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Improve the frontal crashworthiness of vehicle through the design of front rail
Abstract Front rail is a key assembly for the frontal impact of vehicle. In this work, based on the key information of benchmark vehicles and high-strength steels, front rails are designed to improve the crash performance of vehicle and reduce its structural mass (SM). First, the finite element analysis (FEA) of the front rail is carried out, and dynamic drop testings are performed to verify the accuracy of finite element model. Then, the sectional dimensions, materials and thicknesses of ten benchmark models are studied, and the crashworthiness of these models is obtained by FEA. Next, based on these benchmark models, three front rails with representative sectional sizes are obtained through mesh deformation. By experimental design and FEA, the response surface models (RSMs) of these three front rails are constructed. Based on multi-objective artificial tree (MOAT) algorithm, these three front rails are optimized with the goals of minimize SM and maximize mean crushing force (MCF). The optimal design schemes of these models are obtained. These optimal front rails are applied to the full frontal barrier impact analysis of a sport utility vehicle (SUV) model, and the crashworthiness of this model is improved significantly. Since the selection of materials, thicknesses and sectional dimensions bases on engineering practice and benchmark cases, these optimal schemes in this work can be directly applied to actual vehicle model. In brief, this work has significant practical value.
Highlights The impact performance of ten typical front rails is presented. Three drop testings are performed to verify the accuracy of the simulation analysis. Three front rails with typical sectional dimensions are optimized. The crashworthiness of a SUV is improved with the optimized front rails. This work has a significant guiding role for the design of vehicle crashworthiness.
Improve the frontal crashworthiness of vehicle through the design of front rail
Abstract Front rail is a key assembly for the frontal impact of vehicle. In this work, based on the key information of benchmark vehicles and high-strength steels, front rails are designed to improve the crash performance of vehicle and reduce its structural mass (SM). First, the finite element analysis (FEA) of the front rail is carried out, and dynamic drop testings are performed to verify the accuracy of finite element model. Then, the sectional dimensions, materials and thicknesses of ten benchmark models are studied, and the crashworthiness of these models is obtained by FEA. Next, based on these benchmark models, three front rails with representative sectional sizes are obtained through mesh deformation. By experimental design and FEA, the response surface models (RSMs) of these three front rails are constructed. Based on multi-objective artificial tree (MOAT) algorithm, these three front rails are optimized with the goals of minimize SM and maximize mean crushing force (MCF). The optimal design schemes of these models are obtained. These optimal front rails are applied to the full frontal barrier impact analysis of a sport utility vehicle (SUV) model, and the crashworthiness of this model is improved significantly. Since the selection of materials, thicknesses and sectional dimensions bases on engineering practice and benchmark cases, these optimal schemes in this work can be directly applied to actual vehicle model. In brief, this work has significant practical value.
Highlights The impact performance of ten typical front rails is presented. Three drop testings are performed to verify the accuracy of the simulation analysis. Three front rails with typical sectional dimensions are optimized. The crashworthiness of a SUV is improved with the optimized front rails. This work has a significant guiding role for the design of vehicle crashworthiness.
Improve the frontal crashworthiness of vehicle through the design of front rail
Thin-Walled Structures ; 162
20.02.2021
Aufsatz (Zeitschrift)
Elektronische Ressource
Englisch
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