Parameter identification, model calibration, and uncertainty quantification are important steps in the model-building process, and are necessary for obtaining credible results and valuable information. Sensitivity ana...Parameter identification, model calibration, and uncertainty quantification are important steps in the model-building process, and are necessary for obtaining credible results and valuable information. Sensitivity analysis of hydrological model is a key step in model uncertainty quantification, which can identify the dominant parameters, reduce the model calibration uncertainty, and enhance the model optimization efficiency. There are, however, some shortcomings in classical approaches, including the long duration of time and high computation cost required to quantitatively assess the sensitivity of a multiple-parameter hydrological model. For this reason, a two-step statistical evaluation framework using global techniques is presented. It is based on （1） a screening method （Morris） for qualitative ranking of parameters, and （2） a variance-based method integrated with a meta-model for quantitative sensitivity analysis, i.e., the Sobol method integrated with the response surface model （RSMSobol）. First, the Morris screening method was used to qualitatively identify the parameters＇ sensitivity, and then ten parameters were selected to quantify the sensitivity indices. Subsequently, the RSMSobol method was used to quantify the sensitivity, i.e., the first-order and total sensitivity indices based on the response surface model （RSM） were calculated. The RSMSobol method can not only quantify the sensitivity, but also reduce the computational cost, with good accuracy compared to the classical approaches. This approach will be effective and reliable in the global sensitivity analysis of a complex large-scale distributed hydrological model.展开更多
Conventional trajectory optimization techniques have been challenged by their inability to handle threats with irregular shapes and the tendency to be sensitive to control variations of aircraft. Aiming to overcome th...Conventional trajectory optimization techniques have been challenged by their inability to handle threats with irregular shapes and the tendency to be sensitive to control variations of aircraft. Aiming to overcome these difficulties, this paper presents an alternative approach for trajectory optimization, where the problem is formulated into a parametric optimization of the maneuver variables under a tactics template framework. To reduce the size of the problem, global sensitivity analysis （GSA） is performed to identify the less-influential maneuver variables. The probability collectives （PC） algorithm, which is well-suited to discrete and discontinuous optimization, is applied to solve the trajectory optimization problem. The robustness of the trajectory is assessed through multiple sampling around the chosen values of the maneuver variables. Meta-models based on radius basis function （RBF） are created for evaluations of the means and deviations of the problem objectives and constraints. To guarantee the approximation accuracy, the meta-models are adaptively updated during optimization. The proposed approach is demonstrated on a typical airground attack mission scenario. Results reveal that the proposed approach is capable of generating robust and optimal trajectories with both accuracy and efficiency.展开更多
In this study, a new method for conversion of solid finite element solution to beam finite element solution is developed based on the meta-modeling theory which constructs a model consistent with continuum mechanics. ...In this study, a new method for conversion of solid finite element solution to beam finite element solution is developed based on the meta-modeling theory which constructs a model consistent with continuum mechanics. The proposed method is rigorous and efficient compared to a typical conversion method which merely computes surface integration of solid element nodal stresses to obtain cross-sectional forces. The meta-modeling theory ensures the rigorousness of proposed method by defining a proper distance between beam element and solid element solutions in a function space of continuum mechanics. Results of numerical verification test that is conducted with a simple cantilever beam are used to find the proper distance function for this conversion. Time history analysis of the main tunnel structure of a real ramp tunnel is considered as a numerical example for the proposed conversion method. It is shown that cross-sectional forces are readily computed for solid element solution of the main tunnel structure when it is converted to a beam element solution using the proposed method. Further, envelopes of resultant forces which are of primary importance for the purpose of design, are developed for a given ground motion at the end.展开更多
To investigate the application of meta-model for finite element( FE) model updating of structures,the performance of two popular meta-model,i. e.,Kriging model and response surface model( RSM),were compared in detail....To investigate the application of meta-model for finite element( FE) model updating of structures,the performance of two popular meta-model,i. e.,Kriging model and response surface model( RSM),were compared in detail. Firstly,above two kinds of meta-model were introduced briefly. Secondly,some key issues of the application of meta-model to FE model updating of structures were proposed and discussed,and then some advices were presented in order to select a reasonable meta-model for the purpose of updating the FE model of structures. Finally,the procedure of FE model updating based on meta-model was implemented by updating the FE model of a truss bridge model with the measured modal parameters. The results showed that the Kriging model was more proper for FE model updating of complex structures.展开更多
In this work,a hybrid meta-model based design space differentiation（HMDSD）method is proposed for practical problems.In the proposed method,an iteratively reduced promising region is constructed using the expensive p...In this work,a hybrid meta-model based design space differentiation（HMDSD）method is proposed for practical problems.In the proposed method,an iteratively reduced promising region is constructed using the expensive points,with two different search strategies respectively applied inside and outside the promising region.Besides,the hybrid meta-model strategy applied in the search process makes it possible to solve the complex practical problems.Tested upon a serial of benchmark math functions,the HMDSD method shows great efficiency and search accuracy.On top of that,a practical lightweight design demonstrates its superior performance.展开更多
Neural networks are being used to construct meta-models in numerical simulation of structures.In addition to network structures and training algorithms,training samples also greatly affect the accuracy of neural netwo...Neural networks are being used to construct meta-models in numerical simulation of structures.In addition to network structures and training algorithms,training samples also greatly affect the accuracy of neural network models.In this paper,some existing main sampling techniques are evaluated,including techniques based on experimental design theory, random selection,and rotating sampling.First,advantages and disadvantages of each technique are reviewed.Then,seven techniques are used to generate samples for training radial neural networks models for two benchmarks:an antenna model and an aircraft model.Results show that the uniform design,in which the number of samples and mean square error network models are considered,is the best sampling technique for neural network based meta-model building.展开更多
With increasing design demands of turbomachinery,stochastic flutter behavior has become more prominent and even appears a hazard to reliability and safety.Stochastic flutter assessment is an effective measure to quant...With increasing design demands of turbomachinery,stochastic flutter behavior has become more prominent and even appears a hazard to reliability and safety.Stochastic flutter assessment is an effective measure to quantify the failure risk and improve aeroelastic stability.However,for complex turbomachinery with multiple dynamic influencing factors(i.e.,aeroengine compressor with time-variant loads),the stochastic flutter assessment is hard to be achieved effectively,since large deviations and inefficient computing will be incurred no matter considering influencing factors at a certain instant or the whole time domain.To improve the assessing efficiency and accuracy of stochastic flutter behavior,a dynamic meta-modeling approach(termed BA-DWTR)is presented with the integration of bat algorithm(BA)and dynamic wavelet tube regression(DWTR).The stochastic flutter assessment of a typical compressor blade is considered as one case to evaluate the proposed approach with respect to condition variabilities and load fluctuations.The evaluation results reveal that the compressor blade has 0.95% probability to induce flutter failure when operating 100% rotative rate at t=170 s.The total temperature at rotor inlet and dynamic operating loads(vibrating frequency and rotative rate)are the primary sensitive parameters on flutter failure probability.Bymethod comparisons,the presented approach is validated to possess high-accuracy and highefficiency in assessing the stochastic flutter behavior for turbomachinery.展开更多
Our research focuses on creating a meta-model for generating a web mapping application. It was difficult for non-geomatics developers to implement a webmapping application. Indeed, this type of application uses geospa...Our research focuses on creating a meta-model for generating a web mapping application. It was difficult for non-geomatics developers to implement a webmapping application. Indeed, this type of application uses geospatial data that require geomatics skills. For this reason, in order to help non-geomatics developers to set up a webmapping application, we have designed a meta-model that automatically generates a webmapping application using model-driven engineering. The created meta-model is used by non-geomatics developers to explicitly write the concrete syntax specific to the webmapping application using the xtext tool. This concrete syntax is automatically converted into source code using the xtend tool without the intervention of the non-geomatics developers.展开更多
In a context where urban satellite image processing technologies are undergoing rapid evolution,this article presents an innovative and rigorous approach to satellite image classification applied to urban planning.Thi...In a context where urban satellite image processing technologies are undergoing rapid evolution,this article presents an innovative and rigorous approach to satellite image classification applied to urban planning.This research proposes an integrated methodological framework,based on the principles of model-driven engineering(MDE),to transform a generic meta-model into a meta-model specifically dedicated to urban satellite image classification.We implemented this transformation using the Atlas Transformation Language(ATL),guaranteeing a smooth and consistent transition from platform-independent model(PIM)to platform-specific model(PSM),according to the principles of model-driven architecture(MDA).The application of this IDM methodology enables advanced structuring of satellite data for targeted urban planning analyses,making it possible to classify various urban zones such as built-up,cultivated,arid and water areas.The novelty of this approach lies in the automation and standardization of the classification process,which significantly reduces the need for manual intervention,and thus improves the reliability,reproducibility and efficiency of urban data analysis.By adopting this method,decision-makers and urban planners are provided with a powerful tool for systematically and consistently analyzing and interpreting satellite images,facilitating decision-making in critical areas such as urban space management,infrastructure planning and environmental preservation.展开更多
载人航天器研制过程中,人因要素在早期阶段融入设计仍有待提升,且常规的基于模型的系统工程(model based systems engineering,MBSE)体系缺少将人与系统其余部分进行整合的充分考虑,导致开发迭代周期变长,也大幅增加了研制成本。针对这...载人航天器研制过程中,人因要素在早期阶段融入设计仍有待提升,且常规的基于模型的系统工程(model based systems engineering,MBSE)体系缺少将人与系统其余部分进行整合的充分考虑,导致开发迭代周期变长,也大幅增加了研制成本。针对这一问题,提出载人月球探测任务人因领域元模型构建方法,在人-系统整合的框架下,采用MBSE将人因需求整合至载人航天器的开发过程中,并基于系统建模语言SysML建立人因领域元模型,以实现在载人月球探测产品开发的全生命周期中融入人因需求,为产品的规划、设计和开发提供支持,有效减少研制中出现人因设计问题,降低研制成本。通过载人月球探测任务的典型案例进行建模,验证人因领域元模型建立方法的有效性,为类似系统设计的MBSE扩展应用提供参考。展开更多
Monitoring of the earth’s surface has been significantly improved thanks to optical remote sensing by satellites such as SPOT,Landsat and Sentinel-2,which produce vast datasets.The processing of this data,often refer...Monitoring of the earth’s surface has been significantly improved thanks to optical remote sensing by satellites such as SPOT,Landsat and Sentinel-2,which produce vast datasets.The processing of this data,often referred to as Big Data,is essential for decision-making,requiring the application of advanced algorithms to analyze changes in land cover.In the age of artificial intelligence,supervised machine learning algorithms are widely used,although their application in urban contexts remains complex.Researchers have to evaluate and tune various algorithms according to assumptions and experiments,which requires time and resources.This paper presents a meta-modeling approach for urban satellite image classification,using model-driven engineering techniques.The aim is to provide urban planners with standardized solutions for geospatial processing,promoting reusability and interoperability.Formalization includes the creation of a knowledge base and the modeling of processing chains to analyze land use.展开更多
为解决基于深度强化学习的AUV跟踪控制器在面临新任务时需从零开始训练、训练速度慢、稳定性差等问题,设计一种基于元强化学习的AUV多任务快速自适应控制算法——R-SAC(Reptile-Soft Actor Critic)算法。R-SAC算法将元学习与强化学习相...为解决基于深度强化学习的AUV跟踪控制器在面临新任务时需从零开始训练、训练速度慢、稳定性差等问题,设计一种基于元强化学习的AUV多任务快速自适应控制算法——R-SAC(Reptile-Soft Actor Critic)算法。R-SAC算法将元学习与强化学习相结合,结合水下机器人运动学及动力学方程对跟踪任务进行建模,利用RSAC算法在训练阶段为AUV跟踪控制器获得一组最优初始值模型参数,使模型在面临不同的任务时,基于该组参数进行训练时能够快速收敛,实现快速自适应不同任务。仿真结果表明,所提出的方法与随机初始化强化学习控制器相比,收敛速度最低提高了1.6倍,跟踪误差保持在2.8%以内。展开更多
基金supported by the National Natural Science Foundation of China (Grant No. 41271003)the National Basic Research Program of China (Grants No. 2010CB428403 and 2010CB951103)
文摘Parameter identification, model calibration, and uncertainty quantification are important steps in the model-building process, and are necessary for obtaining credible results and valuable information. Sensitivity analysis of hydrological model is a key step in model uncertainty quantification, which can identify the dominant parameters, reduce the model calibration uncertainty, and enhance the model optimization efficiency. There are, however, some shortcomings in classical approaches, including the long duration of time and high computation cost required to quantitatively assess the sensitivity of a multiple-parameter hydrological model. For this reason, a two-step statistical evaluation framework using global techniques is presented. It is based on （1） a screening method （Morris） for qualitative ranking of parameters, and （2） a variance-based method integrated with a meta-model for quantitative sensitivity analysis, i.e., the Sobol method integrated with the response surface model （RSMSobol）. First, the Morris screening method was used to qualitatively identify the parameters＇ sensitivity, and then ten parameters were selected to quantify the sensitivity indices. Subsequently, the RSMSobol method was used to quantify the sensitivity, i.e., the first-order and total sensitivity indices based on the response surface model （RSM） were calculated. The RSMSobol method can not only quantify the sensitivity, but also reduce the computational cost, with good accuracy compared to the classical approaches. This approach will be effective and reliable in the global sensitivity analysis of a complex large-scale distributed hydrological model.
基金supported by Open Research Foundation of Science and Technology on Aerospace Flight Dynamics Laboratory (No. 2012afd1010)
文摘Conventional trajectory optimization techniques have been challenged by their inability to handle threats with irregular shapes and the tendency to be sensitive to control variations of aircraft. Aiming to overcome these difficulties, this paper presents an alternative approach for trajectory optimization, where the problem is formulated into a parametric optimization of the maneuver variables under a tactics template framework. To reduce the size of the problem, global sensitivity analysis （GSA） is performed to identify the less-influential maneuver variables. The probability collectives （PC） algorithm, which is well-suited to discrete and discontinuous optimization, is applied to solve the trajectory optimization problem. The robustness of the trajectory is assessed through multiple sampling around the chosen values of the maneuver variables. Meta-models based on radius basis function （RBF） are created for evaluations of the means and deviations of the problem objectives and constraints. To guarantee the approximation accuracy, the meta-models are adaptively updated during optimization. The proposed approach is demonstrated on a typical airground attack mission scenario. Results reveal that the proposed approach is capable of generating robust and optimal trajectories with both accuracy and efficiency.
文摘In this study, a new method for conversion of solid finite element solution to beam finite element solution is developed based on the meta-modeling theory which constructs a model consistent with continuum mechanics. The proposed method is rigorous and efficient compared to a typical conversion method which merely computes surface integration of solid element nodal stresses to obtain cross-sectional forces. The meta-modeling theory ensures the rigorousness of proposed method by defining a proper distance between beam element and solid element solutions in a function space of continuum mechanics. Results of numerical verification test that is conducted with a simple cantilever beam are used to find the proper distance function for this conversion. Time history analysis of the main tunnel structure of a real ramp tunnel is considered as a numerical example for the proposed conversion method. It is shown that cross-sectional forces are readily computed for solid element solution of the main tunnel structure when it is converted to a beam element solution using the proposed method. Further, envelopes of resultant forces which are of primary importance for the purpose of design, are developed for a given ground motion at the end.
基金Sponsored by the National Key Technology Research and Development Program of China(Grant No.2011BAK02B02)
文摘To investigate the application of meta-model for finite element( FE) model updating of structures,the performance of two popular meta-model,i. e.,Kriging model and response surface model( RSM),were compared in detail. Firstly,above two kinds of meta-model were introduced briefly. Secondly,some key issues of the application of meta-model to FE model updating of structures were proposed and discussed,and then some advices were presented in order to select a reasonable meta-model for the purpose of updating the FE model of structures. Finally,the procedure of FE model updating based on meta-model was implemented by updating the FE model of a truss bridge model with the measured modal parameters. The results showed that the Kriging model was more proper for FE model updating of complex structures.
基金Project supported by the Plan for the growth of young teachers,the National Natural Science Foundation of China(No.51505138)the National 973 Program of China(No.2010CB328005)+1 种基金Outstanding Youth Foundation of NSFC(No.50625519)Program for Changjiang Scholars
文摘In this work,a hybrid meta-model based design space differentiation（HMDSD）method is proposed for practical problems.In the proposed method,an iteratively reduced promising region is constructed using the expensive points,with two different search strategies respectively applied inside and outside the promising region.Besides,the hybrid meta-model strategy applied in the search process makes it possible to solve the complex practical problems.Tested upon a serial of benchmark math functions,the HMDSD method shows great efficiency and search accuracy.On top of that,a practical lightweight design demonstrates its superior performance.
基金Specialized Research Fund for the Doctoral Program of Higher Education,China (No.20010227012)
文摘Neural networks are being used to construct meta-models in numerical simulation of structures.In addition to network structures and training algorithms,training samples also greatly affect the accuracy of neural network models.In this paper,some existing main sampling techniques are evaluated,including techniques based on experimental design theory, random selection,and rotating sampling.First,advantages and disadvantages of each technique are reviewed.Then,seven techniques are used to generate samples for training radial neural networks models for two benchmarks:an antenna model and an aircraft model.Results show that the uniform design,in which the number of samples and mean square error network models are considered,is the best sampling technique for neural network based meta-model building.
基金co-supported by the National Natural Science Foundation of China(Grants 51975028 and 52105136)China Postdoctoral Science Foundation(Grant 2021M690290)the National Science and TechnologyMajor Project(Grant J2019-Ⅳ-0016-0084).
文摘With increasing design demands of turbomachinery,stochastic flutter behavior has become more prominent and even appears a hazard to reliability and safety.Stochastic flutter assessment is an effective measure to quantify the failure risk and improve aeroelastic stability.However,for complex turbomachinery with multiple dynamic influencing factors(i.e.,aeroengine compressor with time-variant loads),the stochastic flutter assessment is hard to be achieved effectively,since large deviations and inefficient computing will be incurred no matter considering influencing factors at a certain instant or the whole time domain.To improve the assessing efficiency and accuracy of stochastic flutter behavior,a dynamic meta-modeling approach(termed BA-DWTR)is presented with the integration of bat algorithm(BA)and dynamic wavelet tube regression(DWTR).The stochastic flutter assessment of a typical compressor blade is considered as one case to evaluate the proposed approach with respect to condition variabilities and load fluctuations.The evaluation results reveal that the compressor blade has 0.95% probability to induce flutter failure when operating 100% rotative rate at t=170 s.The total temperature at rotor inlet and dynamic operating loads(vibrating frequency and rotative rate)are the primary sensitive parameters on flutter failure probability.Bymethod comparisons,the presented approach is validated to possess high-accuracy and highefficiency in assessing the stochastic flutter behavior for turbomachinery.
文摘Our research focuses on creating a meta-model for generating a web mapping application. It was difficult for non-geomatics developers to implement a webmapping application. Indeed, this type of application uses geospatial data that require geomatics skills. For this reason, in order to help non-geomatics developers to set up a webmapping application, we have designed a meta-model that automatically generates a webmapping application using model-driven engineering. The created meta-model is used by non-geomatics developers to explicitly write the concrete syntax specific to the webmapping application using the xtext tool. This concrete syntax is automatically converted into source code using the xtend tool without the intervention of the non-geomatics developers.
文摘In a context where urban satellite image processing technologies are undergoing rapid evolution,this article presents an innovative and rigorous approach to satellite image classification applied to urban planning.This research proposes an integrated methodological framework,based on the principles of model-driven engineering(MDE),to transform a generic meta-model into a meta-model specifically dedicated to urban satellite image classification.We implemented this transformation using the Atlas Transformation Language(ATL),guaranteeing a smooth and consistent transition from platform-independent model(PIM)to platform-specific model(PSM),according to the principles of model-driven architecture(MDA).The application of this IDM methodology enables advanced structuring of satellite data for targeted urban planning analyses,making it possible to classify various urban zones such as built-up,cultivated,arid and water areas.The novelty of this approach lies in the automation and standardization of the classification process,which significantly reduces the need for manual intervention,and thus improves the reliability,reproducibility and efficiency of urban data analysis.By adopting this method,decision-makers and urban planners are provided with a powerful tool for systematically and consistently analyzing and interpreting satellite images,facilitating decision-making in critical areas such as urban space management,infrastructure planning and environmental preservation.
文摘载人航天器研制过程中,人因要素在早期阶段融入设计仍有待提升,且常规的基于模型的系统工程(model based systems engineering,MBSE)体系缺少将人与系统其余部分进行整合的充分考虑,导致开发迭代周期变长,也大幅增加了研制成本。针对这一问题,提出载人月球探测任务人因领域元模型构建方法,在人-系统整合的框架下,采用MBSE将人因需求整合至载人航天器的开发过程中,并基于系统建模语言SysML建立人因领域元模型,以实现在载人月球探测产品开发的全生命周期中融入人因需求,为产品的规划、设计和开发提供支持,有效减少研制中出现人因设计问题,降低研制成本。通过载人月球探测任务的典型案例进行建模,验证人因领域元模型建立方法的有效性,为类似系统设计的MBSE扩展应用提供参考。
文摘Monitoring of the earth’s surface has been significantly improved thanks to optical remote sensing by satellites such as SPOT,Landsat and Sentinel-2,which produce vast datasets.The processing of this data,often referred to as Big Data,is essential for decision-making,requiring the application of advanced algorithms to analyze changes in land cover.In the age of artificial intelligence,supervised machine learning algorithms are widely used,although their application in urban contexts remains complex.Researchers have to evaluate and tune various algorithms according to assumptions and experiments,which requires time and resources.This paper presents a meta-modeling approach for urban satellite image classification,using model-driven engineering techniques.The aim is to provide urban planners with standardized solutions for geospatial processing,promoting reusability and interoperability.Formalization includes the creation of a knowledge base and the modeling of processing chains to analyze land use.
文摘为解决基于深度强化学习的AUV跟踪控制器在面临新任务时需从零开始训练、训练速度慢、稳定性差等问题,设计一种基于元强化学习的AUV多任务快速自适应控制算法——R-SAC(Reptile-Soft Actor Critic)算法。R-SAC算法将元学习与强化学习相结合,结合水下机器人运动学及动力学方程对跟踪任务进行建模,利用RSAC算法在训练阶段为AUV跟踪控制器获得一组最优初始值模型参数,使模型在面临不同的任务时,基于该组参数进行训练时能够快速收敛,实现快速自适应不同任务。仿真结果表明,所提出的方法与随机初始化强化学习控制器相比,收敛速度最低提高了1.6倍,跟踪误差保持在2.8%以内。
