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Size Optimization Of Automotive Rear Bumper Mask Using Equivalent Static Loads Method

Posted on:2021-03-29Degree:MasterType:Thesis
Country:ChinaCandidate:F Q WangFull Text:PDF
GTID:2392330620972025Subject:Vehicle engineering
Abstract/Summary:PDF Full Text Request
In recent years,with the development of the automotive industry,the number of vehicles held by people has been increased.At the same time,the probability of low-speed collisions is also increased.In some minor scratching accidents,plastic bumper masks are the most prone to damage.Therefore,bumper mask are required to have good stiffness characteristics and energy absorption characteristics in low-speed collisions to ensure that its surface deformation is small during accidents and can be easily restored or repaired.On the other hand,in order to reduce the quality of the entire vehicle and fuel consumption,the quality of bumper mask needs to be as small as possible.At the same time,reducing the quality of the bumper mask can save materials,which is of great significance for the production enterprises to reduce product costs and improve product competitiveness.Compared with the front bumper,the rear bumper is simpler in structure and easier in size optimization.Therefore,this thesis will take the rear bumper mask of a passenger car as the research object and optimize the thickness of each part of the mask on the premise of ensuring its crash resistance.Due to the dynamic load,material nonlinearity,contact nonlinearity and other problems involved in the low-speed collision of bumper,it is difficult to optimize directly in the collision state.To solve this problem,this thesis uses the equivalent static loads method(ESLM)to convert the dynamic nonlinear optimization problem into the static linear optimization problem.Firstly,this thesis studies the theory of the ESLM and summarizes the calculation flow of the method.In order to verify the accuracy of the calculation process,a curved thin plate is used as an example.The LS-DYNA module in Hypermesh is used to establish a finite element model of the thin plate collision and converted into a.k file.The OptiStruct module in Hypermesh is used to build a thin plate stiffness solution model and a static linear optimization model,and convert them into two.fem files.Then MATLAB is used to call the LS-DYNA solver and the OptiStruct solver to solve above model files,calculate the equivalent static loads according to the simulation results,and input the equivalent static loads into the static linear optimization model for optimization.This process is repeated until the constraints are met.The final results have verified the feasibility and correctness of the optimization process.Before the size optimization of the rear bumper mask,a low-speed collision simulation model of the rear bumper will be built in LS-DYNA module of Hypermesh according to the national standard GB 17354-1998,and the simulation results have been analyzed.Then,according to the national standard,a rear bumper frontal low-speed crash test has been performed.The test results has been processed and compared with the simulation results which mainly includes the change of the mask contact force and displacement with time during the collision.The final results has verified the accuracy of the low-speed collision simulation model.Finally,the stiffness solution model and the static linear optimization model of the rear bumper mask are be established in the Hypermesh.Combined with the bumper low-speed collision model and thin plate optimization algorithm,the ESLM is used to optimize the size of the mask.In the whole process,MATLAB is used to write the optimization algorithm which can call the LS-DYNA solver and OptiStruct solver automatically,read,calculate,and communicate the data of finite element model files,so as to realize the automation of the entire optimization process.In the end,the quality of the mask is reduced by 8.8% on the premise of ensuring the bumper collision resistance.
Keywords/Search Tags:Passenger car, Rear bumper mask, Equivalent static load method, Low-speed collision, Size optimization, Nonlinear dynamic optimization
PDF Full Text Request
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