Quantum software development utilizes quantum phenomena such as superposition and entanglement to address problems that are challenging for classical systems.However,it must also adhere to critical quantum constraints...Quantum software development utilizes quantum phenomena such as superposition and entanglement to address problems that are challenging for classical systems.However,it must also adhere to critical quantum constraints,notably the no-cloning theorem,which prohibits the exact duplication of unknown quantum states and has profound implications for cryptography,secure communication,and error correction.While existing quantum circuit representations implicitly honor such constraints,they lack formal mechanisms for early-stage verification in software design.Addressing this constraint at the design phase is essential to ensure the correctness and reliability of quantum software.This paper presents a formal metamodeling framework using UML-style notation and and Object Constraint Language(OCL)to systematically capture and enforce the no-cloning theorem within quantum software models.The proposed metamodel formalizes key quantum concepts—such as entanglement and teleportation—and encodes enforceable invariants that reflect core quantum mechanical laws.The framework’s effectiveness is validated by analyzing two critical edge cases—conditional copying with CNOT gates and quantum teleportation—through instance model evaluations.These cases demonstrate that the metamodel can capture nuanced scenarios that are often mistaken as violations of the no-cloning theorem but are proven compliant under formal analysis.Thus,these serve as constructive validations that demonstrate the metamodel’s expressiveness and correctness in representing operations that may appear to challenge the no-cloning theorem but,upon rigorous analysis,are shown to comply with it.The approach supports early detection of conceptual design errors,promoting correctness prior to implementation.The framework’s extensibility is also demonstrated by modeling projective measurement,further reinforcing its applicability to broader quantum software engineering tasks.By integrating the rigor of metamodeling with fundamental quantum mechanical principles,this work provides a structured,model-driven approach that enables traditional software engineers to address quantum computing challenges.It offers practical insights into embedding quantum correctness at the modeling level and advances the development of reliable,error-resilient quantum software systems.展开更多
Domain-specific metamodeling language(DSMML) defined by informal method cannot strictly represent its structural semantics,so its properties such as consistency cannot be holistically and systematically verified.In re...Domain-specific metamodeling language(DSMML) defined by informal method cannot strictly represent its structural semantics,so its properties such as consistency cannot be holistically and systematically verified.In response,the paper proposes a formal representation of the structural semantics of DSMML named extensible markup language(XML) based metamodeling language(XMML) and its metamodels consistency verification method.Firstly,we describe our approach of formalization,based on this,the method of consistency verification of XMML and its metamodels based on first-order logical inference is presented;then,the formalization automatic mapping engine for metamodels is designed to show the feasibility of our formal method.展开更多
Constructing metamodel with global high-fidelity in design space is significant in engineering design. In this paper, a double-stage metamodel （DSM） which integrates advantages of both interpolation metamodel and re...Constructing metamodel with global high-fidelity in design space is significant in engineering design. In this paper, a double-stage metamodel （DSM） which integrates advantages of both interpolation metamodel and regression metamodel is constructed. It takes regression model as the first stage to fit overall distribution of the original model, and then interpolation model of regression model approximation error is used as the second stage to improve accuracy. Under the same conditions and with the same samples, DSM expresses higher fidelity and represents physical characteristics of original model better. Besides, in order to validate DSM characteristics, three examples including Ackley function, airfoil aerodynamic analysis and wing aerodynamic analysis are investigated, In the end, airfoil and wing aerodynamic design optimizations using genetic algorithm are presented to verify the engineering applicability of DSM.展开更多
Metamodeling techniques have been used in robust optimization to reduce the high computational cost of the uncertainty analysis and improve the performance of robust optimization problems with computationally expensiv...Metamodeling techniques have been used in robust optimization to reduce the high computational cost of the uncertainty analysis and improve the performance of robust optimization problems with computationally expensive simulation models. Existing metamodels main focus on polynomial regression（PR）, neural networks（NN） and Kriging models, these metamodels are not well suited for large-scale robust optimization problems with small size training sets and high nonlinearity. To address the problem, a reduced approximation model technique based on support vector regression（SVR） is introduced in order to improve the accuracy of metamodels. A robust optimization method based on SVR is presented for problems that involve high dimension and nonlinear. First appropriate design parameter samples are selected by experimental design theories, then the response samples are obtained from the simulations such as finite element analysis, the SVR metamodel is constructed and treated as the mean and the variance of the objective performance functions. Combining other constraints, the robust optimization model is formed which can be solved by genetic algorithm （GA）. The applicability of the method developed is demonstrated using a case of two-bar structure system study. The performances of SVR were compared with those of PR, Kriging and back-propagation neural networks（BPNN）, the comparison results show that the prediction accuracy of the SVR metamodel was higher than those of other metamodels under uncertainty. The robust optimization solutions are near to the real result, and the proposed method is found to be accurate and efficient for robust optimization. This reaserch provides an efficient method for robust optimization problems with complex structure.展开更多
Virtual product development (VPD) is essentially based on simulation. Due tocomputational inefficiency, traditional engineering simulation software and optimization methods areinadequate to analyze optimization proble...Virtual product development (VPD) is essentially based on simulation. Due tocomputational inefficiency, traditional engineering simulation software and optimization methods areinadequate to analyze optimization problems in VPD. Optimization method based on simulationmetamodel for virtual product development is proposed to satisfy the needs of complex optimaldesigns driven by VPD. This method extends the current design of experiments (DOE) by variousmetamodeling technologies. Simulation metamodels are built to approximate detailed simulation codes,so as to provide link between optimization and simulation, or serve as a bridge for simulationsoftware integration among different domains. An example of optimal design for composite materialstructure is used to demonstrate the newly introduced method.展开更多
High fidelity analysis are utilized in modern engineering design optimization problems which involve expensive black-box models.For computation-intensive engineering design problems,efficient global optimization metho...High fidelity analysis are utilized in modern engineering design optimization problems which involve expensive black-box models.For computation-intensive engineering design problems,efficient global optimization methods must be developed to relieve the computational burden.A new metamodel-based global optimization method using fuzzy clustering for design space reduction(MGO-FCR) is presented.The uniformly distributed initial sample points are generated by Latin hypercube design to construct the radial basis function metamodel,whose accuracy is improved with increasing number of sample points gradually.Fuzzy c-mean method and Gath-Geva clustering method are applied to divide the design space into several small interesting cluster spaces for low and high dimensional problems respectively.Modeling efficiency and accuracy are directly related to the design space,so unconcerned spaces are eliminated by the proposed reduction principle and two pseudo reduction algorithms.The reduction principle is developed to determine whether the current design space should be reduced and which space is eliminated.The first pseudo reduction algorithm improves the speed of clustering,while the second pseudo reduction algorithm ensures the design space to be reduced.Through several numerical benchmark functions,comparative studies with adaptive response surface method,approximated unimodal region elimination method and mode-pursuing sampling are carried out.The optimization results reveal that this method captures the real global optimum for all the numerical benchmark functions.And the number of function evaluations show that the efficiency of this method is favorable especially for high dimensional problems.Based on this global design optimization method,a design optimization of a lifting surface in high speed flow is carried out and this method saves about 10 h compared with genetic algorithms.This method possesses favorable performance on efficiency,robustness and capability of global convergence and gives a new optimization strategy for engineering design optimization problems involving expensive black box models.展开更多
Aiming to reduce the high expense of 3-Dimensional(3D)aerodynamics numerical sim-ulations and overcome the limitations of the traditional parametric learning methods,a point cloud deep learning non-parametric metamode...Aiming to reduce the high expense of 3-Dimensional(3D)aerodynamics numerical sim-ulations and overcome the limitations of the traditional parametric learning methods,a point cloud deep learning non-parametric metamodel method is proposed in this paper.The 3D geometric data,corresponding to the object boundaries,are chosen as point clouds and a deep learning neural net-work metamodel fed by the point clouds is further established based on the PointNet architecture.This network can learn an end-to-end mapping between spatial positions of the object surface and CFD numerical quantities.With the proposed aerodynamic metamodel approach,the point clouds are constructed by collecting the coordinates of grid vertices on the object surface in a CFD domain,which can maintain the boundary smoothness and allow the network to detect small changes between geometries.Moreover,the point clouds are easily accessible from 3D sensors.The point cloud deep learning neural network,which employs re-sampling technique,the spatial transformer network and the fully connected layer,is developed to predict the aerodynamic char-acteristics of 3D geometry.The effectiveness of the proposed metamodel method is further verified by aerodynamic prediction and robust shape optimization of the ONERA M6 wing.The results show that the proposed method can achieve more satisfactory agreement with the experimental measurements compared to the parametric-learning-based deep neural network.展开更多
Modern engineering design optimization often relies on computer simulations to evaluate candidate designs, a setup which results in expensive black-box optimization problems. Such problems introduce unique challenges,...Modern engineering design optimization often relies on computer simulations to evaluate candidate designs, a setup which results in expensive black-box optimization problems. Such problems introduce unique challenges, which has motivated the application of metamodel-assisted computational intelligence algorithms to solve them. Such algorithms combine a computational intelligence optimizer which employs a population of candidate solutions, with a metamodel which is a computationally cheaper approximation of the expensive computer simulation. However, although a variety of metamodels and optimizers have been proposed, the optimal types to employ are problem dependant. Therefore, a priori prescribing the type of metamodel and optimizer to be used may degrade its effectiveness. Leveraging on this issue, this study proposes a new computational intelligence algorithm which autonomously adapts the type of the metamodel and optimizer during the search by selecting the most suitable types out of a family of candidates at each stage. Performance analysis using a set of test functions demonstrates the effectiveness of the proposed algorithm, and highlights the merit of the proposed adaptation approach.展开更多
Combining a trust region method with a biased sampling method,a novel optimization strategy(TRBSKRG)based on a dynamic metamodel is proposed.Initial sampling points are selected by a maximin Latin hypercube design met...Combining a trust region method with a biased sampling method,a novel optimization strategy(TRBSKRG)based on a dynamic metamodel is proposed.Initial sampling points are selected by a maximin Latin hypercube design method,and the metamodel is constructed with Kriging functions.The global optimization algorithm is employed to perform the biased sampling by searching the maximum expectation improvement point or the minimum of surrogate prediction point within the trust region.And the trust region is updated according to the current known information.The iteration continues until the potential global solution of the true optimization problem satisfied the convergence conditions.Compared with the trust region method and the biased sampling method,the proposed optimization strategy can obtain the global optimal solution to the test case,in which improvements in computation efficiency are also shown.When applied to an aerodynamic design optimization problem,the aerodynamic performance of tandem UAV is improved while meeting the constraints,which verifies its engineering application.展开更多
The robust design optimization(RDO)is an effective method to improve product performance with uncertainty factors.The robust optimal solution should be not only satisfied the probabilistic constraints but also less se...The robust design optimization(RDO)is an effective method to improve product performance with uncertainty factors.The robust optimal solution should be not only satisfied the probabilistic constraints but also less sensitive to the variation of design variables.There are some important issues in RDO,such as how to judge robustness,deal with multi-objective problem and black-box situation.In this paper,two criteria are proposed to judge the deterministic optimal solution whether satisfies robustness requirment.The robustness measure based on maximum entropy is proposed.Weighted sum method is improved to deal with the objective function,and the basic framework of metamodel assisted robust optimization is also provided for improving the efficiency.Finally,several engineering examples are used to verify the advantages.展开更多
A method for optimizing automotive doors under multiple criteria involving the side impact, stiffness, natural frequency, and structure weight is presented. Metamodeling technique is employed to construct approximatio...A method for optimizing automotive doors under multiple criteria involving the side impact, stiffness, natural frequency, and structure weight is presented. Metamodeling technique is employed to construct approximations to replace the high computational simulation models. The approximating functions for stiffness and natural frequency are constructed using Taylor series approximation. Three popular approximation techniques,i.e.polynomial response surface (PRS), stepwise regression (SR), and Kriging are studied on their accuracy in the construction of side impact functions. Uniform design is employed to sample the design space of the door impact analysis. The optimization problem is solved by a multi-objective genetic algorithm. It is found that SR technique is superior to PRS and Kriging techniques in terms of accuracy in this study. The numerical results demonstrate that the method successfully generates a well-spread Pareto optimal set. From this Pareto optimal set, decision makers can select the most suitable design according to the vehicle program and its application.展开更多
Recently,the ontological metamodel plays an increasingly important role to specify systems in two forms:ontology and metamodel.Ontology is a descriptive model representing reality by a set of concepts,their interrelat...Recently,the ontological metamodel plays an increasingly important role to specify systems in two forms:ontology and metamodel.Ontology is a descriptive model representing reality by a set of concepts,their interrelations,and constraints.On the other hand,metamodel is a more classical,but more powerful model in which concepts and relationships are represented in a prescriptive way.This study firstly clarifies the difference between the two approaches,then explains their advantages and limitations,and attempts to explore a general ontological metamodeling framework by integrating each characteristic,in order to implement semantic simulation model engineering.As a proof of concept,this paper takes the combat effectiveness simulation systems as a motivating case,uses the proposed framework to define a set of ontological composable modeling frameworks,and presents an underwater targets search scenario for running simulations and analyzing results.Finally,this paper expects that this framework will be generally used in other fields.展开更多
Construction,management and extension of state space is crucial for many applications,Combining with frequent change of requirement of state space,these applications are rather complicated.Synthesizing techniques of r...Construction,management and extension of state space is crucial for many applications,Combining with frequent change of requirement of state space,these applications are rather complicated.Synthesizing techniques of refection architecture,MOP mechanism and metamodel,this paper advances a MOP(Meta Object Protocol)based constructive state metamodel,which defines construct and rules for building models with extensible structure for state space.Based on this metamodel,modeling with extension of state space that adopts decorator pattern and role object pattern is discussed.An implementation of modeling based on this metamodel is presented.With appropriate extension,models based on this metamodel can dynamically adapt state space change.Key words metamodel-reflection-MOP-state space CLC number TP 311.5 Foundation item:Supported by the National Natural Science Foundation of China(60373086)Biography:Liu Jin(1977-),male,Ph.D candidate,research direction:metamodeling and ontology application.展开更多
Although there is currently no unified standard theoretical formula for calculating the contact stress of cylindrical gears with a circular arc tooth trace(referred to as CATT gear),a mathematical model for determinin...Although there is currently no unified standard theoretical formula for calculating the contact stress of cylindrical gears with a circular arc tooth trace(referred to as CATT gear),a mathematical model for determining the contact stress of CATT gear is essential for studying how parameters affect its contact stress and building the contact stress limit state equation for contact stress reliability analysis.In this study,a mathematical relationship between design parameters and contact stress is formulated using the KrigingMetamodel.To enhance the model’s accuracy,we propose a new hybrid algorithm that merges the genetic algorithm with the Quantum Particle Swarm optimization algorithm,leveraging the strengths of each.Additionally,the“parental inheritance+self-learning”optimization model is used to fine-tune the KrigingMetamodel’s parameters.Following this,amathematicalmodel for calculating the contact stress of Variable Hyperbolic Circular-Arc-Tooth-Trace(VH-CATT)gears using the optimized Kriging model was developed.We then examined how different gear parameters affect the VH-CATT gears’contact stress.Our simulation results show:(1)Improvements in R2,RMSE,and RMAE.R2 rose from0.9852 to 0.9974(a 1.22%increase),nearing 1,suggesting the optimized Kriging Metamodel’s global error is minimized.Meanwhile,RMSE dropped from3.9210 to 1.6492,a decline of 57.94%.The global error of the GA-IQPSO-Kriging algorithm was also reduced,with RMAE decreasing by 58.69%from 0.1823 to 0.0753,showing the algorithm’s enhanced precision.In a comparison of ten experimental groups selected randomly,the GA-IQPSO-Kriging and FEM-based contact analysis methods were used to measure contact stress.Results revealed a maximum error of 12.11667 MPA,which represents 2.85%of the real value.(2)Several factors,including the pressure angle,tooth width,modulus,and tooth line radius,are inversely related to contact stress.The descending order of their impact on the contact stress is:tooth line radius>modulus>pressure angle>tooth width.(3)Complex interactions are noted among various parameters.Specifically,when the tooth line radius interacts with parameters such as pressure angle,tooth width,and modulus,the resulting stress contour is nonlinear,showcasing amultifaceted contour plane.However,when tooth width,modulus,and pressure angle interact,the stress contour is nearly linear,and the contour plane is simpler,indicating a weaker coupling among these factors.展开更多
The Electro-Hydrostatic Actuator(EHA)is a typical hydro-mechatronic control system.Due to the limited accuracy of measurement,inadequate knowledge,and vague judgments,hybrid uncertainties,including aleatory and episte...The Electro-Hydrostatic Actuator(EHA)is a typical hydro-mechatronic control system.Due to the limited accuracy of measurement,inadequate knowledge,and vague judgments,hybrid uncertainties,including aleatory and epistemic uncertainties,inevitably exist in the performance assessment of EHA systems.Existing methods ignored the hybrid uncertainties which can hardly obtain a satisfactory result while wasting a lot of time on the experimental design.To overcome this drawback,a metamodeling method for hybrid uncertainty propagation of EHA systems is developed via an active learning Gaussian Process(GP)model.The proposed method is bifurcated into three pillars:(A)Initializing the GP model and generating the optimum candidate sampling set by an Optimized Max-Minimize Distance(OMMD)algorithm,which aims to maximize the minimum distance between the added samples and original samples,(B)maximizing the learning function and generating new samples by a developed farthest or nearest judgment strategy,while updating the original GP model,and(C)judging the convergence by three uncertainty metrics,i.e.,the area metric,maximum variance metric,and the mean value metric.A numerical example is exemplified to evaluate the effectiveness and efficiency of the proposed method.Meanwhile,the EHA system of aircrafts is examined to show the application of the proposed method for high-dimensional problems.The effects of the uncertainties in the Proportional-Integral-Differential(PID)of the EHA system are also examined.展开更多
Model Driven Engineering (MDE) is a model-centric software development approach aims at improving the quality and productivity of software development processes. While some progresses in MDE have been made, there are ...Model Driven Engineering (MDE) is a model-centric software development approach aims at improving the quality and productivity of software development processes. While some progresses in MDE have been made, there are still many challenges in realizing the full benefits of model driven engineering. These challenges include incompleteness in existing modeling notations, inadequate in tools support, and the lack of effective model transformation mechanism. This paper provides a solution to build a template-based model transformation framework using a simplified metamode called Hierarchical Relational Metamodel (HRM). This framework supports MDE while providing the benefits of readability and rigorousness of meta-model definitions and transformation definitions.展开更多
Reusing business process models and best practices can improve the productivity, quality and agility in the early development phases of enterprise software systems. To help developers reuse the business process models...Reusing business process models and best practices can improve the productivity, quality and agility in the early development phases of enterprise software systems. To help developers reuse the business process models and best practices, we propose a methodology and an integrated environment for business process modeling driven by the metamodel. Furthermore, we propose a process-template design method to unify the granularity and separate the commonality and variability of business processes so that business process models can be reused across different enterprise software systems. The proposed methodology enables to create reuse-oriented business process templates before the business process modeling. To support the proposed methodology, we developed an integrated environment for creating, reusing and verifying the business process models. As the key techniques, we describe the methodology and its integrated environment, including a metamodel and notations. We applied the methodology and integrated environment to an actual enterprise software development project, and evaluated that the productivity of business process modeling is improved by at least 46%. As the conclusion, this paper contributes to prove the effectiveness of the meta-model driven business process modeling methodology for the reuse of business process models.展开更多
The electric power transfer capability on the Manitoba-Ontario interconnection depends on various system operating conditions such as area generation patterns and ambient temperatures. This work models the power netwo...The electric power transfer capability on the Manitoba-Ontario interconnection depends on various system operating conditions such as area generation patterns and ambient temperatures. This work models the power network as a black-box function, which is evaluated with the system reliability analysis techniques to determine the maximum transfer capability under a given operating condition. A metamodel or an approximation model of the maximized power transfer capability is built based on the sampled system responses and optimized with respect to the corresponding operating conditions. An optimal metamodel is implemented as a prototype software tool, PTCanalyzer, and applied to Manitoba-Ontario interconnection power transfer calculations. This optimized metamodel technique provides an in-depth understanding of the dependency of the power transfer capability on system operating conditions and proves to be an effective tool in optimizing the operation planning of the interconnection for a given power system configuration. The PTCanalyzer has the potential to be used for optimization of other power network interconnections.展开更多
Value delivery is becoming an important asset for an organization due to increasing competition in industry. Therefore, companies apply Agile Software Development (ASD) to be more competitive and reduce time to market...Value delivery is becoming an important asset for an organization due to increasing competition in industry. Therefore, companies apply Agile Software Development (ASD) to be more competitive and reduce time to market. Using ASD for the development of systems implies that established approaches of Requirements Engineering (RE) undergo some changes in order to be more flexible to changing requirements. To this end, the field of agile RE is emergent and different process models for agile RE have arisen. The aim of this paper is to build an abstract layer about the variety of existing process models by means of a metamodel for agile RE. It has been created in several iterations and relies on the evaluation of related process models. Furthermore, we have derived process models for agile RE in industry by presenting instances of the metamodel in two different cases: one is based on Scrum whereas the other is based on Kanban. This paper contributes to the software development body of knowledge by delivering a metamodel for agile RE that supports researchers and practitioners modeling and improving their own process models. We can conclude that the agile RE metamodel is highly relevant for the industry as well as for the research community, since we have derived it following empirical research in the field of ASD.展开更多
This paper proposes kriging metamodels for the dynamic response of high-rise buildings with outrigger systems subject to seismic and wind loads.Three types of outrigger systems are considered.Three-dimensional(3D)fini...This paper proposes kriging metamodels for the dynamic response of high-rise buildings with outrigger systems subject to seismic and wind loads.Three types of outrigger systems are considered.Three-dimensional(3D)finite element models of high-rise buildings with outrigger systems are developed using ANSYS.Data generated from the finite element models are used to develop the proposed kriging metamodels.A sensitivity analysis is then carried out to determine the most sensitive input parameters in kriging metamodels to gain insights and suggest possible future developments.The proposed kriging metamodels are used to develop fragility estimates for high-rise buildings with three types of outrigger systems under seismic and wind loads.展开更多
文摘Quantum software development utilizes quantum phenomena such as superposition and entanglement to address problems that are challenging for classical systems.However,it must also adhere to critical quantum constraints,notably the no-cloning theorem,which prohibits the exact duplication of unknown quantum states and has profound implications for cryptography,secure communication,and error correction.While existing quantum circuit representations implicitly honor such constraints,they lack formal mechanisms for early-stage verification in software design.Addressing this constraint at the design phase is essential to ensure the correctness and reliability of quantum software.This paper presents a formal metamodeling framework using UML-style notation and and Object Constraint Language(OCL)to systematically capture and enforce the no-cloning theorem within quantum software models.The proposed metamodel formalizes key quantum concepts—such as entanglement and teleportation—and encodes enforceable invariants that reflect core quantum mechanical laws.The framework’s effectiveness is validated by analyzing two critical edge cases—conditional copying with CNOT gates and quantum teleportation—through instance model evaluations.These cases demonstrate that the metamodel can capture nuanced scenarios that are often mistaken as violations of the no-cloning theorem but are proven compliant under formal analysis.Thus,these serve as constructive validations that demonstrate the metamodel’s expressiveness and correctness in representing operations that may appear to challenge the no-cloning theorem but,upon rigorous analysis,are shown to comply with it.The approach supports early detection of conceptual design errors,promoting correctness prior to implementation.The framework’s extensibility is also demonstrated by modeling projective measurement,further reinforcing its applicability to broader quantum software engineering tasks.By integrating the rigor of metamodeling with fundamental quantum mechanical principles,this work provides a structured,model-driven approach that enables traditional software engineers to address quantum computing challenges.It offers practical insights into embedding quantum correctness at the modeling level and advances the development of reliable,error-resilient quantum software systems.
基金the Yunnan Provincial Department of Education Research Fund Key Project(No.2011z025)General Project(No.2011y214)
文摘Domain-specific metamodeling language(DSMML) defined by informal method cannot strictly represent its structural semantics,so its properties such as consistency cannot be holistically and systematically verified.In response,the paper proposes a formal representation of the structural semantics of DSMML named extensible markup language(XML) based metamodeling language(XMML) and its metamodels consistency verification method.Firstly,we describe our approach of formalization,based on this,the method of consistency verification of XMML and its metamodels based on first-order logical inference is presented;then,the formalization automatic mapping engine for metamodels is designed to show the feasibility of our formal method.
文摘Constructing metamodel with global high-fidelity in design space is significant in engineering design. In this paper, a double-stage metamodel （DSM） which integrates advantages of both interpolation metamodel and regression metamodel is constructed. It takes regression model as the first stage to fit overall distribution of the original model, and then interpolation model of regression model approximation error is used as the second stage to improve accuracy. Under the same conditions and with the same samples, DSM expresses higher fidelity and represents physical characteristics of original model better. Besides, in order to validate DSM characteristics, three examples including Ackley function, airfoil aerodynamic analysis and wing aerodynamic analysis are investigated, In the end, airfoil and wing aerodynamic design optimizations using genetic algorithm are presented to verify the engineering applicability of DSM.
基金supported by National Natural Science Foundation of China (Grant No.60572007)National Basic Research Program of China(973 Program,Grant No.613580202)
文摘Metamodeling techniques have been used in robust optimization to reduce the high computational cost of the uncertainty analysis and improve the performance of robust optimization problems with computationally expensive simulation models. Existing metamodels main focus on polynomial regression（PR）, neural networks（NN） and Kriging models, these metamodels are not well suited for large-scale robust optimization problems with small size training sets and high nonlinearity. To address the problem, a reduced approximation model technique based on support vector regression（SVR） is introduced in order to improve the accuracy of metamodels. A robust optimization method based on SVR is presented for problems that involve high dimension and nonlinear. First appropriate design parameter samples are selected by experimental design theories, then the response samples are obtained from the simulations such as finite element analysis, the SVR metamodel is constructed and treated as the mean and the variance of the objective performance functions. Combining other constraints, the robust optimization model is formed which can be solved by genetic algorithm （GA）. The applicability of the method developed is demonstrated using a case of two-bar structure system study. The performances of SVR were compared with those of PR, Kriging and back-propagation neural networks（BPNN）, the comparison results show that the prediction accuracy of the SVR metamodel was higher than those of other metamodels under uncertainty. The robust optimization solutions are near to the real result, and the proposed method is found to be accurate and efficient for robust optimization. This reaserch provides an efficient method for robust optimization problems with complex structure.
基金National Natural Science Foundation of China (No.5988950)
文摘Virtual product development (VPD) is essentially based on simulation. Due tocomputational inefficiency, traditional engineering simulation software and optimization methods areinadequate to analyze optimization problems in VPD. Optimization method based on simulationmetamodel for virtual product development is proposed to satisfy the needs of complex optimaldesigns driven by VPD. This method extends the current design of experiments (DOE) by variousmetamodeling technologies. Simulation metamodels are built to approximate detailed simulation codes,so as to provide link between optimization and simulation, or serve as a bridge for simulationsoftware integration among different domains. An example of optimal design for composite materialstructure is used to demonstrate the newly introduced method.
基金supported by National Natural Science Foundation of China(Grant No.51105040)Aeronautic Science Foundation of China(Grant No.2011ZA72003)Excellent Young Scholars Research Fund of Beijing Institute of Technology(Grant No.2010Y0102)
文摘High fidelity analysis are utilized in modern engineering design optimization problems which involve expensive black-box models.For computation-intensive engineering design problems,efficient global optimization methods must be developed to relieve the computational burden.A new metamodel-based global optimization method using fuzzy clustering for design space reduction(MGO-FCR) is presented.The uniformly distributed initial sample points are generated by Latin hypercube design to construct the radial basis function metamodel,whose accuracy is improved with increasing number of sample points gradually.Fuzzy c-mean method and Gath-Geva clustering method are applied to divide the design space into several small interesting cluster spaces for low and high dimensional problems respectively.Modeling efficiency and accuracy are directly related to the design space,so unconcerned spaces are eliminated by the proposed reduction principle and two pseudo reduction algorithms.The reduction principle is developed to determine whether the current design space should be reduced and which space is eliminated.The first pseudo reduction algorithm improves the speed of clustering,while the second pseudo reduction algorithm ensures the design space to be reduced.Through several numerical benchmark functions,comparative studies with adaptive response surface method,approximated unimodal region elimination method and mode-pursuing sampling are carried out.The optimization results reveal that this method captures the real global optimum for all the numerical benchmark functions.And the number of function evaluations show that the efficiency of this method is favorable especially for high dimensional problems.Based on this global design optimization method,a design optimization of a lifting surface in high speed flow is carried out and this method saves about 10 h compared with genetic algorithms.This method possesses favorable performance on efficiency,robustness and capability of global convergence and gives a new optimization strategy for engineering design optimization problems involving expensive black box models.
基金supported by the National Natural Science Foundation of China(No.52175214)the Basic Research Program of Equipment Development Department(No.514010103-302).
文摘Aiming to reduce the high expense of 3-Dimensional(3D)aerodynamics numerical sim-ulations and overcome the limitations of the traditional parametric learning methods,a point cloud deep learning non-parametric metamodel method is proposed in this paper.The 3D geometric data,corresponding to the object boundaries,are chosen as point clouds and a deep learning neural net-work metamodel fed by the point clouds is further established based on the PointNet architecture.This network can learn an end-to-end mapping between spatial positions of the object surface and CFD numerical quantities.With the proposed aerodynamic metamodel approach,the point clouds are constructed by collecting the coordinates of grid vertices on the object surface in a CFD domain,which can maintain the boundary smoothness and allow the network to detect small changes between geometries.Moreover,the point clouds are easily accessible from 3D sensors.The point cloud deep learning neural network,which employs re-sampling technique,the spatial transformer network and the fully connected layer,is developed to predict the aerodynamic char-acteristics of 3D geometry.The effectiveness of the proposed metamodel method is further verified by aerodynamic prediction and robust shape optimization of the ONERA M6 wing.The results show that the proposed method can achieve more satisfactory agreement with the experimental measurements compared to the parametric-learning-based deep neural network.
文摘Modern engineering design optimization often relies on computer simulations to evaluate candidate designs, a setup which results in expensive black-box optimization problems. Such problems introduce unique challenges, which has motivated the application of metamodel-assisted computational intelligence algorithms to solve them. Such algorithms combine a computational intelligence optimizer which employs a population of candidate solutions, with a metamodel which is a computationally cheaper approximation of the expensive computer simulation. However, although a variety of metamodels and optimizers have been proposed, the optimal types to employ are problem dependant. Therefore, a priori prescribing the type of metamodel and optimizer to be used may degrade its effectiveness. Leveraging on this issue, this study proposes a new computational intelligence algorithm which autonomously adapts the type of the metamodel and optimizer during the search by selecting the most suitable types out of a family of candidates at each stage. Performance analysis using a set of test functions demonstrates the effectiveness of the proposed algorithm, and highlights the merit of the proposed adaptation approach.
基金Supported by the National Natural Science Foundation of China(11532002)
文摘Combining a trust region method with a biased sampling method,a novel optimization strategy(TRBSKRG)based on a dynamic metamodel is proposed.Initial sampling points are selected by a maximin Latin hypercube design method,and the metamodel is constructed with Kriging functions.The global optimization algorithm is employed to perform the biased sampling by searching the maximum expectation improvement point or the minimum of surrogate prediction point within the trust region.And the trust region is updated according to the current known information.The iteration continues until the potential global solution of the true optimization problem satisfied the convergence conditions.Compared with the trust region method and the biased sampling method,the proposed optimization strategy can obtain the global optimal solution to the test case,in which improvements in computation efficiency are also shown.When applied to an aerodynamic design optimization problem,the aerodynamic performance of tandem UAV is improved while meeting the constraints,which verifies its engineering application.
基金The study is supported by the National Numerical Wind tunnel project(No.2019ZT2-A05)the Nature Science Foundation of China(No.11902254).
文摘The robust design optimization(RDO)is an effective method to improve product performance with uncertainty factors.The robust optimal solution should be not only satisfied the probabilistic constraints but also less sensitive to the variation of design variables.There are some important issues in RDO,such as how to judge robustness,deal with multi-objective problem and black-box situation.In this paper,two criteria are proposed to judge the deterministic optimal solution whether satisfies robustness requirment.The robustness measure based on maximum entropy is proposed.Weighted sum method is improved to deal with the objective function,and the basic framework of metamodel assisted robust optimization is also provided for improving the efficiency.Finally,several engineering examples are used to verify the advantages.
基金Supported by National"863"Program of China (No.2006AA04Z127) .
文摘A method for optimizing automotive doors under multiple criteria involving the side impact, stiffness, natural frequency, and structure weight is presented. Metamodeling technique is employed to construct approximations to replace the high computational simulation models. The approximating functions for stiffness and natural frequency are constructed using Taylor series approximation. Three popular approximation techniques,i.e.polynomial response surface (PRS), stepwise regression (SR), and Kriging are studied on their accuracy in the construction of side impact functions. Uniform design is employed to sample the design space of the door impact analysis. The optimization problem is solved by a multi-objective genetic algorithm. It is found that SR technique is superior to PRS and Kriging techniques in terms of accuracy in this study. The numerical results demonstrate that the method successfully generates a well-spread Pareto optimal set. From this Pareto optimal set, decision makers can select the most suitable design according to the vehicle program and its application.
基金the National Natural Science Foundation of China(61273198).
文摘Recently,the ontological metamodel plays an increasingly important role to specify systems in two forms:ontology and metamodel.Ontology is a descriptive model representing reality by a set of concepts,their interrelations,and constraints.On the other hand,metamodel is a more classical,but more powerful model in which concepts and relationships are represented in a prescriptive way.This study firstly clarifies the difference between the two approaches,then explains their advantages and limitations,and attempts to explore a general ontological metamodeling framework by integrating each characteristic,in order to implement semantic simulation model engineering.As a proof of concept,this paper takes the combat effectiveness simulation systems as a motivating case,uses the proposed framework to define a set of ontological composable modeling frameworks,and presents an underwater targets search scenario for running simulations and analyzing results.Finally,this paper expects that this framework will be generally used in other fields.
基金Supported by the National Natural Science Foun dation of China(60373086)
文摘Construction,management and extension of state space is crucial for many applications,Combining with frequent change of requirement of state space,these applications are rather complicated.Synthesizing techniques of refection architecture,MOP mechanism and metamodel,this paper advances a MOP(Meta Object Protocol)based constructive state metamodel,which defines construct and rules for building models with extensible structure for state space.Based on this metamodel,modeling with extension of state space that adopts decorator pattern and role object pattern is discussed.An implementation of modeling based on this metamodel is presented.With appropriate extension,models based on this metamodel can dynamically adapt state space change.Key words metamodel-reflection-MOP-state space CLC number TP 311.5 Foundation item:Supported by the National Natural Science Foundation of China(60373086)Biography:Liu Jin(1977-),male,Ph.D candidate,research direction:metamodeling and ontology application.
基金supported by the National Natural Science Foundation of China(Project No.51875370)the Natural Science Foundation of Sichuan Province(Project Nos.2022NSFSC0454,2022NSFSC1975)+2 种基金Sichuan Science and Technology Program(Project No.2023ZYD0139)the University Key Laboratory of Sichuan in Process Equipment and Control Engineering(No.GK201905)Key Laboratory of Fluid and Power Machinery,Ministry of Education(No.LTDL2020-006).
文摘Although there is currently no unified standard theoretical formula for calculating the contact stress of cylindrical gears with a circular arc tooth trace(referred to as CATT gear),a mathematical model for determining the contact stress of CATT gear is essential for studying how parameters affect its contact stress and building the contact stress limit state equation for contact stress reliability analysis.In this study,a mathematical relationship between design parameters and contact stress is formulated using the KrigingMetamodel.To enhance the model’s accuracy,we propose a new hybrid algorithm that merges the genetic algorithm with the Quantum Particle Swarm optimization algorithm,leveraging the strengths of each.Additionally,the“parental inheritance+self-learning”optimization model is used to fine-tune the KrigingMetamodel’s parameters.Following this,amathematicalmodel for calculating the contact stress of Variable Hyperbolic Circular-Arc-Tooth-Trace(VH-CATT)gears using the optimized Kriging model was developed.We then examined how different gear parameters affect the VH-CATT gears’contact stress.Our simulation results show:(1)Improvements in R2,RMSE,and RMAE.R2 rose from0.9852 to 0.9974(a 1.22%increase),nearing 1,suggesting the optimized Kriging Metamodel’s global error is minimized.Meanwhile,RMSE dropped from3.9210 to 1.6492,a decline of 57.94%.The global error of the GA-IQPSO-Kriging algorithm was also reduced,with RMAE decreasing by 58.69%from 0.1823 to 0.0753,showing the algorithm’s enhanced precision.In a comparison of ten experimental groups selected randomly,the GA-IQPSO-Kriging and FEM-based contact analysis methods were used to measure contact stress.Results revealed a maximum error of 12.11667 MPA,which represents 2.85%of the real value.(2)Several factors,including the pressure angle,tooth width,modulus,and tooth line radius,are inversely related to contact stress.The descending order of their impact on the contact stress is:tooth line radius>modulus>pressure angle>tooth width.(3)Complex interactions are noted among various parameters.Specifically,when the tooth line radius interacts with parameters such as pressure angle,tooth width,and modulus,the resulting stress contour is nonlinear,showcasing amultifaceted contour plane.However,when tooth width,modulus,and pressure angle interact,the stress contour is nearly linear,and the contour plane is simpler,indicating a weaker coupling among these factors.
基金the National Natural Science Foundation of China(Nos.72301057,72271044,72331002,and 52305010)the Sichuan Science and Technology Program,China(No.2023YFG0157).
文摘The Electro-Hydrostatic Actuator(EHA)is a typical hydro-mechatronic control system.Due to the limited accuracy of measurement,inadequate knowledge,and vague judgments,hybrid uncertainties,including aleatory and epistemic uncertainties,inevitably exist in the performance assessment of EHA systems.Existing methods ignored the hybrid uncertainties which can hardly obtain a satisfactory result while wasting a lot of time on the experimental design.To overcome this drawback,a metamodeling method for hybrid uncertainty propagation of EHA systems is developed via an active learning Gaussian Process(GP)model.The proposed method is bifurcated into three pillars:(A)Initializing the GP model and generating the optimum candidate sampling set by an Optimized Max-Minimize Distance(OMMD)algorithm,which aims to maximize the minimum distance between the added samples and original samples,(B)maximizing the learning function and generating new samples by a developed farthest or nearest judgment strategy,while updating the original GP model,and(C)judging the convergence by three uncertainty metrics,i.e.,the area metric,maximum variance metric,and the mean value metric.A numerical example is exemplified to evaluate the effectiveness and efficiency of the proposed method.Meanwhile,the EHA system of aircrafts is examined to show the application of the proposed method for high-dimensional problems.The effects of the uncertainties in the Proportional-Integral-Differential(PID)of the EHA system are also examined.
文摘Model Driven Engineering (MDE) is a model-centric software development approach aims at improving the quality and productivity of software development processes. While some progresses in MDE have been made, there are still many challenges in realizing the full benefits of model driven engineering. These challenges include incompleteness in existing modeling notations, inadequate in tools support, and the lack of effective model transformation mechanism. This paper provides a solution to build a template-based model transformation framework using a simplified metamode called Hierarchical Relational Metamodel (HRM). This framework supports MDE while providing the benefits of readability and rigorousness of meta-model definitions and transformation definitions.
文摘Reusing business process models and best practices can improve the productivity, quality and agility in the early development phases of enterprise software systems. To help developers reuse the business process models and best practices, we propose a methodology and an integrated environment for business process modeling driven by the metamodel. Furthermore, we propose a process-template design method to unify the granularity and separate the commonality and variability of business processes so that business process models can be reused across different enterprise software systems. The proposed methodology enables to create reuse-oriented business process templates before the business process modeling. To support the proposed methodology, we developed an integrated environment for creating, reusing and verifying the business process models. As the key techniques, we describe the methodology and its integrated environment, including a metamodel and notations. We applied the methodology and integrated environment to an actual enterprise software development project, and evaluated that the productivity of business process modeling is improved by at least 46%. As the conclusion, this paper contributes to prove the effectiveness of the meta-model driven business process modeling methodology for the reuse of business process models.
文摘The electric power transfer capability on the Manitoba-Ontario interconnection depends on various system operating conditions such as area generation patterns and ambient temperatures. This work models the power network as a black-box function, which is evaluated with the system reliability analysis techniques to determine the maximum transfer capability under a given operating condition. A metamodel or an approximation model of the maximized power transfer capability is built based on the sampled system responses and optimized with respect to the corresponding operating conditions. An optimal metamodel is implemented as a prototype software tool, PTCanalyzer, and applied to Manitoba-Ontario interconnection power transfer calculations. This optimized metamodel technique provides an in-depth understanding of the dependency of the power transfer capability on system operating conditions and proves to be an effective tool in optimizing the operation planning of the interconnection for a given power system configuration. The PTCanalyzer has the potential to be used for optimization of other power network interconnections.
基金supported by the Pololas project(TIN2016-76956-C3-2-R)of the Spanish Ministry of Economy and Competitiveness.
文摘Value delivery is becoming an important asset for an organization due to increasing competition in industry. Therefore, companies apply Agile Software Development (ASD) to be more competitive and reduce time to market. Using ASD for the development of systems implies that established approaches of Requirements Engineering (RE) undergo some changes in order to be more flexible to changing requirements. To this end, the field of agile RE is emergent and different process models for agile RE have arisen. The aim of this paper is to build an abstract layer about the variety of existing process models by means of a metamodel for agile RE. It has been created in several iterations and relies on the evaluation of related process models. Furthermore, we have derived process models for agile RE in industry by presenting instances of the metamodel in two different cases: one is based on Scrum whereas the other is based on Kanban. This paper contributes to the software development body of knowledge by delivering a metamodel for agile RE that supports researchers and practitioners modeling and improving their own process models. We can conclude that the agile RE metamodel is highly relevant for the industry as well as for the research community, since we have derived it following empirical research in the field of ASD.
基金funded by the National Natural Science Founda-tion of China(Grant No.52025083)the financial support received from this organization and China Scholar-ship Council during a visiting study in University of Illinois at Urbana-Champaign(No.201906260196).
文摘This paper proposes kriging metamodels for the dynamic response of high-rise buildings with outrigger systems subject to seismic and wind loads.Three types of outrigger systems are considered.Three-dimensional(3D)finite element models of high-rise buildings with outrigger systems are developed using ANSYS.Data generated from the finite element models are used to develop the proposed kriging metamodels.A sensitivity analysis is then carried out to determine the most sensitive input parameters in kriging metamodels to gain insights and suggest possible future developments.The proposed kriging metamodels are used to develop fragility estimates for high-rise buildings with three types of outrigger systems under seismic and wind loads.
