With the rapid development of modern wireless communications and radar, antennas and arrays are becoming more complex, therein having, e.g., more degrees of design freedom, integration and fabrication constraints and ...With the rapid development of modern wireless communications and radar, antennas and arrays are becoming more complex, therein having, e.g., more degrees of design freedom, integration and fabrication constraints and design objectives. While fullwave electromagnetic simulation can be very accurate and therefore essential to the design process, it is also very time consuming, which leads to many challenges for antenna design, optimization and sensitivity analysis(SA). Recently, machine-learning-assisted optimization(MLAO) has been widely introduced to accelerate the design process of antennas and arrays. Machine learning(ML) methods, including Gaussian process regression, support vector machine(SVM) and artificial neural networks(ANNs), have been applied to build surrogate models of antennas to achieve fast response prediction. With the help of these ML methods, various MLAO algorithms have been proposed for different applications. A comprehensive survey of recent advances in ML methods for antenna modeling is first presented. Then, algorithms for ML-assisted antenna design, including optimization and SA, are reviewed. Finally, some challenges facing future MLAO for antenna design are discussed.展开更多
This paper proposes a hybrid architecture based on Multi-disciplinary Design Optimization(MDO) with the Variable Complexity Modeling(VCM) method, to solve the problem of general design optimization for a stratosphere ...This paper proposes a hybrid architecture based on Multi-disciplinary Design Optimization(MDO) with the Variable Complexity Modeling(VCM) method, to solve the problem of general design optimization for a stratosphere airship. Firstly, MDO based on the Concurrent SubSpace Optimization(CSSO) strategy is improved for handling the subsystem coupling problem in stratosphere airship design which contains aerodynamics, structure, and energy. Secondly, the VCM method based on the surrogate model is presented for reducing the computational complexity in high-fidelity modeling without loss of accuracy. Moreover, the global-to-local optimization strategy is added to the architecture to enhance the process. Finally, the result gives a prominent stratosphere airship general solution that validates the feasibility and efficiency of the optimization architecture. Besides, a sensitivity analysis is conducted to outline the critical impact upon stratosphere airship design.展开更多
In the face of the pressing environmental issues,the past decade witnessed the booming development of the distributed energy systems(DESs).A notable problem of DESs is the inevitable uncertainty that may make DESs dev...In the face of the pressing environmental issues,the past decade witnessed the booming development of the distributed energy systems(DESs).A notable problem of DESs is the inevitable uncertainty that may make DESs deviate significantly from the deterministically obtained expectations,in both aspects of optimal design and economic operation.It thus necessitates the sensitivity analysis to quantify the impacts of the massive parametric uncertainties.This paper aims to give a comprehensive quantification,and carries out a multi-stage sensitivity analysis on DESs from the perspectives of evaluation criteria,optimal design and economic operation.First,a mathematical model of a DES is developed to present the solutions to the three stages of the DES.Second,the Monte-Carlo simulation is carried out subject to the probabilistic distributions of the energy,technical and economic parameters.Based on the simulation results,the variance-based Sobol method is applied to calculate the individual importance,interactional importance and total importance of various parameters.The comparison of the multi-stage results shows that only a few parameters play critical roles while the uncertainty of most of the massive parameters has little impact on the system performance.In addition,the influence of parameter interactions in the optimal design stage are much stronger than that in the evaluation criteria and operation strategy stages.展开更多
在汽车车身概念设计阶段,针对轻量化设计及优化白车身刚度问题,建立了某款纯电动铝合金汽车车身骨架基于真实接头的简化力学模型。运用灵敏度分析筛选出灵敏度较大部件,然后利用元模型的优化方法(hybrid and adaptive meta-modeling met...在汽车车身概念设计阶段,针对轻量化设计及优化白车身刚度问题,建立了某款纯电动铝合金汽车车身骨架基于真实接头的简化力学模型。运用灵敏度分析筛选出灵敏度较大部件,然后利用元模型的优化方法(hybrid and adaptive meta-modeling method,HAM)对灵敏度较大部件进行截面厚度优化,使优化目标车身质量得到了降低,同时改善了车身的模态和刚度,根据研究的数据制造了电动车车身。展开更多
基金supported in part by the National Key R&D Program of China under grant 2018YFB1801101the National Natural Science Foundation of China under grants 61671145 and 61960206006the Key R&D Program of Jiangsu Province of China under grant BE2018121.
文摘With the rapid development of modern wireless communications and radar, antennas and arrays are becoming more complex, therein having, e.g., more degrees of design freedom, integration and fabrication constraints and design objectives. While fullwave electromagnetic simulation can be very accurate and therefore essential to the design process, it is also very time consuming, which leads to many challenges for antenna design, optimization and sensitivity analysis(SA). Recently, machine-learning-assisted optimization(MLAO) has been widely introduced to accelerate the design process of antennas and arrays. Machine learning(ML) methods, including Gaussian process regression, support vector machine(SVM) and artificial neural networks(ANNs), have been applied to build surrogate models of antennas to achieve fast response prediction. With the help of these ML methods, various MLAO algorithms have been proposed for different applications. A comprehensive survey of recent advances in ML methods for antenna modeling is first presented. Then, algorithms for ML-assisted antenna design, including optimization and SA, are reviewed. Finally, some challenges facing future MLAO for antenna design are discussed.
基金supported in part by the National Key R&D Program of China(No.2016YFB1200100)
文摘This paper proposes a hybrid architecture based on Multi-disciplinary Design Optimization(MDO) with the Variable Complexity Modeling(VCM) method, to solve the problem of general design optimization for a stratosphere airship. Firstly, MDO based on the Concurrent SubSpace Optimization(CSSO) strategy is improved for handling the subsystem coupling problem in stratosphere airship design which contains aerodynamics, structure, and energy. Secondly, the VCM method based on the surrogate model is presented for reducing the computational complexity in high-fidelity modeling without loss of accuracy. Moreover, the global-to-local optimization strategy is added to the architecture to enhance the process. Finally, the result gives a prominent stratosphere airship general solution that validates the feasibility and efficiency of the optimization architecture. Besides, a sensitivity analysis is conducted to outline the critical impact upon stratosphere airship design.
基金supported by National Natural Science Foundation of China(No.51936003)National Key Research and Development Program of China(No.2018YFB1502904)
文摘In the face of the pressing environmental issues,the past decade witnessed the booming development of the distributed energy systems(DESs).A notable problem of DESs is the inevitable uncertainty that may make DESs deviate significantly from the deterministically obtained expectations,in both aspects of optimal design and economic operation.It thus necessitates the sensitivity analysis to quantify the impacts of the massive parametric uncertainties.This paper aims to give a comprehensive quantification,and carries out a multi-stage sensitivity analysis on DESs from the perspectives of evaluation criteria,optimal design and economic operation.First,a mathematical model of a DES is developed to present the solutions to the three stages of the DES.Second,the Monte-Carlo simulation is carried out subject to the probabilistic distributions of the energy,technical and economic parameters.Based on the simulation results,the variance-based Sobol method is applied to calculate the individual importance,interactional importance and total importance of various parameters.The comparison of the multi-stage results shows that only a few parameters play critical roles while the uncertainty of most of the massive parameters has little impact on the system performance.In addition,the influence of parameter interactions in the optimal design stage are much stronger than that in the evaluation criteria and operation strategy stages.
文摘在汽车车身概念设计阶段,针对轻量化设计及优化白车身刚度问题,建立了某款纯电动铝合金汽车车身骨架基于真实接头的简化力学模型。运用灵敏度分析筛选出灵敏度较大部件,然后利用元模型的优化方法(hybrid and adaptive meta-modeling method,HAM)对灵敏度较大部件进行截面厚度优化,使优化目标车身质量得到了降低,同时改善了车身的模态和刚度,根据研究的数据制造了电动车车身。
