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
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
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
Techniques for constructing metamodels of device parameters at BSIM3v3 level accuracy are presented to improve knowledge-based circuit sizing optimization. Based on the analysis of the prediction error of analytical p...Techniques for constructing metamodels of device parameters at BSIM3v3 level accuracy are presented to improve knowledge-based circuit sizing optimization. Based on the analysis of the prediction error of analytical performance expressions, operating point driven （OPD） metamodels of MOSFETs are introduced to capture the circuit＇s characteristics precisely. In the algorithm of metamodel construction, radial basis functions are adopted to interpolate the scattered multivariate data obtained from a well tailored data sampling scheme designed for MOSFETs. The OPD metamodels can be used to automatically bias the circuit at a specific DC operating point. Analytical-based performance expressions composed by the OPD metamodels show obvious improvement for most small-signal performances compared with simulation-based models. Both operating-point variables and transistor dimensions can be optimized in our nesting-loop optimization formulation to maximize design flexibility. The method is successfully applied to a low-voltage low-power amplifier.展开更多
The complexity and diversity of modern software demands a variety of metamodel-based modeling languages for software development. Existing languages change continuously, and new ones are constantly emerging. In this s...The complexity and diversity of modern software demands a variety of metamodel-based modeling languages for software development. Existing languages change continuously, and new ones are constantly emerging. In this situation, and especially for metamodel-based modeling languages, a quality assurance mechanism for metamodels is needed. This paper presents an approach to assessing the quality of metamodels. A quality model, which systematically characterizes and classifies quality attributes, and an operable measuring mechanism for effectively assessing the quality of metamodels based on the quality model, are pre- sented, using UML as the main example.展开更多
Metamodels have been widely used as an alternative for expensive physical experiments or complex,time-consuming computational simulations to provide a fast but accurate analysis.However,challenge remains in the prior ...Metamodels have been widely used as an alternative for expensive physical experiments or complex,time-consuming computational simulations to provide a fast but accurate analysis.However,challenge remains in the prior determination of the most suitable metamodel for a particular case because of the lack of information about the actual behavior of a system.In addition,existing studies on metamodels have largely restricted on solving deterministic problems(e.g.,data from finite element models),whereas some real-life engineering problems(e.g.,data from physical experiment)are stochastic problems with noisy data.In this work,a robust ensemble of metamodels(EMs)is proposed by combining three regression stand-alone metamodels in a weighted sum form.The weight factor is adaptively determined according to the hybrid error metric,which combines global and local error measures to improve the accuracy of the EMs.Furthermore,three typical individual metamodels that can filter noise are selected to construct the EMs to extend their application in practical engineering problems.Three well-known benchmark problems with different levels of noise and three engineering problems are used to verify the effectiveness of the proposed EMs.Results show that the proposed EMs have higher accuracy and robustness than the individual metamodels and other typical EMs in major cases.展开更多
In fiber laser beam welding(LBW),the selection of optimal processing parameters is challenging and plays a key role in improving the bead geometry and welding quality.This study proposes a multi-objective optimization...In fiber laser beam welding(LBW),the selection of optimal processing parameters is challenging and plays a key role in improving the bead geometry and welding quality.This study proposes a multi-objective optimization framework by combining an ensemble of metamodels(EMs)with the multi-objective artificial bee colony algorithm(MOABC)to identify the optimal welding parameters.An inverse proportional weighting method that considers the leave-one-out prediction error is presented to construct EM,which incorporates the competitive strengths of three metamodels.EM constructs the correlation between processing parameters(laser power,welding speed,and distance defocus)and bead geometries(bead width,depth of penetration,neck width,and neck depth)with average errors of 10.95%,7.04%,7.63%,and 8.62%,respectively.On the basis of EM,MOABC is employed to approximate the Pareto front,and verification experiments show that the relative errors are less than 14.67%.Furthermore,the main effect and the interaction effect of processing parameters on bead geometries are studied.Results demonstrate that the proposed EM-MOABC is effective in guiding actual fiber LBW applications.展开更多
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
SPEM(software process engineering metamodel)是国际标准化组织制定的标准元模型,正日益成为软件过程建模领域的行业标准,但在过程执行方面,SPEM还存在不足.将软件过程看作是一种特殊的工作流,提出了一种应用工作流运行机制支持软件...SPEM(software process engineering metamodel)是国际标准化组织制定的标准元模型,正日益成为软件过程建模领域的行业标准,但在过程执行方面,SPEM还存在不足.将软件过程看作是一种特殊的工作流,提出了一种应用工作流运行机制支持软件过程执行的方法.通过将SPEM模型转换为XPDL(XML process definition language)模型,利用XPDL引擎支持SPEM模型的执行.制定了SPEM和XPDL之间的映射规则,设计了转换算法并开发了转换引擎.该方法被应用在SoftPM项目中,成功地基于XPDL引擎Shark实现了对软件过程模型的执行支持.展开更多
Usage of rolling contact bearings in variety of rotor-dynamic applications has put forth a need to develop a detailed and easy to implement techniques for the assessment of damage related features in these bearings so...Usage of rolling contact bearings in variety of rotor-dynamic applications has put forth a need to develop a detailed and easy to implement techniques for the assessment of damage related features in these bearings so that before mechanical failure,maintenance actions can be planned well in advance.In accordance to this,a method based on dimensional amplitude response analysis and scaling laws is presented in this paper for the diagnosis of defects in different components of rolling contact bearings in a dimensionally scaled rotor-bearing system.Rotor,bearing,operating and defect parameters involved are detailed for dimensional analysis using frequency domain vibration data.A defect parameter for modeling all the three dimensions of the defect as well as the different shapes like square,circular,rectangular is put forth which takes into account the volume as well as the surface area of the defect.Experimental data set is generated for the‘model’bearing(designated as SKF30205J2/Q)using Box-Behnken design of response surface methodology for solution of the theoretical model by factorial regression approach.Obtained metamodel is then used for the prediction of the objective variable,i.e.,Vibration acceleration amplitude at the defect frequency component for other types of‘test’bearings(designated as SKF 30305C and SKF 22220 EK)using the developed scaling laws.Confirmation experiments showed that the computable relationship amongst objective variable and the dimensionless parameters can be forecast and correlated.展开更多
Simulation and optimization are the key points of virtual product development (VPD). Traditional engineering simulation software and optimization methods are inadequate to analyze the optimization problems because of ...Simulation and optimization are the key points of virtual product development (VPD). Traditional engineering simulation software and optimization methods are inadequate to analyze the optimization problems because of its computational inefficiency. A systematic design optimization strategy by using statistical methods and mathematical optimization technologies is proposed. This method extends the design of experiments (DOE) and the simulation metamodel technologies. Metamodels are built to in place of detailed simulation codes based on effectively DOE, and then be linked to optimization routines for fast analysis, or serve as a bridge for integrating simulation software across different domains. A design optimization of composite material structure is used to demonstrate the newly introduced methodology.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
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
Efficient experiment design is of great significance for the validation of simulation model with high nonlinearity and large input space.Excessive validation experiment raises the cost while insufficient test increase...Efficient experiment design is of great significance for the validation of simulation model with high nonlinearity and large input space.Excessive validation experiment raises the cost while insufficient test increases the risks of accepting an invalid model.In this paper,an adaptive sequential experiment design method combining global exploration criterion and local exploitation criterion is proposed.The exploration criterion utilizes discrepancy metric to improve the space-filling property of the design points while the exploitation criterion employs the leave one out error to discover informative points.To avoid the clustering of samples in the local region,an adaptive weight updating approach is provided to maintain the balance between exploration and exploitation.Besides,the credibility distribution function characterizing the relationship between the input and result credibility is introduced to support the model validation experiment design.Finally,six benchmark problems and an engineering case are applied to examine the performance of the proposed method.The experiments indicate that the proposed method achieves satisfactory performance for function approximation in accuracy and convergence.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
In this paper a hybrid process of modeling and optimization, which integrates a support vector machine （SVM） and genetic algorithm （GA）, was introduced to reduce the high time cost in structural optimization of sh...In this paper a hybrid process of modeling and optimization, which integrates a support vector machine （SVM） and genetic algorithm （GA）, was introduced to reduce the high time cost in structural optimization of ships. SVM, which is rooted in statistical learning theory and an approximate implementation of the method of structural risk minimization, can provide a good generalization performance in metamodeling the input-output relationship of real problems and consequently cuts down on high time cost in the analysis of real problems, such as FEM analysis. The GA, as a powerful optimization technique, possesses remarkable advantages for the problems that can hardly be optimized with common gradient-based optimization methods, which makes it suitable for optimizing models built by SVM. Based on the SVM-GA strategy, optimization of structural scantlings in the midship of a very large crude carrier （VLCC） ship was carried out according to the direct strength assessment method in common structural rules （CSR）, which eventually demonstrates the high efficiency of SVM-GA in optimizing the ship structural scantlings under heavy computational complexity. The time cost of this optimization with SVM-GA has been sharply reduced, many more loops have been processed within a small amount of time and the design has been improved remarkably.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 simulation and planning system(SPS)requires accurate and real-time feedback regarding the deformation of soft tissues during the needle insertion procedure.Traditional mechanical-based models such as the finite el...The simulation and planning system(SPS)requires accurate and real-time feedback regarding the deformation of soft tissues during the needle insertion procedure.Traditional mechanical-based models such as the finite element method(FEM)are widely used to compute the deformations of soft tissue.However,it is difficult for the FEM or other methods to find a balance between an acceptable image fidelity and real-time deformation feedback due to their complex material properties,geometries and interaction mechanisms.In this paper,a Kriging-based method is applied to model the soft tissue deformation to strike a balance between the accuracy and efficiency of deformation feedback.Four combinations of regression and correlation functions are compared regarding their ability to predict the maximum deformations of ten characteristic markers at a fixed insertion depth.The results suggest that a first order regression function with Gaussian correlation functions can best fit the results of the ground truth.The functional response of the Kriging-based method is utilized to model the dynamic deformations of markers at a series of needle insertion depths.The feasibility of the method is verified by investigating the adaptation to step variations.Compared with the ground truth of the finite element(FE)results,the maximum residual is less than 0.92 mm in the Y direction and 0.31 mm in the X direction.The results suggest that the Kriging metamodel provides real-time deformation feedback for a target and an obstacle to a SPS.展开更多
Callovo-Oxfordian(COx)claystone has been considered as a potential host rock for geological radioactive waste disposal in France(Cigéo project).During the exploitation phase(100 years),the stability of drifts(e.g...Callovo-Oxfordian(COx)claystone has been considered as a potential host rock for geological radioactive waste disposal in France(Cigéo project).During the exploitation phase(100 years),the stability of drifts(e.g.galleries/alveoli)within the disposal is assured by the liner,which includes two layers:concrete arch segment and compressible material.The latter exhibits a significant deformation capacity(about 50%)under low stress(<3 MPa).Although the response of these underground structures can be governed by complex thermo-hydro-mechanical coupling,the creep behavior of COx claystone has been considered as the main factor controlling the increase of stress state in the concrete liner and hence the long-term stability of drifts.Therefore,by focusing only on the purely mechanical behavior,this study aims at investigating the uncertainty effect of the COx claystone time-dependent properties on the stability of an alveolus of Cigéo during the exploitation period.To describe the creep behavior of COx claystone,we use Lemaitre’s viscoplastic model with three parameters whose uncertainties are identified from laboratory creep tests.For the reliability analysis,an extension of a well-known Kriging metamodeling technique is proposed to assess the exceedance probability of acceptable stress in the concrete liner of the alveolus.The open-source code Code_Aster is chosen for the direct numerical evaluations of the performance function.The Kriging-based reliability analysis elucidates the effect of the uncertainty of COx claystone on the long-term stability of the concrete liner.Moreover,the role of the compressible material layer between the concrete liner and the host rock is also highlighted.展开更多
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.
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.
文摘Techniques for constructing metamodels of device parameters at BSIM3v3 level accuracy are presented to improve knowledge-based circuit sizing optimization. Based on the analysis of the prediction error of analytical performance expressions, operating point driven （OPD） metamodels of MOSFETs are introduced to capture the circuit＇s characteristics precisely. In the algorithm of metamodel construction, radial basis functions are adopted to interpolate the scattered multivariate data obtained from a well tailored data sampling scheme designed for MOSFETs. The OPD metamodels can be used to automatically bias the circuit at a specific DC operating point. Analytical-based performance expressions composed by the OPD metamodels show obvious improvement for most small-signal performances compared with simulation-based models. Both operating-point variables and transistor dimensions can be optimized in our nesting-loop optimization formulation to maximize design flexibility. The method is successfully applied to a low-voltage low-power amplifier.
文摘The complexity and diversity of modern software demands a variety of metamodel-based modeling languages for software development. Existing languages change continuously, and new ones are constantly emerging. In this situation, and especially for metamodel-based modeling languages, a quality assurance mechanism for metamodels is needed. This paper presents an approach to assessing the quality of metamodels. A quality model, which systematically characterizes and classifies quality attributes, and an operable measuring mechanism for effectively assessing the quality of metamodels based on the quality model, are pre- sented, using UML as the main example.
基金This work was supported by the National Key R&D Program of China(Grant No.2017YFD0400405)。
文摘Metamodels have been widely used as an alternative for expensive physical experiments or complex,time-consuming computational simulations to provide a fast but accurate analysis.However,challenge remains in the prior determination of the most suitable metamodel for a particular case because of the lack of information about the actual behavior of a system.In addition,existing studies on metamodels have largely restricted on solving deterministic problems(e.g.,data from finite element models),whereas some real-life engineering problems(e.g.,data from physical experiment)are stochastic problems with noisy data.In this work,a robust ensemble of metamodels(EMs)is proposed by combining three regression stand-alone metamodels in a weighted sum form.The weight factor is adaptively determined according to the hybrid error metric,which combines global and local error measures to improve the accuracy of the EMs.Furthermore,three typical individual metamodels that can filter noise are selected to construct the EMs to extend their application in practical engineering problems.Three well-known benchmark problems with different levels of noise and three engineering problems are used to verify the effectiveness of the proposed EMs.Results show that the proposed EMs have higher accuracy and robustness than the individual metamodels and other typical EMs in major cases.
基金supported by the Project of International Cooperation and Exchanges NSFC(Grant No.51861165202)the National Natural Science Foundation of China(Grant Nos.51575211,51705263,51805330)the 111 Project of China(Grant No.B16019).
文摘In fiber laser beam welding(LBW),the selection of optimal processing parameters is challenging and plays a key role in improving the bead geometry and welding quality.This study proposes a multi-objective optimization framework by combining an ensemble of metamodels(EMs)with the multi-objective artificial bee colony algorithm(MOABC)to identify the optimal welding parameters.An inverse proportional weighting method that considers the leave-one-out prediction error is presented to construct EM,which incorporates the competitive strengths of three metamodels.EM constructs the correlation between processing parameters(laser power,welding speed,and distance defocus)and bead geometries(bead width,depth of penetration,neck width,and neck depth)with average errors of 10.95%,7.04%,7.63%,and 8.62%,respectively.On the basis of EM,MOABC is employed to approximate the Pareto front,and verification experiments show that the relative errors are less than 14.67%.Furthermore,the main effect and the interaction effect of processing parameters on bead geometries are studied.Results demonstrate that the proposed EM-MOABC is effective in guiding actual fiber LBW applications.
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Supported by the National Natural Science Foundation of China under Grant No.60273026(国家自然科学基金)the National High-Tech Research and Development Plan of China under Grant No.2002AA116060(国家高技术研究发展计划(863))
文摘SPEM(software process engineering metamodel)是国际标准化组织制定的标准元模型,正日益成为软件过程建模领域的行业标准,但在过程执行方面,SPEM还存在不足.将软件过程看作是一种特殊的工作流,提出了一种应用工作流运行机制支持软件过程执行的方法.通过将SPEM模型转换为XPDL(XML process definition language)模型,利用XPDL引擎支持SPEM模型的执行.制定了SPEM和XPDL之间的映射规则,设计了转换算法并开发了转换引擎.该方法被应用在SoftPM项目中,成功地基于XPDL引擎Shark实现了对软件过程模型的执行支持.
文摘Usage of rolling contact bearings in variety of rotor-dynamic applications has put forth a need to develop a detailed and easy to implement techniques for the assessment of damage related features in these bearings so that before mechanical failure,maintenance actions can be planned well in advance.In accordance to this,a method based on dimensional amplitude response analysis and scaling laws is presented in this paper for the diagnosis of defects in different components of rolling contact bearings in a dimensionally scaled rotor-bearing system.Rotor,bearing,operating and defect parameters involved are detailed for dimensional analysis using frequency domain vibration data.A defect parameter for modeling all the three dimensions of the defect as well as the different shapes like square,circular,rectangular is put forth which takes into account the volume as well as the surface area of the defect.Experimental data set is generated for the‘model’bearing(designated as SKF30205J2/Q)using Box-Behnken design of response surface methodology for solution of the theoretical model by factorial regression approach.Obtained metamodel is then used for the prediction of the objective variable,i.e.,Vibration acceleration amplitude at the defect frequency component for other types of‘test’bearings(designated as SKF 30305C and SKF 22220 EK)using the developed scaling laws.Confirmation experiments showed that the computable relationship amongst objective variable and the dimensionless parameters can be forecast and correlated.
文摘Simulation and optimization are the key points of virtual product development (VPD). Traditional engineering simulation software and optimization methods are inadequate to analyze the optimization problems because of its computational inefficiency. A systematic design optimization strategy by using statistical methods and mathematical optimization technologies is proposed. This method extends the design of experiments (DOE) and the simulation metamodel technologies. Metamodels are built to in place of detailed simulation codes based on effectively DOE, and then be linked to optimization routines for fast analysis, or serve as a bridge for integrating simulation software across different domains. A design optimization of composite material structure is used to demonstrate the newly introduced methodology.
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.
基金supported by the National Natural Science Foundation of China(No.61627810)。
文摘Efficient experiment design is of great significance for the validation of simulation model with high nonlinearity and large input space.Excessive validation experiment raises the cost while insufficient test increases the risks of accepting an invalid model.In this paper,an adaptive sequential experiment design method combining global exploration criterion and local exploitation criterion is proposed.The exploration criterion utilizes discrepancy metric to improve the space-filling property of the design points while the exploitation criterion employs the leave one out error to discover informative points.To avoid the clustering of samples in the local region,an adaptive weight updating approach is provided to maintain the balance between exploration and exploitation.Besides,the credibility distribution function characterizing the relationship between the input and result credibility is introduced to support the model validation experiment design.Finally,six benchmark problems and an engineering case are applied to examine the performance of the proposed method.The experiments indicate that the proposed method achieves satisfactory performance for function approximation in accuracy and convergence.
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 the Project of Ministry of Education and Finance (No.200512)the Project of the State Key Laboratory of Ocean Engineering (GKZD010053-10)
文摘In this paper a hybrid process of modeling and optimization, which integrates a support vector machine （SVM） and genetic algorithm （GA）, was introduced to reduce the high time cost in structural optimization of ships. SVM, which is rooted in statistical learning theory and an approximate implementation of the method of structural risk minimization, can provide a good generalization performance in metamodeling the input-output relationship of real problems and consequently cuts down on high time cost in the analysis of real problems, such as FEM analysis. The GA, as a powerful optimization technique, possesses remarkable advantages for the problems that can hardly be optimized with common gradient-based optimization methods, which makes it suitable for optimizing models built by SVM. Based on the SVM-GA strategy, optimization of structural scantlings in the midship of a very large crude carrier （VLCC） ship was carried out according to the direct strength assessment method in common structural rules （CSR）, which eventually demonstrates the high efficiency of SVM-GA in optimizing the ship structural scantlings under heavy computational complexity. The time cost of this optimization with SVM-GA has been sharply reduced, many more loops have been processed within a small amount of time and the design has been improved remarkably.
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National Major Scientific Research Instrument Development Project of China(Grant No.81827804)Zhejiang Provincial Natural Science Foundation of China(Grant No.LSD19H180004)+1 种基金Science Fund for Creative Group of NSFC(Grant No.51821903)National Natural Science Foundation of China(Grant No.51665049).
文摘The simulation and planning system(SPS)requires accurate and real-time feedback regarding the deformation of soft tissues during the needle insertion procedure.Traditional mechanical-based models such as the finite element method(FEM)are widely used to compute the deformations of soft tissue.However,it is difficult for the FEM or other methods to find a balance between an acceptable image fidelity and real-time deformation feedback due to their complex material properties,geometries and interaction mechanisms.In this paper,a Kriging-based method is applied to model the soft tissue deformation to strike a balance between the accuracy and efficiency of deformation feedback.Four combinations of regression and correlation functions are compared regarding their ability to predict the maximum deformations of ten characteristic markers at a fixed insertion depth.The results suggest that a first order regression function with Gaussian correlation functions can best fit the results of the ground truth.The functional response of the Kriging-based method is utilized to model the dynamic deformations of markers at a series of needle insertion depths.The feasibility of the method is verified by investigating the adaptation to step variations.Compared with the ground truth of the finite element(FE)results,the maximum residual is less than 0.92 mm in the Y direction and 0.31 mm in the X direction.The results suggest that the Kriging metamodel provides real-time deformation feedback for a target and an obstacle to a SPS.
文摘Callovo-Oxfordian(COx)claystone has been considered as a potential host rock for geological radioactive waste disposal in France(Cigéo project).During the exploitation phase(100 years),the stability of drifts(e.g.galleries/alveoli)within the disposal is assured by the liner,which includes two layers:concrete arch segment and compressible material.The latter exhibits a significant deformation capacity(about 50%)under low stress(<3 MPa).Although the response of these underground structures can be governed by complex thermo-hydro-mechanical coupling,the creep behavior of COx claystone has been considered as the main factor controlling the increase of stress state in the concrete liner and hence the long-term stability of drifts.Therefore,by focusing only on the purely mechanical behavior,this study aims at investigating the uncertainty effect of the COx claystone time-dependent properties on the stability of an alveolus of Cigéo during the exploitation period.To describe the creep behavior of COx claystone,we use Lemaitre’s viscoplastic model with three parameters whose uncertainties are identified from laboratory creep tests.For the reliability analysis,an extension of a well-known Kriging metamodeling technique is proposed to assess the exceedance probability of acceptable stress in the concrete liner of the alveolus.The open-source code Code_Aster is chosen for the direct numerical evaluations of the performance function.The Kriging-based reliability analysis elucidates the effect of the uncertainty of COx claystone on the long-term stability of the concrete liner.Moreover,the role of the compressible material layer between the concrete liner and the host rock is also highlighted.
