"As an Author Service Manager, my responsibilities include monitoring and facilitating all publishing activities for authors and editors. From chapter submission and review to approval and revision, copyediting and design, until final publication, I work closely with authors and editors to ensure a simple and easy publishing process. I maintain constant and effective communication with authors, editors and reviewers, which allows for a level of personal support that enables contributors to fully commit and concentrate on the chapters they are writing, editing, or reviewing. I assist authors in the preparation of their full chapter submissions and track important deadlines and ensure they are met. I help to coordinate internal processes such as linguistic review, and monitor the technical aspects of the process. As an ASM I am also involved in the acquisition of editors. Whether that be identifying an exceptional author and proposing an editorship collaboration, or contacting researchers who would like the opportunity to work with IntechOpen, I establish and help manage author and editor acquisition and contact."}},relatedBooks:[{type:"book",id:"6942",title:"Global Social Work",subtitle:"Cutting Edge Issues and Critical Reflections",isOpenForSubmission:!1,hash:"222c8a66edfc7a4a6537af7565bcb3de",slug:"global-social-work-cutting-edge-issues-and-critical-reflections",bookSignature:"Bala Raju Nikku",coverURL:"https://cdn.intechopen.com/books/images_new/6942.jpg",editedByType:"Edited by",editors:[{id:"263576",title:"Dr.",name:"Bala",surname:"Nikku",slug:"bala-nikku",fullName:"Bala Nikku"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"1591",title:"Infrared Spectroscopy",subtitle:"Materials Science, Engineering and Technology",isOpenForSubmission:!1,hash:"99b4b7b71a8caeb693ed762b40b017f4",slug:"infrared-spectroscopy-materials-science-engineering-and-technology",bookSignature:"Theophile Theophanides",coverURL:"https://cdn.intechopen.com/books/images_new/1591.jpg",editedByType:"Edited by",editors:[{id:"37194",title:"Dr.",name:"Theophanides",surname:"Theophile",slug:"theophanides-theophile",fullName:"Theophanides Theophile"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"3092",title:"Anopheles mosquitoes",subtitle:"New insights into malaria vectors",isOpenForSubmission:!1,hash:"c9e622485316d5e296288bf24d2b0d64",slug:"anopheles-mosquitoes-new-insights-into-malaria-vectors",bookSignature:"Sylvie Manguin",coverURL:"https://cdn.intechopen.com/books/images_new/3092.jpg",editedByType:"Edited by",editors:[{id:"50017",title:"Prof.",name:"Sylvie",surname:"Manguin",slug:"sylvie-manguin",fullName:"Sylvie Manguin"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"3161",title:"Frontiers in Guided Wave Optics and Optoelectronics",subtitle:null,isOpenForSubmission:!1,hash:"deb44e9c99f82bbce1083abea743146c",slug:"frontiers-in-guided-wave-optics-and-optoelectronics",bookSignature:"Bishnu Pal",coverURL:"https://cdn.intechopen.com/books/images_new/3161.jpg",editedByType:"Edited by",editors:[{id:"4782",title:"Prof.",name:"Bishnu",surname:"Pal",slug:"bishnu-pal",fullName:"Bishnu Pal"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"72",title:"Ionic Liquids",subtitle:"Theory, Properties, New Approaches",isOpenForSubmission:!1,hash:"d94ffa3cfa10505e3b1d676d46fcd3f5",slug:"ionic-liquids-theory-properties-new-approaches",bookSignature:"Alexander Kokorin",coverURL:"https://cdn.intechopen.com/books/images_new/72.jpg",editedByType:"Edited by",editors:[{id:"19816",title:"Prof.",name:"Alexander",surname:"Kokorin",slug:"alexander-kokorin",fullName:"Alexander Kokorin"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"1373",title:"Ionic Liquids",subtitle:"Applications and Perspectives",isOpenForSubmission:!1,hash:"5e9ae5ae9167cde4b344e499a792c41c",slug:"ionic-liquids-applications-and-perspectives",bookSignature:"Alexander Kokorin",coverURL:"https://cdn.intechopen.com/books/images_new/1373.jpg",editedByType:"Edited by",editors:[{id:"19816",title:"Prof.",name:"Alexander",surname:"Kokorin",slug:"alexander-kokorin",fullName:"Alexander Kokorin"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"57",title:"Physics and Applications of Graphene",subtitle:"Experiments",isOpenForSubmission:!1,hash:"0e6622a71cf4f02f45bfdd5691e1189a",slug:"physics-and-applications-of-graphene-experiments",bookSignature:"Sergey Mikhailov",coverURL:"https://cdn.intechopen.com/books/images_new/57.jpg",editedByType:"Edited by",editors:[{id:"16042",title:"Dr.",name:"Sergey",surname:"Mikhailov",slug:"sergey-mikhailov",fullName:"Sergey Mikhailov"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"371",title:"Abiotic Stress in Plants",subtitle:"Mechanisms and Adaptations",isOpenForSubmission:!1,hash:"588466f487e307619849d72389178a74",slug:"abiotic-stress-in-plants-mechanisms-and-adaptations",bookSignature:"Arun Shanker and B. Venkateswarlu",coverURL:"https://cdn.intechopen.com/books/images_new/371.jpg",editedByType:"Edited by",editors:[{id:"58592",title:"Dr.",name:"Arun",surname:"Shanker",slug:"arun-shanker",fullName:"Arun Shanker"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"878",title:"Phytochemicals",subtitle:"A Global Perspective of Their Role in Nutrition and Health",isOpenForSubmission:!1,hash:"ec77671f63975ef2d16192897deb6835",slug:"phytochemicals-a-global-perspective-of-their-role-in-nutrition-and-health",bookSignature:"Venketeshwer Rao",coverURL:"https://cdn.intechopen.com/books/images_new/878.jpg",editedByType:"Edited by",editors:[{id:"82663",title:"Dr.",name:"Venketeshwer",surname:"Rao",slug:"venketeshwer-rao",fullName:"Venketeshwer Rao"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"4816",title:"Face Recognition",subtitle:null,isOpenForSubmission:!1,hash:"146063b5359146b7718ea86bad47c8eb",slug:"face_recognition",bookSignature:"Kresimir Delac and Mislav Grgic",coverURL:"https://cdn.intechopen.com/books/images_new/4816.jpg",editedByType:"Edited by",editors:[{id:"528",title:"Dr.",name:"Kresimir",surname:"Delac",slug:"kresimir-delac",fullName:"Kresimir Delac"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}}]},chapter:{item:{type:"chapter",id:"19532",title:"Concurrent Subspace Optimization for Aircraft System Design",doi:"10.5772/19143",slug:"concurrent-subspace-optimization-for-aircraft-system-design",body:'Concurrent Subspace Optimization (CSSO) is one of the main decomposition approaches in Multidisciplinary Design Optimization (MDO). It supports a collaborative and distributed multidisciplinary design optimization environment among different disciplinary groups. Sobieski first proposed the subspace optimization method (Sobieszczanski-Sobieski, 1988), and Sobieski’s blueprint was further developed by Bloebaum and subsequently named the concurrent subspace optimization method (Bolebaum, 1991). Renaud developed a second-order variant of the Global Sensitivity Equation (GSE) method and an alternative potential coordination procedure for the CSSO method (Renaud & Gabriele, 1993a, 1993b, 1994). Sellar proposed to replace GSE with the neutral-network based response surface method (Sellar et al., 1996).
The CSSO method allows a complex couple system to be decomposed into smaller, temporarily decoupled subsystems, each corresponding to different disciplines (subspaces). Each subspace optimization minimizes the system objective function subject to its own constraints as well as constraints contributed from the other subspaces. Each subspace optimization use its own high-fidelity analysis tools as well as given surrogate models or low-fidelity analysis tool provided by the other subspaces for analysis. Subsequently, the subspace optimizations can be performed concurrently. The system-level coordination optimization will be implemented completely based on approximation analysis tools. The subspace optimizations and the coordination optimization will be alternatively performed until results are finally decided by the coordination optimization. Therefore, the CSSO method is particularly suited to applications in a design organization where tasks are distributed among different design groups.
The CSSO method was developed initially for a single objective MDO problem. However, most MDO problems are essentially multi-objective. In recent years more work (Aute & Azarm, 2006; Huang & Bloebaum, 2004; McAllister et al., 2000; McAllister et al., 2004; Orr & Hajela, 2005; Parashar & Bloebaum, 2006; Tappeta & Renaud, 1997; Zhang et al., 2008) has focused on extending existing MDO method to handle such multi-objective MDO problems, by means of integrating a multi-objective optimization method within the MDO framework. This kind of method can be called a multi-objective MDO method.
It is an effective way to integrate multi-objective optimization method within the CSSO framework to develop the multi-objective MDO method. CSSO was extended to solve multi-objective MDO problems, including the Multi-objective Pareto CSSO (MOPCSSO) method, the Multi-objective Range CSSO (MORCSSO) method, the Multi-objective Target CSSO (MOTCSSO) method, the Multi-objective Genetic Algorithm CSSO (MOGACSSO) method and Adaptive Weighted Sum based CSSO (AWSCSSO). In MOPCSSO the Constraint method is integrated within CSSO framework (Huang & Bloebaum, 2007). In MORCSSO and MOTCSSO the concept of designer preference is introduced (Huang & Bloebaum, 2004). In MOGACSSO the Genetic Algorithm is combined with CSSO and in the hope of improving the computational efficiency (Parashar & Bloebaum, 2006). In AWSCSSO the Adaptive Weighted Sum method is introduced into CSSO (Zhang et al., 2008).
Aircraft is a complex engineering system in which multiple disciplines (such as aerodynamics, structure, thrust, noise, electronics, cost, et al.) are included. Actually different disciplines are not independent of each other. For example, deformation of a wing structure affects the aerodynamic lift distribution on the wing and in turn a new deformation is caused, which is the well-known aeroelastic problem. The designers of each discipline cannot work without consideration of other disciplines, which makes the aircraft system design become complicated. Especially in the aircraft preliminary design, the specialists of different disciplines have to often work together and discuss with each other to decide many design variables so that higher performance can be achieved. Aircraft system design is a typical multidisciplinary design problem.
In recent years, industry has paid more attention to improving efficiency in the design of complex systems, such as aircraft. MDO has emerged as an engineering discipline that focuses on the development of new design and optimization strategies for the complex systems. MDO researchers strive to reduce the time and cost associated with the coupling interaction among several disciplines. “Decomposition approaches provide many advantages for the solution of complex MDO problems, as they enable a partitioning of a large coupled problem into smaller, more manageable sub-problems. The resulting computational benefits, besides the obvious one associated with the solution of smaller problems, include creating a potential distributed processing environment. The primary benefit, however, pertains to the savings in personal hours, because groups are no longer required to wait around for other groups in the process to complete their design tasks.”(Huang & Bloebaum, 2007)
Mathematically for minimization problems the general form for MDO can be represented as follows:
Where
Global Sensitivity Equation based CSSO and Response Surface based CSSO will be discussed in this chapter as the typical CSSO methods.
GSECSSO is a bi-level optimization method. As an example, the GSECSSO method for a problem with subsystem 1 and 2 (Eq. (1): one objective and two coupled subsystems) is stated in following paragraphs.
The mathematical models of subspace optimizations of GSECSSO can be written as
Where Xi, Yi and Ci are design variable vector, state variable vector and cumulative constraint of subspace i, respectively.
By means of Kresselmeier-Steinhauser (KS) function (Kreisselmeier & Steinhauser, 1979), the cumulative constraint, Ci, which represents the constraints of subsystem i, is expressed as
Where
Global Sensitivity Equation (GSE) (Sobieszczanski-Sobieski, 1990) is a method for computing sensitivity derivatives of state (output) variables with respect to independent (input) variables for complex, internally coupled systems, while avoiding the cost and inaccuracy of finite differencing performed on the entire system analysis. By using GSE expressed below, the global sensitivities are calculated, by
The responsibility coefficients divide the responsibility of satisfying constraints among all participating subspaces.
The mathematical model of coordination optimization of GSECSSO can be written as
Where
Flowchart of GSECSSO method
The flowchart of GSECSSO is shown in Fig. 1. The original problem is decomposed and the mathematical models of subspace optimizations and coordination optimization are established as Eq. (2) and Eq. (5). Based on the initial design X0, the system analysis is performed and the GSE is used to find the system derivatives. At the first iteration, the design variables are allocated to the appropriate subspaces, as well as the responsibility, trade-off and switch coefficients, r, t, s, are initialized. Subsequently, each subspace optimization minimizes the system objective function subject to its own constraints as well as constraints contributed from the other subspaces. The subspace optimizations are performed concurrently with respect to a disjoint subset of design variables. The updated design vector is the simple combination of local optimal design sub-vectors. After that the global sensitivities and optimum sensitivities are updated. Then the system-level coordination optimization is implemented to optimize coordination parameters.
In GSECSSO, the subspace optimizations are implemented concurrently with respect to a disjoint subset of design variables, which substantially reduces the complexity of the optimization problem within each disciplinary group. The updated design vector is the simple combination of local optimal design sub-vectors. This provides designers with a significant potential benefit in terms of computational effort. However, sometimes the convergence of GSECSSO is oscillatory and premature. It is due to that the trade-off coefficients and the KS parameter
The variant of GSECSSO (Huang & Bloebaum, 2004) is much more efficient than the original GSECSSO. It is adopted in multiple multi-objective CSSO methods (Huang & Bloebaum, 2004; Parashar & Bloebaum, 2006; Zhang et al., 2008).
In the variant of GSECSSO, several modifications are made: (1) the trade-off coefficients are abandoned since all trade-offs will occur directly with minimization of the objective functions within each subspace; (2) The KS parameter,
Taking the MDO problem in Eq. (1) (one objective, two coupled subsystems, i.e. N=2) as an example, the mathematical models of subspace optimizations for the modified CSSO can be written as
Where Xi, Yi and Ci are design variable vector, state variable vector and cumulative constraint of subspace i, respectively.
the flowchart of modified CSSO method is shown in Fig. 2. In the first stage, based on the initial design variables the system analysis is implemented. If the initial scenario does not satisfy the constraints, a minimization will be required to reduce initial infeasibility of the constraints as much as possible. In the subsequent stage, all the sensitivities are computed by using GSE. Based on the sensitivity information, the impacts of design variables upon each subspace can be analyzed and the design variables will be allocated to the subspace of the greatest impact. In the third stage, the subspace optimizations are performed concurrently. There is no system optimization in this method. The updated design vector is the simple combination of the local optimal design sub-vectors.
Flowchart of the modified CSSO method
The variant of GSECSSO behaves better than GSECSSO on convergence performance. Nevertheless, how to set the start value and increase step of the KS parameter is still hard to say. These two values will affect convergence to some extent and need to be further investigated. The constraint minimization will bring extra computation cost. Furthermore, the linear approximation may sometimes cause oscillatory and premature convergence.
The RSCSSO method performs optimization in bi-level framework. Taking the MDO problem in Eq. (1) (one objective, two coupled subsystems, i.e. N=2) as an example, the mathematical models of subspace optimizations for RSCSSO can be written as
Where Xi and Yi are design variable vector and state variable vector of subspace i, respectively. In subspace i, Yi is calculated using the high-fidelity analysis tool while the other state variables with a symbol ‘^’ above are approximated by a quadratic response surface. Actually, other surrogate modeling techniques, such as kriging model or radial basis functions, can be used for approximation as well.
The mathematical model of the coordination optimization of RSCSSO can be written as
In the system-level optimization, all the state variables are approximately calculated and the design variables of all subspaces are optimized.
The flowchart of RSCSSO is shown in Fig. 3. Firstly, the original problem is decomposed into subspace optimizations in Eq. (7) and coordination optimization in Eq. (8). Then couples of sample points of the design variable vector are generated via Design of Experiment (DOE) method, such as the orthogonal experiment design. The system analysis will be performed based on these data and the results will be stored in the database.
Flowchart of RSCSSO method
Subsequently these data will be used to generate the response surface models for the state variables. After that the subspace optimizations are performed simultaneously, the results of which will be analyzed and augmented into the database for updating the response surface models, as flow (1) in Fig. 3. Then all of the design variables will be optimized in the coordination optimization and the result will be used to update response surface models as well, as flow (2) in Fig. 3. The subspace optimizations and the coordination optimization are alternately performed until stable convergence is achieved.
In the RSCSSO method, the subspace optimizations are to generate sufficient design information for approximation. After the concurrent subspace optimizations, a global approximation problem is formulated about the current design vector using information stored in the design database. It is the coordination procedure of the global approximation that drives constraint satisfaction and the overall system optimization. A fast and robust convergence appears in the RSCSSO method. However, with an increase in design variables the sample points needed for creating response surface models will be greatly increased. Furthermore, augmenting optimal points is not so reasonable for improving response surface models. As the final result is completely decided by the coordination optimization, the subspace optimizations have little impact on the results and can be abandoned.
The mathematical model for an analytical MDO example considering two coupling disciplines is of the form
Where
Coupling relationship between discipline 1 and discipline 2 for a numerical example
The comparison of the results obtained by different methods is listed in Table 1. Sequential Quadratic Programming (SQP) method is taken as a reference for the convenience of this comparative study. From Table 1, several conclusions can be drawn as follows: (1) the system-level analysis can be remarkably reduced when using different CSSO methods, which shows that the CSSO methods are well suited for MDO problems; (2) the variant of GSECSSO method outperforms the RSCSSO method, in view of the optimization results as well as the required number of system- and disciplinary-level analysis.
variables | SQP | Variant of GSECSSO | RSCSSO |
8.002860 | 8.002943 | 8.031944 | |
3.028427 | 3.028442 | 2.993963 | |
0.000000 | 0.000000 | 0.192359 | |
0.000000 | 0.000000 | 0.000000 | |
Number of system analysis | 122 | 7 | 38 |
Number of discipline 1 analysis | 0 | 72 | 179 |
Number of discipline 2 analysis | 0 | 64 | 144 |
Comparison of different CSSO methods with direct SQP for an analytical optimization problem
This example is taken from the reference (Azarm & Li, 1989). The object is to minimize the overall weight, subject to the constraints for bending and torque stresses. The mathematical model for the optimization problem is as following:
The optimization problem is classified into two disciplines: bearing discipline and shaft discipline, without any coupling relationship.
The optimization problem is solved by SQP and different CSSO methods. The results are depicted in Table 2. The convergence histories are shown in Fig. 5. From this comparative study, some conclusions can be drawn as follows: 1) Both GSECSSO and the variant of GSECSSO enable the reduction of system analysis, which in turn improve the efficiency; 2) No reduction of the system analysis is achieved by using RSCSSO. Nevertheless, the optimization procedure as well as the data-flow interface is much simpler, and the optimization can be potentially improved by implementing parallel system analysis for sample points and parallel subspace optimization; 3) The variant of GSECSSO method outperforms the GSECSSO method.
Variables | SQP | GSECSSO | Variant of GSECSSO | RSCSSO |
2994.341316 | 2995.606759 | 2995.607422 | 2994.193811 | |
3.5 | 3.500000 | 3.500000 | 3.500021 | |
0.7 | 0.7 | 0.7 | 0.700000 | |
17 | 17 | 17 | 17.000000 | |
7.3 | 7.300000 | 7.3 | 7.300000 | |
7.715320 | 7.733301 | 7.733330 | 7.715316 | |
3.350215 | 3.352131 | 3.352156 | 3.349631 | |
5.286654 | 5.287256 | 5.287246 | 5.286643 | |
Number of system analysis | 40 | 15 | 11 | 51 |
Number of bearing-discipline analysis | 0 | 326 | 188 | 280 |
Number of shaft-discipline analysis | 0 | 2986 | 1818 | 288 |
Comparison of different CSSO methods with direct SQP for gear-box optimization
Convergence history of CSSO optimization for gear reducer
The MOPCSSO, MORCSSO and AWSCSSO methods will be discussed in this chapter as the typical multi-objective CSSO methods.
The constraint method is an effective multi-objective optimization method which optimizes preferred objective with others treated as constraints. MOPCSSO is developed by introducing the constraint method into the variant of GSECSSO.
Taking the MDO problem in Eq. (1) (two objectives, two coupled subsystems, i.e. N=2) as an example, the mathematical models of subspace optimizations of MOPCSSO can be written as
The variant of GSECSSO method described in Sub-section 3.2 is adopted in the single objective optimization for each objective function. The flowchart of MOPCSSO is same as that in Fig. 2. During the system optimization, all objective functions are improved simultaneously or individual objective function are improved without worsening the others. The optimization continues until no further improvement can be made so that the Pareto optimum can be obtained finally.
The goal programming method is one of the most popular multi-objective optimization techniques that consider designer’s preference. MORCSSO and Multi-objective Target CSSO (MOTCSSO) are both developed by combining idea of the goal programming method with MOPCSSO.
The
If the design point does not meet the preference at beginning, the first-stage optimizations are implemented to obtain a design in the preferred range from the starting point.
The mathematical models of subspace optimizations of MORCSSO can be written as
In the second stage the design point is optimized closer to the Pareto frontier gradually within the preferred range.
After the optimization in the first stage, the design is in the designer’s preferred objective range. Then the mathematical models of subspace optimizations of MORCSSO is same as those of MOPCSSO in Eq. (11).
In the course of optimization in the second stage, the design point maybe flees out of the preferred objective range again. In such a case, the optimization below should be performed.
Framework of MORCSSO method
For the MORCSSO method, in the case of
Why does the optimization fail in this case? It can be analyzed from Eq. (15) in the objective space shown in Fig. 7. For Eq. (15), (1)
The analysis of Eq. (15) in bi-objective space
The analysis of Eq. (15) in three-objective space
How to solve this problem? From Fig. 7, we only need to move the line
The procedure of solving the Pareto front by AWSCSSO is similar to Adaptive Weighted Sum (AWS) method (Kim & de Weck, 2004, 2005). As an example, the AWSCSSO method for a generic bi-objective problem, with subsystem 1 and 2, is stated in the following paragraphs (Eq. (1): two objectives and two coupled subsystems). In the same way AWSCSSO can be also applied for the multi-objective problem with three or more subsystems.
In the first stage a rough profile of the Pareto front is determined.
The variant of CSSO method described in Sub-section 3.2 is adopted in the single objective optimization for each objective function and objective function is normalized as following:
When Xi* is the optimal solution vector for the single objective optimization of the ith objective function Ji, the utopia point and pseudo nadir point are defined as
Where
Then with a large step size of the weighting factor the usual weighted sum method is used in the variant of GSECSSO to approximate the Pareto front quickly. The subspace optimization for AWSCSSO can be expressed as
Where the value with symbol ‘^’ above is a linearly approximated one, C1 and C2 are cumulative constraints of G1 and G2, respectively, and
By estimating the size of each Pareto patch, the refined regions in the objective space are determined. An example of the mesh refinement in AWSCSSO is shown in Fig. 9. Where hollow points represent the newly refined node PE (expected solution) while solid points represent initial four nodes that define the patch. As shown in Fig. 9, the quadrilateral patch is taken as an example. If the line segment that connects two neighboring nodes of the patch is too long, it is divided into only two equal line segments. The central point becomes the
Refine patches of AWSCSSO method
new refined node. These refined nodes are connected to form a refined mesh. Then the sub-optimizations in Eq. (19) are performed using different additional constraints for different refined nodes and the new Pareto points are obtained. In next step, according to the prescribed density of Pareto points, the Pareto-front patch that is too large will be refined again in the same way. In subsequent steps, the refinement and sub-optimizations are repeated until the number of Pareto points does not increase anymore.
In the subsequent stage only these regions are specified as feasible domains for sub-optimization problem with additional constraints. Each Pareto front patch is then refined by imposing additional equality constraints that connect the pseudo nadir point (PN) and the expected Pareto optimal solutions (PE) on a piecewise planar surface in the objective space (as shown in Fig. 10).
AWSCSSO method for multidimensional problems
Sub-optimizations are defined by imposing additional constraint H to Eq. (19) as
The additional inequality constraint is
Where L is the adaptive relax factor that is less than 1.
Fig. 11 shows the framework of AWSCSSO. In Fig. 11, W1i and W2i are weighting factors in stage 1 and stage 2, respectively; H is the additional constraint. The optimization problem is performed in two stages in the AWSCSSO method. In the first stage the Pareto front is approximated quickly with large step size of weight factors. The optimization problems of this stage are defined in Eq. (19). In the subsequent stage, by calculating the distances between neighboring solutions on the front in objective space, the refined regions are identified and the refined mesh is formulated. Only these regions then become the feasible regions for optimization by imposing additional constraints in the objective space. The optimization problems of this stage are defined in Eq. (20). The different locations of new Pareto points are defined by the different additional constraints. Optimization is performed in each of the regions and the new solution set is acquired. Being a MDO problem, the optimization is performed by the variant of GSECSSO method.
Framework of AWSCSSO method
This problem is taken from a test problem (Huang, 2003). This is a test problem available in the NASA Langley Research Center MDO Test Suite. It has two objectives, F1 and F2, to be minimized. It consists of ten inequality constraints, four coupled state variables and ten design variables in two coupled subsystems. The mathematical model is not listed here for concision. We refer the readers to the test problem 1 in the corresponding references.
The comparison of the solution obtained by MOPCSSO and AWSCSSO is shown in Fig. 12. It can be concluded that for the problem with convex Pareto front a uniformly-spaced wide-distributed and smooth Pareto front can be obtained by AWSCSS method. When using MOPCSSO I have not captured the whole range.
Comparison of Pareto front obtained by using AWSCSSO and MOPCSSO
This problem consists of two objective functions, six design variables and six constraints. Two objectives, F1 and F2 need to be minimized. The model problem is defined as
Comparison of Pareto fronts obtained by using AWSCSSO and MOPCSSO methods
The comparison of Pareto front obtained by AWSCSSO and MOPCSSO is shown in Fig. 13. It is concluded that, for the problem with non-convex Pareto front, the more uniformly-spaced, more widely-distributed and smoother Pareto front is also obtained by the AWSCSSO method.
The mathematical model of this problem is defined as
The objective functions in Eq. (23) are to maximize useful load fraction (U) and lift-to-drag ratio for the cruising condition (L/DC). The constraints in Eq.(23) are as follows. (1) The drag coefficient for the take-off condition and landing condition (Cd0L) is no more than 0.2 and that for the cruising condition (Cd0C) is no more than 0.02. (2) The overall fuel balance coefficient (Rf) is no less than 1. (3) The achievable climb gradient for the take-off condition (qTo) is greater than 0.027 and that for the landing condition (qL) is greater than 0.024. (4) The take-off field length (DTo) is less than 1981m and the landing field length (DL) is less than 1371m. The overall fuel balance coefficient is defined as the ratio of the fuel weight required for mission to that available for mission. The design variables are listed in Table 3.
Design Variable ∕ unit | Symbol | Lower limit | Upper limit |
Wing area ∕ m2 | S | 111.48 | 232.26 |
Aspect ratio | AR | 9.5 | 10.5 |
Design gross weight ∕ 103kg | Wdg | 63.504 | 113.400 |
Installed thrust ∕ 103kg | Ti | 12.587 | 24.948 |
List of design variables
Two disciplines, aerodynamics and weight, are considered in this problem. The dataflow between and in subsystems is analyzed in Fig. 14, where L/DTo, L/DL, L/DC are the lift-to-drag ratios for the take-off, landing and cruising conditions respectively, Vbr is the cruise velocity with the longest range, Rfr is the fuel weight fraction required for mission, and Cd0C is the zero-lift drag coefficient for the cruising condition.
Two disciplines, aerodynamics and weight, are coupled. When the state variables in aerodynamics such as cruise velocity with the longest range, lift coefficients, zero-lift drag coefficients, skin-friction drag coefficients, lift-to-drag ratio are computed, some state variables in Weight such as Rfr should be known. Similarly, when the state variables in Weight such as useful load fraction, overall fuel balance coefficient, achievable climb gradient on take-off and landing, take-off field length and landing field length are computed, some state variables in Aerodynamics such as L/DTo, L/DL, L/DC and Vbr should also be provided. In the Aerodynamics discipline, Vbr is coupled with Cd0C. The dataflow between state variables and design variables can be seen in Fig. 15. Many details of equations in the aerodynamic discipline model and weight discipline model can refer to the reference (Zhang et al., 2008). The full description of them can be found in the references (Lewis & Mistree, 1995; Lewis, 1997).
Dataflow between and in subspaces
Dataflow between state variables and design variables
Pareto front obtained by using AWSCSSO
The Pareto front obtained using AWSCSSO is shown in Fig. 16. Each solution on Pareto front is obtained using CSSO with iterative subspace optimizations. Taking one of the optimal designs as example, the values of the design variables are: S=232.3m2, AR=10.5, Wdg=113.4×103kg, Ti=16.75×103kg. The performance parameters of the aircraft in optimal design are as follows: Cd0C=0.01777, L/DC=21.05, Vbr=183.43m/s, qTo=0.03303, qL=0.08804, DTo=1823m and DL=1086m. Several conclusions can be made from these results. 1) The AWSCSSO method is primarily proved to be applicable for aircraft conceptual design. 2) The distribution of Pareto points is not so uniform as expected. These results are still very encouraging in general. The non-uniformity may be due to the additional constraint that changes the location to expected solution. Further study is still needed on how to achieve the balance between uniformity and convergence.
The CSSO method is one of the main bi-level MDO methods. Couples of CSSO methods for single- and multi-objective MDO problems are discussed in this chapter. It can be concluded that, (1) number of the system analysis can be greatly reduced by using the CSSO methods, which in turn improve the efficiency; (2) the CSSO methods enable concurrent design and optimization of different design groups, which can greatly improve efficiency; (3) the CSSO methods are effective and applicable in solving not only single-objective but also multi-objective MDO problems.
For the CSSO methods, although the RSCSSO method behaves more robust, it will actually reduce to a single-level surrogate modeling based MDO method since the subspace optimizations have little impact on the results. So the GSECSSO method is more promising as a bi-level method and worth further studying. The future study on the GSECSSO method will focus on improving its robustness and efficiency. For the multi-objective CSSO methods, the AWSCSSO method behaves better on obtaining widely-distributed Pareto points. The future work on the multi-objective CSSO methods will focus on improving the solution quality and also on testing it for more realistic engineering design problems.
Various forms of viral hepatitis represent a world health concern and challenge, generating a considerable socio-economic burden. Of these, hepatitis A as a type of food-borne hepatitis is mainly endemic in developing regions with the condition of inadequate sanitation and hygiene, such as in parts of Africa, Asia, Eastern Europe, South America, and Middle East [1, 2]. With the improvement of public health, the incidence of HAV infections in China have been gradually reduced (published data from 1990 to 2017) [3]. Up to now, 1.5 million cases of hepatitis A virus (HAV) infections are reported worldwide [2], which indicated that hepatitis A remains a primary problem in hygiene and public health. Hepatitis A has a very similar clinical symptom compared to hepatitis E. Except for the severer pathological injuries of hepatitis E than that of hepatitis A, both of two are self-limiting diseases, do not lead to liver cirrhosis and liver cancer, and transmit via orofecal route and person-to-person contact [4]. Consequently, HAV-contaminated water, vegetables, fruits, blood products, and other foodstuffs, especially undercooked shellfish including clams, oysters, and mussels (Figure 1) [5, 6], are the major pathways of infections with hepatitis A [7, 8]. Under certain circumstances, intravenous drug users with the collective use of syringes are at risk categories for HAV infections [9], and also there exist vertical transmissions of HAV from mother to child but occur very rarely (Figure 1) [10]. HAV as the main pathogen causing acute viral hepatitis is classified as a sole member of the genus Hepatovirus of the family Picornaviridae, which includes many medical and veterinary pathogens in 1991 [11, 12, 13]. HAV is a single linear positive-stranded RNA virus whose genomic full length is approximately 7500 nucleotides, which contain 5′-noncoding region (UTR), protein coding region, and 3′-noncoding region (UTR) (Figure 2) [13]. Researchers have found that HAV present in the form of naked, nonenveloped virions in feces aids to the viral transmissions through the environment. However, when HAV emerges in the blood of infected persons, the virion isolates itself from neutralizing antibodies by the way of producing quasi-envelope in host-derived membranes [14]. Epidemiological data showed that the most susceptible populations of HAV are the children in early childhood [2], and the disease prevalence exceeds 90% before the age of 10 [15], albeit most of infected youngers are usually mild or asymptomatic [16]. Hence, accelerating the immunological research and viral vaccine development can improve human immunity and reduce the spread of HAV. World Health Organization (WHO) recommends that vaccination combating HAV be integrated into the national immunization schedule for children aged ≥1 year on the consideration of many factors including cost-effectiveness [17]. What is noteworthiness is that, the illness infected with HAV in those people who are older than 60 will be very severe [18]. Moreover, HAV superinfections in chronic liver disease (CLD) sufferers (e.g., hepatitis B or C) are usually associated with raising morbidity and mortality [19, 20]. To date, animal model is one of the promising tools in the investigation of human HAV infections. Studies on HAV immunopathological mechanism and host immune response mainly used nonhuman primates such as chimpanzees and marmosets as animal models. Due to the lack of other alternative animal models that support HAV infections, the study of the HAV biology and further development of therapies for hepatitis A have been hampered. Here in this chapter, the biological features of HAV will be discussed, the animal models of hepatitis A and their characteristics will be sketched, and the merits and demerits for these models will be analyzed as well.
Diagram showing the possible transmission routes of HAV.
The genome structure, protein structure components and overall structure of HAV. Refer to [28, 35, 36].
As early as 5000 years ago, hepatitis A-like illnesses were documented in ancient China. In Europe, similar disease known as “benign epidemic jaundice” was also described during the Hippocratic era [21]. As time goes by, in 1947, McCallum et al. termed infectious hepatitis as hepatitis A [22]. In the first half of 1967, Krugman et al. found the distinctive features between infectious hepatitis (hepatitis A) and serum hepatitis (hepatitis B) in clinical, epidemiological, and immunological aspects [23]. By 1973, Feinslone et al. firstly detected hepatitis A in feces of patients using the technology of immune electron microscopy (IEM) [24]. Morphologically and structurally, the purified HAV virion, having an outer diameter of 27–80 nm, is an icosahedral nucleocapsid protein granular which contains one linear positive-stranded ribonucleic acid (RNA) genome [25]. The genome encodes a single large polyprotein of 2227 amino acids [26], which is matured and folded to produce 10 biologically active viral proteins, including four structural proteins that construct the capsid (VP4 (~2.5 kDa), VP2 (24–30 kDa), VP3 (21–28 kDa), and VP1pX) and 6 nonstructural proteins that are indispensable for replication of the RNA genome (2B, 2C, 3A, 3B [VPg], 3Cpro [a cysteine protease], and 3Dpol) (RNA-dependent RNA polymerase) (Figure 2) [27]. By using standard serological technique and molecular identification methods, HAV is identified to belong to merely one single serotype, and is divided into seven distinct genotypes of which three genotypes (I, II, and VII) that circulate in humans, one genotype (III) isolated from either humans or owl monkeys, and other three genotypes (V–VII) exist in nonhuman primates [28, 29, 30]. Genotypes I, II, and III are sub-classified into subtypes A and B (Genotypes IA, IB, IIA, IIB, IIIA, and IIIB) [31]. Molecular epidemiology has further revealed that HAV sub-genotype IA is responsible for the most circulations among human population [32]. For sub-genotype IB HAV strains, several reports have declared that they were associated with food such as frozen strawberries in Australia and several countries of Europe [33, 34]. Recent studies of X-ray analysis have uncovered that HAV possesses a primitive capsid architecture related to that of picorna-like viruses infecting insects, which imply a correlation of primeval evolution as well as a novel cellular entrance mechanism for viruses [35]. The structure information (especially the 3D microstructural study) of viral protein is now a robust tool for dissecting their biological functions. In 2018, Stuart et al. reviewed updated studies on the structural features of outer protein shell of HAV and proposed the future researching scopes including relevant structural elucidations of the enveloped particles, as well as the capture of intermediates in the state of assembly, attachment, and/or uncoating [36]. In terms of receptor binding mechanism, Wang et al. pointed out that using a receptor mimic mechanism for neutralization of infectivity may hold promise for the therapeutic intervention of hepatitis A [37]. With regard to the origin of human HAV, phylogenetic analyses show that, in the remote past, these ancient viruses have emerged in different host species, and ancestral state reconstructions indicate HAV is likely to have originated in rodents [38]. What’s more, investigations should be fundamentally focused on therapeutic interventions and new creations of HAV vaccines as a result of hepatitis A vaccine is one of the most effective strategy for the treatment of hepatitis A [39]. To date, four inactivated monovalent HAV vaccines from different manufactures (Havrix®, Epaxal®, Avaxim®, and Vaqta®) have been commercially available to the global markets [40]. Other hepatitis A vaccines such as a Chinese live attenuated vaccine (H2 strain, Zhejiang Academy of Medical Sciences, Hangzhou, PR China) and a Vietnam one are just self-sufficient in domestic production [41]. HAV infections are still an important cause of morbidity and mortality in developed countries such as the United States [42], let alone other nations with poor sanitation. Therefore, the work of scientific research for hepatitis A vaccine is still certainly on the way.
According to literatures, HAV strains of wild type are quite difficult in propagating in vitro. When culturing in cell-conditioned medium, they show low growth rate characteristically, as well as have no apparent cytopathic effects [43]. Additionally, HAV has its own special life history: it primarily replicates in the hepatic tissue, is excreted in biliary system to reach the intestinal contents [44], and is mostly shed in the feces and soil [45], where the viruses may persist for an extended period of time [46, 47]. Consequently, it is significantly important for researchers to find the proper infected models that aim at the investigation of HAV. As Hirai-Yuki and his co-authors have ever pointed out that, it is essential to develop improved animal models for the deeper investigations of the molecular and cellular mechanisms associated virus-hepatocyte interactions within the distinctive environment of liver tissue of hosts [48]. Here are the examples of disease models for HAV infections showed in Table 1.
Authors/year | Animal models | Comments | Refs |
---|---|---|---|
Dienstag et al/1975 | Chimpanzees | Provided evidence for the susceptibility of chimpanzees to HAV | [49] |
Amado et al/2010 | Cynomolgus monkeys | Cynomolgus monkey was confirmed as a suitable model to study HAV infection | [67] |
LeDuc et al/1983 | New World owl monkeys | Confirmed the susceptibility of New World owl monkey to HAV | [53] |
Song et al/2016 | Pigs | First experimental evidence to demonstrate human HAV strains can infect pigs | [76] |
Hirai-Yuki et al/2016 | Mice | Provided a new paradigm for viral pathogenesis in the liver | [83] |
Hornei et al/2001 | Guinea pigs | Useful for studying some aspects of HAV pathogenesis and for testing the safety of vaccines. | [88] |
Zhan et al/1981 | Tree shrews | HAV can replicate in tree shrews and are potential for candidate models for HAV infections | [97] |
Anthony et al/2015 | Harbor seals | Describe the discovery of an HAV-like virus in seals | [98] |
Liu et al/2019 | Pekin ducks | There are differences in the pathogenicity of different subtypes of DHAV in ducklings | [100] |
Wen et al/2019 | Ducks | Provided new insights into the genetics and pathogenesis of DHAV-3 | [101] |
Examples of disease models for HAV infections.
Broadly speaking, NHPs resemble humans in anatomy, physiology, and pathology over any other animals, which make them to be considered as the principal models for diseases including HAV infections. Human HAV has successfully infected various species of NHPs, such as chimpanzees (Pan troglodytes) [49], common marmosets (Callithrix jacchus) [50, 51], Squirrel Monkeys (Saimiri sciureus) [52], New World owl monkeys (Platyrrhines) [53], African green monkeys (Cercopithecus aethiops) [54], owl monkey (Aotus trivirgatus) [55], brown macaques (Macaca arctoides) [56], stump-tailed monkey (Macaca speciosa) [57] and tamarins, etc. (Table 1), but the host range of this virus is still narrow [58], mainly limited to relatively few species. The most common animal models used in laboratories for interrogating HAV infection are mainly marmosets and chimpanzees, which are of scarce resources (Chimpanzees are so expensive that they are not widely available for research use) in most countries. In addition, experimental data indicated that more than 90% of wild chimpanzees carried anti-HAV antibodies [59], which made them less suitable for investigating HAV-infectious diseases, but chimpanzees reared in captivity are more susceptible to infect hepatitis A. Moreover, it is very difficult for laboratory technicians to feed and operate experimentally on these two animals in many situations. And quite importantly, ethical concerns have advocated the decreasing use of chimpanzees for invasive experiments of research [60].
Take chimpanzees for example, they are the candidate experimental subjects that are most closely related to humans genetically, and most probably to be simulative and predictive of human outcomes when used as disease models. In 1962, Deinhardt et al. launched the initial attemption experiment that used chimpanzees to be infected with HAV through inoculating viral materials (serum or feces), but the gained results could not provide conclusive evidences for the transmission of infective hepatitis from humans to chimpanzees [61]. Intriguingly, in 1963, Hillis presented biochemical and histologic evidences that promisingly proved chimpanzees as useful as experimental hosts for human hepatitis viruses [62, 63]. In the mid-1970s, results of most of numerous publishment, which attempt to spread hepatitis A to chimpanzees yielded negative or equivocal results [64]. By 1984, Tsiquaye et al. carried out a study on acute hepatitis A infection occurred in hepatitis B chimpanzee carriers, which showed that superinfection can significantly alter the parameters of HBV chronicity in chimpanzees [65]. The authors pointed out that further observations were needed to establish the degree of severity of concurrent infection of HBV carriers with HAV, since such changes may have implications in some countries where the proportion of HBV carriers is high plus hepatitis A is highly prevalent [65]. For the purpose to locate where does the HAV might duplicate in the body, in 1989, Cohen and his colleagues conducted a study of single chimpanzee involvement in experiment, and found a possible oropharyngeal site for viral replication due to the emergence of HAV in saliva and throat swabs [66]. Similarly, Amado and co-authors acquired an experimental result that salivary gland was an extrahepatic site for early HAV replication in cynomolgus monkeys [67]. In the following two decades, the investigators shifted the focus of animal models to other NHPs instead of chimpanzees either because of the high cost of chimpanzee research or because of the poor contribution of chimpanzee experiments for biomedical applications [68]. Until 2011, Lanford et al. utilized three chimpanzee models to study the early innate immune responses to HAV infections. They found that HAV has a better property of keeping itself latent compared to HCV during early stage of acute resolving infection, and HAV infections represent a distinctly different paradigm in the course of intrahepatic interactions of virus-host [69].
The chimpanzees have been demonstrated to be an invaluable model tools for the investigation on HBV-induced disease pathogenesis and the discovery of novel prophylactic drugs and anti-viral therapies [70]. Optimistically, with the advancement of biotechnology, utilizing chimpanzee and other NHPs as disease models for HAV infection will surely play significant roles in HAV-associated studies in the future.
Compared with NHPs, pigs have several advantages such as easy breeding and rearing, convenient handling and fewer ethical concerns, which make them be widely used in biomedical research [71]. Under natural conditions, it had been reported that HAV infections are being restricted to humans and nonhuman primates [72], and the appropriate models used for HAV infection have been restricted to nonhuman primates [73]. Due to several limitations of such animal models, other surrogate models need to be developed for further study. According to literatures, the immune system in pigs shares many similarities with humans for over 80% of analytical parameters, which made swine as a more suitable and common animal model for humans [74, 75]. Moreover, pigs have been used preclinically as disease models for preclinical studies usually. Until 2016, Song et al. firstly found the experiment evidence to prove human HAV strains can also infect swine [76], which took the first step to approach swine models for HAV infections. In this experiment, Song and colleagues observed that HAV can survive and replicate in pigs, which have replaced NHPs. However, there were no significant changes in the clinical manifestations and serum markers for pigs infected with HAV. Finally, they further suggested that pigs might be a suitable animal model for future studies related to HAV pathogenesis [76].
Over the last two decades, mouse models have been successfully used in tackling various biological questions associated with intrahepatic immune response mechanisms for disease pathogenesis and clearing of HBV [77]. And also, such types of models can be applied to study the adaptive immune response to hepacivirus infection and will play roles in vaccine development. However, HAV is not capable to replicate in mice due to incompatibilities in the interaction of the virus and the innate immune system of mice. Therefore, scientists tackle this difficulty by utilizing chimeric mice, which facilitated the successful replication of HAV in the body through bypassing the cytosolic pattern recognition receptor, MAVS [78].
Generally speaking, certain cellular receptors are the key molecules that mediate viruses of entry into special kinds of cells in the body. Human membrane protein TIM-1 (T cell immunoglobulin and mucin domain protein-1) is a type of phosphatidylserine receptor that was firstly described as HAVCR1 [79], which helps cellular entry and infection with innumerable conventional enveloped viruses that bind phosphatidylserine on their surface [80]. And specially, TIM-3 receptor facilitates HAV for its entrance into target cells in humans [81]. However, recent research showed that TIM-1 is not an essential hepatovirus factor although its PtdSer-binding activity may contribute to the spread of quasi-enveloped virus and liver damage in mice [82]. For most of mouse models, wild-type mice are naturally resistant to HAV infection [83], and murine cell lines still exist defects in viral entry processes functionally [84]. For these reasons, multiple approaches have been developed by investigators to generate “humanize” mice at a genetic level to aid them susceptible to infection with HAV.
Previously, Yang et al. reported that, by using cell culture method, HAV ablates type 1 IFN responses thereby disrupting activation of IRF3 through the MDA5 pathway [85]. In 2013, Pang used HAV to infect SCID-beige/Alb-uPA mice with chimeric human/mouse livers for the purpose to test the susceptibility of mice to HAV. The result shows that these chimeric mice are permissive to HAV infection and represent valuable small animal models for future studies [86]. In 2016, Hirai-Yuki et al. applied the murine models with genetic defects in the induction of type I interferon (IFN) responses for HAV infection to reveal a previously undefined link between innate immune responses to virus infection and acute liver injury, which furnishes a novel paradigm for viral etiopathogenesis in the liver [83]. In 2018, a research team of Hirai-Yuki wrote a review of the study on “Murine models of hepatitis A virus infection” in which they provided an extensive and in-depth perspective into the development and application of mice models for HAV [48]. Additionally, in this chapter, it emphatically introduced the mechanism of degrading MAVS via viral proteases, in which it facilitates long-term survival of virus and spread through escaping from IFN-mediated restriction of virus replication and limiting pathogenesis and hepatic damage [48].
Till now, mouse models have been applied to support infections with HBV, HCV, and even HAV successfully. This probably has to do with building infections in the mouse liver, which is a key point in the development of viral hepatitis. Predictably, it has a promising future for utilizing mice as effective models for the investigation of HAV infection with the technological development of biomedical models.
The guinea pig models are more similar to humans than other small animal models in physiology and immunology. Specifically, the guinea pigs have the property of being analogous to humans in reproductive physiology and estrous cycle [87], etc. Guinea pigs have been used as an HAV infection model, but their use is limited because of the lack of development of anti-HAV antibodies in inoculated guinea pigs. In 2001, Hornei et al. conducted a study to determine whether HAV is capable to infect guinea pigs and whether they can be valid as a disease model for replicating HAV pathogenesis in humans and for the evaluation of vaccines [88]. The authors found that very low levels of HAV were detected in the livers of guinea pigs, which inoculated with human HAV [88]. Furthermore, they also described that the experimental guinea pigs shared similar response pattern with a New World nonhuman primate (Callithrix jacchus) after being challenged with HAV materials [88, 89]. The method of using guinea pigs to establish models for HAV infection is still under controversy. In 2010, de Castro Araujo and colleagues designed a research project to respond to the question “Whether HAV is capable to infect guinea pigs?”. However, they failed to establish a guinea pig as model for HAV [90].
Chinese tree shrew (Tupaia belangeri chinensis), mainly distributing in Yunnan and Guangxi provinces in China, is a class of small animals having a closer evolutionary relationship with humans compared to rodents [91]. Tree shrews have emerged in the vision of scientists for more than 30 years as a result of having many valuable features that are suitable in animals utilized as experimental models in biomedical studies [92], particularly in the fields of toxicology and virology [93]. To date, there are many attempts to employ tree shrews as models for human disorders such as hepatitis B [94] and hepatitis C infections [95, 96]. In 1981, Zhan et al. used fecal suspension of hepatitis A patients (concentration: 5%) to infect nine tree shrews through oral route, and eventually no apparent clinical symptoms of acute hepatitis were found. About 7–13 days after the viral infections, seven tree shrews were detected HAV that lies in their stools in 12–27 days, which indicated that HAV could reproduce in the body of tree shrews. The experimental results indicated that HAV can replicate in tree shrews and are potential for candidate models for HAV infections [97]. Additionally, they also found disease symptoms including increased alanine transaminase (ALT), hepatic hyperemia, hepatic edema, steatosis, and hyperplasia of Kupffer cells in the infected tree shrews, which further manifested that HAV can propagate in tree shrews [97].
Early studies suggested that HAV was unable to lead to infections of any common small laboratory animals successfully except NHPs. However, this “prejudice” has already been challenged and overturned by animal model engineering as well as by new scientific discoveries. In 2015, several strains of human HAV have been found in seals, which may indicate that the first natural nonprimate HAV to be discovered, and provide further understanding for the evolutionary history and pathogenicity of HAV [98]. Moreover, in recent years, HAV-associated hepatoviruses have been found in bats, rodents, hedgehogs [38], duck [99, 100, 101], and woodchucks [102, 103], which suggested that there may be more candidate animals potentially used as animal models of HAV. On the contrary, some scholars believed that these new viruses are substantially more divergent from each other and from human HAV (including simian HAV), which is in accordance with them being assigned to several additional species in taxonomy [78].
The animal experiments definitely play an important role in the development of life sciences and medical sciences. Therefore, ethical analysis concerning animal experiments is essential because it cannot completely avoid the use of animals [104] in the process of biomedicine and preclinical medicine research. Specially, NHPs act as the particularly valuable models for testing interventions against the Ebola and Marburg viruses in the field of studying of current infectious diseases, which can effectively objectively simulate human diseases via infections in these animals [105], and further contribute to the development of new protective and therapeutic vaccines. At a certain level, ethical issues become more important than scientific interest for this type of animal test [104] because such infections are often lethal to the experimental animals, which are commonly viewed as unethical. Similarly, experiments with HAV infection also expose animals (mainly NHPs) to injury or disease. Consequently, how to balance the contradiction between ethical challenges and NHPs infectious experiments becomes a vitally important subject.
Animal model research is entering a new and exciting stage along with the technologies of computational information and molecular biochemistry. For example, it is now possible for us to employ genome-edited techniques (e.g., ZFNs, TALENs, and CRISPR/Cas) to knockout specific genes, to knock in new genes, or to introduce specific mutations, and then to produce valuable animal models that benefited for our investigations. However, as we know, “no model is perfect, but many are useful” [106]. Therefore, establishing susceptible animal models is one of the methods in the research fields of HAV. By using appropriate and reliable animal models, virologist can perform a series of studies associated with hepatitis A including epidemiologic features, viral infectivity, humoral and cellular immunity, cytokine responses, virus pathogenesis, as well as the research and development of antiviral vaccinations.
For the development of hepatitis A vaccines, it is worth mentioning that a highly effective vaccine against HAV was manufactured by classical inactivation of the whole virus generated from cell culture [107], which commendably avoids the ethical controversy of using NHPs models. Moreover, there is a need to provide more support for the studies of long-term protection vaccines against hepatitis A infection [108].
Last but not least, it is very likely that a much wider host range of HAV-associated viruses will be discovered in other mammalian species in the future [38].
The Nobel laureate Peter Medawar have ever succinctly concluded that “No virus is known to do good” [109]. However, as we all know, “viruses are not omnipotent.” For hepatitis viruses, the narrow hepatic tissue tropism maybe is the cause of constraining the host ranges of hepatitis viruses to relatively few special host species. As previously reported, only one serotype of HAV had been found globally [110]. However, according to Bosch et al., there exists the possibility of emergence of a novel serotype originated from zoonotic reservoirs [18]. In summary, it is necessary to further develop candidate animal models for hepatitis A infection although HAV is easily capable of adapting growth in the condition of conventional mammalian cell cultures [92].
In recent decades, HAV has been ignored by viral research circles to a certain extent due to the research spending and interest have shifted to other hepatotropic pathogens. Finally, animal model research, as a preclinical study aiming to hepatitis A, can offer a scientific platform to accelerate the pace for drugs screening and vaccines development.
We would like to express our sincere thanks to Professor Costin Teodor Streba, Professor Cristin Constantin Vere and Professor Ion Rogoveanu for initiating this book project.
No conflicts of interest were reported.
This work was supported in part by grants from the National Key R&D Program Projects (2017YFF0104904), National Key Research and Development Plan (2016YFD0500600), Guangdong Provincial Science and Technology Plan Project (2017B020207004), Maternal and Child Health Molecular Genetic Medicine Research Project of Maternal and Child Health Center in Chinese CDCP (FY-ZX-ZD-0285, MHBD-0833-001), Youth Innovation Project of Sichuan Medical Research (Q16036), Key Science & Technology Project of Leshan in 2018 (18SZD150).
alanine transaminase chronic liver disease duck hepatitis A virus hepatitis A virus hepatitis B virus hepatitis C virus hepatitis D virus hepatitis E virus hepatitis G virus immune electron microscopy type I interferon internal ribosomal entry site nonhuman primates transcription activator-like effector nucleases World Health Organization zinc finger nucleases
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