Summary
Buckling and certain contact situations cause scattering results of numerical crash simulation: For a BMW model differences between the position of a node in two simulation runs of up to 10 cm were observed, just as a result of round-off differences in the case of parallel computing. An engineer has to measure this scatter, to check whether important parts of the car show such indeterministic behavior and to find the origins. The tool DIFF-CRASH compares simulation results and uses data mining technology to cluster those nodes of the car model, which show similar scatter among the simulation runs. For the BMW model the indeterministic behavior could be traced back to a certain part of the motor carrier and was removed by a redesign. DIFF-CRASH is the only activity using data mining technology for crash simulation stability analysis. In this paper we present the clustering algorithm and illustrate its usage in car crash simulation analysis.
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References
Berry, M.W.; Drmac, R.; Jessup E.R.: Matrices, vector spaces and information retrieval. SIAM review, 41(2):335:362 (1999)
Berry, M.W.; Dumais, S.; O'Brien, G.: Using linear algebra for intelligent information retrieval, SIAM Review, 37(4):573–595 (1995)
Bendisch, J.; von Trotha, H.: Investigation on car stability in crash simulations. In ATTCE Proceedings Volume 1 Safety, SAE International (2001)
Hamerly, G.: Learning structure and concepts in data through data clustering, doctoral thesis of Uni. of California, San Diego (2002)
Han, J.; Kamber, M.: Data Mining Concepts and Techniques, Morgan Kaufmann (2000)
Jain, A.K.; Dubes, R.C.: Algorithms for Clustering Data, Prentice Hall (1988)
Karypis, G.; Han, J.; Kumar, V.: CHAMELEON: A hierarchical clustering algorithm using dynamic modeling, Computer, 32:68–75 (1999)
Marczyk, J.: Principles of simulation-based computer-aided engineering. FIM Publications, Barcelona (1999)
Marczyk, J.: Computational stochastic mechanics in meta-computing perspective, International Center for Numerical Methods in Engineering, Barcelona (1997)
Mierendorff, H.; Stueben, K.; Thomas, O.: EUROPORT-1 Porting industrial codes to parallel architectures. In: Hertzberger, B.; Serazzi, G. (eds.); High Performance Computing and Networking, Lecture Notes in Computer Science, Number 919, Springer, Berlin, Heidelberg (1995)
Pizzuti, C., Talia, D.: Using SVD for data mining of high dimensional data sets, 2nd workshop on mining scientific datasets, AHPCRC (2000)
Thole, C.A., Mei, L.: Comparison of several similarity functions for stability analysis of crash simulation results. SCAI Report, FhG-SCAI Sankt Augustin (2002)
Thomas, R.S., Nolan, N.W.: Once is not enough — A few more thoughts, Sound and Vibration (1994)
Thole, C., Kolibal, S.; Wolf, K.: AUTOBENCH — Virtual prototypes for automotive industry. In Deville, M.; Owen, R. (eds): 16th IMACS World Congress 2000 Proceedings, IMACS, Rutgers University, New Brunswick (2000)
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Meil, L., Thole, C.A. (2005). Clustering Algorithms for Parallel Car-Crash Simulation Analysis. In: Bock, H.G., Phu, H.X., Kostina, E., Rannacher, R. (eds) Modeling, Simulation and Optimization of Complex Processes. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-27170-8_25
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DOI: https://doi.org/10.1007/3-540-27170-8_25
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-23027-4
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