Development of Most Probable Optimal Design Method and Application

Volume 1: 32nd Design Automation Conference, Parts a and B

2006

This paper presents an efficient algorithm for developing vehicle structures for crashworthiness, based on the analyses of crash mode, a history of the deformation of the different structural zones during a crash event. It emulates a process called crash mode matching where structural crashworthiness is improved by manually modifying the design until its crash mode matches the one the designers deem as optimal. Given an initial design and a desired crash mode, the algorithm iteratively finds a new design whose crash mode is increasingly closer to the desired one. At each iteration, a new design is chosen as the best among the normally distributed samples near the current design, whose mean and standard deviation are adjusted by a set of fuzzy rules. Each fuzzy rule encapsulates elementary knowledge of manual crash mode matching, as a mapping from the differences between the current and desired crash modes, to the changes in mean and standard deviation for sampling a sizing parameter in a structural zone. A case study on a vehicle frontal crash demonstrated the algorithm outperformed the conventional methods both in design quality and computational time.

Section: Optimization With Surrogate Modelsmentioning

confidence: 99%

An Efficient Algorithm for Vehicle Crashworthiness Design Via Crash Mode Matching

Volume 1: 32nd Design Automation Conference, Parts a and B

2006

“…For use during the detailed design phase where the parametric geometry of the vehicle structures is fully known, the surrogate models that empirically capture the input-output relationship of a FE crash simulation are widely used [5][6][7][8][9][10][11][12]. However, the ranges of design variables (typically dimensions) are often limited in order to build an accurate model with a small number of sample input and output obtained by FE runs.…”

Section: Related Workmentioning

confidence: 99%

Design for Crashworthiness of Vehicle Structures via Equivalent Mechanism Approximations and Crush Mode Matching

2004

Design Engineering

This paper presents a 3D extension to our previous work on vehicle crashworthiness design that utilizes “equivalent” mechanism models of vehicle structures as a tool for the early design exploration. An equivalent mechanism (EM) is a network of rigid links with lumped masses connected by prismatic and revolute joints with nonlinear springs, which approximate aggregated behaviors of structural members during crush. A number of finite element (FE) models of thin-walled beams with typical cross sections and wall thicknesses are analyzed to build a surrogate model that maps the beam dimensions to nonlinear spring properties. Using the surrogate model, an EM model is optimized for given design objectives by selecting the nonlinear springs among the ones realizable by thin-walled beams. The optimum EM model serves to identify a good crash mode (CM), the time history of collapse of the structural members, and to suggest the dimensions of the structural members to attain it. After the optimization, the FE model of an entire structure is “assembled” from the suggested dimensions, which is further modified to attain the good CM identified by the optimum EM model. A case study of a 3D vehicle front half body demonstrates that the proposed approach can help obtain good designs with far less computational resources than the direct optimization of a FE model.

“…Development of approximate models for simulating the vehicle behavior during crash, yet do not require the larger computational resources as those required for full FEM simulations, is also an active area of research. On one side, there are the reduced and lumped parameter models [9,10,11] based on simplified physical representations of the vehicle, while on the other hand, there are the surrogate modeling techniques which are mathematical methods for general purpose functional approximation [12][13][14][15][16]. While lumped parameter models have the advantage of including an embedded physical model, they are generally not very useful in parametric design optimization because of their lack of detail.…”

Section: Relvant Literaturementioning

confidence: 99%

Design Optimization of a Vehicle B-Pillar Subjected to Roof Crush Using Mixed Reactive Taboo Search

Volume 2: 29th Design Automation Conference, Parts a and B

Nassef

2003

The primary obstacle in automated design for crashworthiness is the heavy computational resources required during the optimization processes. Hence it is desirable to develop efficient optimization algorithms capable of finding good solutions without requiring too many model simulations. This paper presents an efficient mixed discrete and continuous optimization algorithm, Mixed Reactive Taboo Search (MRTS), and its application to the design of a vehicle B-Pillar subjected to roof crush conditions. The problem is sophisticated enough to explore the MRTS’ capability of identifying multiple local optima with a single optimization run, yet the associated finite element model (FEM) is not too large to make the computational resources required for global optimization prohibitive. The optimization results demonstrated that a single run of MRTS identified a set of better designs with smaller number of simulation runs, than multiple runs of Sequential Quadratic Programming (SQP) with several starting points.