年間 6 号発行
ISSN 印刷: 2152-5080
ISSN オンライン: 2152-5099
IF:
1.7
5-Year IF:
1.9
Immediacy Index:
0.5
Eigenfactor:
0.0007
JCI:
0.5
SJR:
0.584
SNIP:
0.676
CiteScore™::
3
H-Index:
25
Indexed in
ENABLING THE ANALYSIS OF FINITE ELEMENT SIMULATION BUNDLES
Download
要約
We propose a methodology capable of allowing a fast evaluation of thousands of finite element design variants simultaneously. This approach uses a high dimensional analysis concept, namely diffusion maps, that has been in use successfully for years in many areas of science. Using feature vectors from a bundle of finite element simulations containing information on the design variables on different mesh sizes and applying this analysis concept is the purpose of this paper. Applying this approach enables the identification of a set of parameters (reduction coordinates) along which (i) geometrical variants can be identified and (ii) for random time-dependent problems, slow variables can be identified that show the variables with the most significant impact on a design. We demonstrate the application of this approach in several industrial examples in the areas of metal forming and vibration analysis as well as vehicle crash simulation, which is a noisy stochastic process. Finally, we show per example that this approach can identify and expound on the occurrence of a bifurcation point, a very important issue in vehicle design.
-
Diez C., Kunze P., Toewe D., Wieser C., Harzheim L., Schumacher A., Big-Data Based Rule-Finding for Analysis of Crash Simulations, in Advances in Structural and Multidisciplinary Optimization, 2018. Crossref
-
Liu Wenjun, Su Sen, Qiu Jinlong, Zhang Yongyong, Yin Zhiyong, Exploration of Pedestrian Head Injuries—Collision Parameter Relationships through a Combination of Retrospective Analysis and Finite Element Method, International Journal of Environmental Research and Public Health, 13, 12, 2016. Crossref
-
Garcke Jochen, Pathare Mandar, Prabakaran Nikhil, ModelCompare, in Scientific Computing and Algorithms in Industrial Simulations, 2017. Crossref
-
Iza-Teran Rodrigo, Garcke Jochen, A Geometrical Method for Low-Dimensional Representations of Simulations, SIAM/ASA Journal on Uncertainty Quantification, 7, 2, 2019. Crossref
-
Kracker David, Dhanasekaran Revan Kumar, Schumacher Axel, Garcke Jochen, Method for automated detection of outliers in crash simulations, International Journal of Crashworthiness, 2022. Crossref
-
Bohn Bastian, Garcke Jochen, Griebel Michael, A sparse grid based method for generative dimensionality reduction of high-dimensional data, Journal of Computational Physics, 309, 2016. Crossref
-
Garcke Jochen, Hahner Sara, Iza-Teran Rodrigo, Alignment of highly resolved time-dependent experimental and simulated crash test data, International Journal of Crashworthiness, 2022. Crossref