Traditional Global Sensitivity Analysis（GSA） focuses on ranking inputs according to their contributions to the output uncertainty.However,information about how the specific regions inside an input affect the output ...Traditional Global Sensitivity Analysis（GSA） focuses on ranking inputs according to their contributions to the output uncertainty.However,information about how the specific regions inside an input affect the output is beyond the traditional GSA techniques.To fully address this issue,in this work,two regional moment-independent importance measures,Regional Importance Measure based on Probability Density Function（RIMPDF） and Regional Importance Measure based on Cumulative Distribution Function（RIMCDF）,are introduced to find out the contributions of specific regions of an input to the whole output distribution.The two regional importance measures prove to be reasonable supplements of the traditional GSA techniques.The ideas of RIMPDF and RIMCDF are applied in two engineering examples to demonstrate that the regional moment-independent importance analysis can add more information concerning the contributions of model inputs.展开更多
Due to the good balance between high efficiency and accuracy, meta-model based optimization algorithm is an important global optimization category and has been widely applied. To better solve the highly nonlinear and ...Due to the good balance between high efficiency and accuracy, meta-model based optimization algorithm is an important global optimization category and has been widely applied. To better solve the highly nonlinear and computation intensive en- gineering optimization problems, an enhanced hybrid and adaptive meta-model based global optimization （E-HAM） is first proposed in this work. Important region update method （IRU） and different sampling size strategies are proposed in the opti- mization method to enhance the performance. By applying self-moving and scaling strategy, the important region will be up- dated adaptively according to the search results to improve the resulting precision and convergence rate. Rough sampling strategy and intensive sampling strategy are applied at different stages of the optimization to improve the search efficiently and avoid results prematurely gathering in a small design space. The effectiveness of the new optimization algorithm is verified by comparing to six optimization methods with different variables bench mark optimization problems. The E-HAM optimization method is then applied to optimize the design parameters of the practical negative Poisson＇s ratio （NPR） crash box in this work. The results indicate that the proposed E-HAM has high accuracy and efficiency in optimizing the computation intensive prob- lems and can be widely used in engineering industry.展开更多
基金supported by the National Natural Science Foundation of China(No.NSFC51608446)the Fundamental Research Fund for Central Universities of China(No.3102016ZY015)
文摘Traditional Global Sensitivity Analysis（GSA） focuses on ranking inputs according to their contributions to the output uncertainty.However,information about how the specific regions inside an input affect the output is beyond the traditional GSA techniques.To fully address this issue,in this work,two regional moment-independent importance measures,Regional Importance Measure based on Probability Density Function（RIMPDF） and Regional Importance Measure based on Cumulative Distribution Function（RIMCDF）,are introduced to find out the contributions of specific regions of an input to the whole output distribution.The two regional importance measures prove to be reasonable supplements of the traditional GSA techniques.The ideas of RIMPDF and RIMCDF are applied in two engineering examples to demonstrate that the regional moment-independent importance analysis can add more information concerning the contributions of model inputs.
基金supported by the Research Project of State Key Laboratory of Mechanical System and Vibration(Grant Nos.MSV201507&MSV201606)the National Natural Science Foundation of China(Grant No.51375007)+3 种基金the Natural Science Foundation of Jiangsu Province(Grant No.SBK2015022352)the Fundamental Research Funds for the Central Universities(Grant No.NE2016002)the Open Fund Program of the State Key Laboratory of Vehicle Lightweight Design,P.R.China(Grant No.20130303)the Visiting Scholar Foundation of the State Key Lab of Mechanical Transmission in Chongqing University(Grant Nos.SKLMT-KFKT-2014010&SKLMT-KFKT-201507)
文摘Due to the good balance between high efficiency and accuracy, meta-model based optimization algorithm is an important global optimization category and has been widely applied. To better solve the highly nonlinear and computation intensive en- gineering optimization problems, an enhanced hybrid and adaptive meta-model based global optimization （E-HAM） is first proposed in this work. Important region update method （IRU） and different sampling size strategies are proposed in the opti- mization method to enhance the performance. By applying self-moving and scaling strategy, the important region will be up- dated adaptively according to the search results to improve the resulting precision and convergence rate. Rough sampling strategy and intensive sampling strategy are applied at different stages of the optimization to improve the search efficiently and avoid results prematurely gathering in a small design space. The effectiveness of the new optimization algorithm is verified by comparing to six optimization methods with different variables bench mark optimization problems. The E-HAM optimization method is then applied to optimize the design parameters of the practical negative Poisson＇s ratio （NPR） crash box in this work. The results indicate that the proposed E-HAM has high accuracy and efficiency in optimizing the computation intensive prob- lems and can be widely used in engineering industry.
