汽車乘員約束系統(tǒng)多參數(shù)優(yōu)化理論及方法研究
發(fā)布時間：2018-12-08 15:55
【摘要】：本課題得到國家自然科學(xué)基金項目(51275164)“基于全局敏感性分析和擬合基函數(shù)代理模型的汽車碰撞人體損傷穩(wěn)健優(yōu)化”資助。 汽車乘員約束系統(tǒng)設(shè)計是汽車安全性設(shè)計的重要內(nèi)容。在乘員約束系統(tǒng)的研究開發(fā)中，采用計算機模擬和優(yōu)化設(shè)計相結(jié)合的方法，可以有效縮短產(chǎn)品研發(fā)周期并提高產(chǎn)品性能。汽車碰撞是一個復(fù)雜的多參數(shù)影響的系統(tǒng)響應(yīng)，碰撞過程中的乘員損傷除了與安全帶、安全氣囊等約束裝置的性能直接相關(guān)外，，還與邊界條件如車體碰撞加速度和乘員本身的乘坐位置等相關(guān)。隨著系統(tǒng)參數(shù)個數(shù)的增加，系統(tǒng)的優(yōu)化設(shè)計計算成本急劇上升。因此如何在概念設(shè)計階段識別出高度非線性系統(tǒng)中的重要參數(shù)，實現(xiàn)快速尋優(yōu)，對于提高車輛的安全性能并縮短研發(fā)周期具有重要的意義。 針對上述問題，本文提出一種多參數(shù)條件下復(fù)雜非線性系統(tǒng)優(yōu)化設(shè)計策略。以汽車100％正面碰撞下的汽車乘員約束系統(tǒng)為研究對象，選取碰撞波形以及安全帶，安全氣囊，內(nèi)飾總成的20個參數(shù)作為優(yōu)化設(shè)計變量。針對系統(tǒng)多參數(shù)特點，采用了基于變量分組的全局敏感性分析方法，將乘員約束系統(tǒng)設(shè)計變量按照總成相關(guān)性分為一組，分別對各組變量進行全局敏感性分析，通過計算每個變量對系統(tǒng)響應(yīng)總方差的貢獻度來評估參數(shù)的重要性。分析過程中采用描述性蒙特卡羅模擬在整個設(shè)計空間內(nèi)采樣計算，以元模型代替仿真模型來完成設(shè)計參數(shù)的敏感性分析。將分析獲得的信息用于混合元模型全局優(yōu)化算法(HybridandadaptivemetamodelingMethod,HAM)，將二階多項式響應(yīng)面、Kriging模型、徑向基函數(shù)三種元模型有機結(jié)合，自適應(yīng)選擇最佳的元模型進行尋優(yōu)。算法在搜索過程中通過有規(guī)律的選取一定數(shù)量且函數(shù)值趨向最優(yōu)解的樣本點對元模型不斷更新與重建，同時根據(jù)樣本點函數(shù)值排序?qū)υO(shè)計空間進行分區(qū)，構(gòu)建重點區(qū)域并通過采樣逐漸提高重點區(qū)域的精度。最后考慮元模型可能存在的擬合誤差，在所有樣本點中選擇函數(shù)值較小的三個樣本點構(gòu)建關(guān)鍵空間，通過元模型在關(guān)鍵空間內(nèi)搜索全局最優(yōu)解，最終完成系統(tǒng)的優(yōu)化設(shè)計。 論文研究結(jié)果表明：在包含多參數(shù)的汽車乘員約束系統(tǒng)的優(yōu)化設(shè)計中，本文提出的基于全局敏感性分析和混合元模型優(yōu)化的策略是十分有效的。通過基于方差的全局敏感性能夠快速識別系統(tǒng)中的重要參數(shù)，同時混合元模型全局優(yōu)化算法打破了單一元模型的局限性，能夠快速、經(jīng)濟、準(zhǔn)確地解決汽車乘員約束系統(tǒng)尋優(yōu)的難題。同時也為復(fù)雜非線性系統(tǒng)的優(yōu)化求解提供了很好的借鑒。
[Abstract]:The project is supported by the National Natural Science Foundation of China (51275164), "robust optimization of human body damage in vehicle collisions based on global sensitivity analysis and fitting basis function agent model". The design of vehicle occupant restraint system is an important content of automobile safety design. In the research and development of the passenger restraint system, the combination of computer simulation and optimal design can effectively shorten the product development cycle and improve the product performance. Vehicle crash is a complex multi-parameter impact system response. The occupant damage during the collision process is directly related to the performance of seatbelts, airbags and other restraints. It is also related to the boundary conditions such as the acceleration of the collision of the car body and the seat of the occupant himself. With the increase of the number of system parameters, the calculation cost of optimal design of the system increases sharply. Therefore, how to identify the important parameters of the highly nonlinear system in the conceptual design stage and realize the rapid optimization is of great significance to improve the safety performance of the vehicle and shorten the research and development period. In order to solve the above problems, this paper presents an optimal design strategy for complex nonlinear systems with multiple parameters. Taking the vehicle occupant restraint system under 100% frontal impact as the research object, the impact waveform and 20 parameters of the safety belt, airbag and interior assembly are selected as the optimal design variables. According to the multi-parameter characteristics of the system, a global sensitivity analysis method based on the grouping of variables is adopted. The design variables of the passenger constrained system are divided into a group according to the assembly correlation, and the global sensitivity analysis of each group of variables is carried out respectively. The importance of the parameters is evaluated by calculating the contribution of each variable to the total variance of the system response. In the process of analysis, descriptive Monte Carlo simulation is used to sample the whole design space and metamodel is used instead of the simulation model to complete the sensitivity analysis of design parameters. The information obtained from the analysis is used in the hybrid element model global optimization algorithm (HybridandadaptivemetamodelingMethod,HAM). The second-order polynomial response surface, Kriging model and radial basis function are combined to adaptively select the optimal meta-model for optimization. In the process of searching, the meta-model is constantly updated and reconstructed by selecting a certain number of samples and the function value tends to the optimal solution. At the same time, the design space is partitioned according to the sort of function value of the sample point. The key area is constructed and the precision of the key area is improved gradually by sampling. Finally, considering the possible fitting error of the metamodel, three sample points with small function value are selected to construct the key space, and the global optimal solution is searched in the critical space by the meta-model, and the optimal design of the system is finally completed. The research results show that the strategy based on global sensitivity analysis and hybrid meta-model optimization is very effective in the optimization design of vehicle occupant constraint system with multiple parameters. The global sensitivity based on variance can quickly identify the important parameters in the system, and the global optimization algorithm of hybrid metamodel breaks the limitation of single element model, and it is fast and economical. To solve the problem of vehicle occupant constraint system optimization accurately. At the same time, it also provides a good reference for the optimization of complex nonlinear systems.
【學(xué)位授予單位】：湖南大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2014
【分類號】：U491.61
本文編號：2368605
[Abstract]:The project is supported by the National Natural Science Foundation of China (51275164), "robust optimization of human body damage in vehicle collisions based on global sensitivity analysis and fitting basis function agent model". The design of vehicle occupant restraint system is an important content of automobile safety design. In the research and development of the passenger restraint system, the combination of computer simulation and optimal design can effectively shorten the product development cycle and improve the product performance. Vehicle crash is a complex multi-parameter impact system response. The occupant damage during the collision process is directly related to the performance of seatbelts, airbags and other restraints. It is also related to the boundary conditions such as the acceleration of the collision of the car body and the seat of the occupant himself. With the increase of the number of system parameters, the calculation cost of optimal design of the system increases sharply. Therefore, how to identify the important parameters of the highly nonlinear system in the conceptual design stage and realize the rapid optimization is of great significance to improve the safety performance of the vehicle and shorten the research and development period. In order to solve the above problems, this paper presents an optimal design strategy for complex nonlinear systems with multiple parameters. Taking the vehicle occupant restraint system under 100% frontal impact as the research object, the impact waveform and 20 parameters of the safety belt, airbag and interior assembly are selected as the optimal design variables. According to the multi-parameter characteristics of the system, a global sensitivity analysis method based on the grouping of variables is adopted. The design variables of the passenger constrained system are divided into a group according to the assembly correlation, and the global sensitivity analysis of each group of variables is carried out respectively. The importance of the parameters is evaluated by calculating the contribution of each variable to the total variance of the system response. In the process of analysis, descriptive Monte Carlo simulation is used to sample the whole design space and metamodel is used instead of the simulation model to complete the sensitivity analysis of design parameters. The information obtained from the analysis is used in the hybrid element model global optimization algorithm (HybridandadaptivemetamodelingMethod,HAM). The second-order polynomial response surface, Kriging model and radial basis function are combined to adaptively select the optimal meta-model for optimization. In the process of searching, the meta-model is constantly updated and reconstructed by selecting a certain number of samples and the function value tends to the optimal solution. At the same time, the design space is partitioned according to the sort of function value of the sample point. The key area is constructed and the precision of the key area is improved gradually by sampling. Finally, considering the possible fitting error of the metamodel, three sample points with small function value are selected to construct the key space, and the global optimal solution is searched in the critical space by the meta-model, and the optimal design of the system is finally completed. The research results show that the strategy based on global sensitivity analysis and hybrid meta-model optimization is very effective in the optimization design of vehicle occupant constraint system with multiple parameters. The global sensitivity based on variance can quickly identify the important parameters in the system, and the global optimization algorithm of hybrid metamodel breaks the limitation of single element model, and it is fast and economical. To solve the problem of vehicle occupant constraint system optimization accurately. At the same time, it also provides a good reference for the optimization of complex nonlinear systems.
【學(xué)位授予單位】：湖南大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2014
【分類號】：U491.61
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