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Published in:
2018 | OriginalPaper | Chapter
Automatic Generation, Validation and Correlation of the Submodels for the Use in the Optimization of Crashworthy Structures
Authors : Carlos J. Falconi D., Alexander F. Walser, Harman Singh, Axel Schumacher
Published in: Advances in Structural and Multidisciplinary Optimization
Publisher: Springer International Publishing
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Abstract
The structural optimization of large crashworthy systems like a vehicle body in a crash loaded case is a time consuming and costly process. The computation time can be reduced by dividing the large system (main model) into small systems called submodels. These submodels can be effectively used in the optimization to shorten the response time of the simulation. The generation of submodels by hand is challenging and requires a lot of effort and knowledge to create and validate them. This paper presents a workflow to automatically generate and validate the submodels using various mathematical functions.
A submodel is a region of interest cut out from the large system which is to be analyzed in detail [1]. This detailed analysis can be useful to enhance the structural performance of a large crash system. There are two important parameters to generate a submodel using the so called connecting island algorithm, the threshold ratio and the connecting island value. These parameters are based on an evaluation function which is a structural response with time averaging and space averaging. The size of the submodel depends on these two parameters.
The validation of a submodel is a four step process i.e. a local, global, mean and a response validation. These four steps measure the deviation between the submodel and their counterparts in the large system. The validation process is used to identify the quality of a submodel. It is discussed how the validation criteria effects the submodel quality and its size. The aim of this research work is to create a method for automatic generation and validation of submodels which is universally applicable to different crash models. The method is demonstrated on two different examples. The first one is an academic crash model of a cantilever frame hit by a rigid sphere. The submodels are generated and validated for different crash scenarios, where the rigid sphere hits the cantilever frame at different positions. The second is an industrial crash example of Toyota YARIS in front and side crash.