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Model Update and Statistical Correlation Metrics for Automotive Crash Simulations
Technical Paper
2007-01-1744
ISSN: 0148-7191, e-ISSN: 2688-3627
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English
Abstract
In order to develop confidence in numerical models which are used for automotive crash simulations, results are compared with test data. Modeling assumptions are made when constructing a simulation model for a complex system, such as a vehicle. Through a thorough understanding of the modeling assumptions an appropriate set of variables can be selected and adjusted in order to improve correlation with test data. Such a process can lead to better modeling practices when constructing a simulation model. Comparisons between the time history of acceleration responses from test and simulations are the most challenging. Computing accelerations correctly is more difficult compared to computing displacements, velocities, or intrusion levels due to the second order differentiation with time. In this paper a methodology for enabling the update of a simulation model for improved correlation is presented. Fast running models are developed for the time histories of the acceleration at the measurement locations based on principal component decomposition and metamodeling. A large number of iterations are required during the model update process in order to guide the changes in the numerical model for improved correlation. The fast running models are utilized during this process instead of the actual solver for computing the time histories of the accelerations. Once the model update is completed, the fast running models are further employed for enabling probabilistic analyses that can reflect the modeling uncertainties in the simulation results. Repeatability of a test is also an issue either due to vehicle-to-vehicle variability, or due to the challenges of instrumenting a vehicle and collecting the test data. Therefore, a statistical correlation metric between the numerical solution and the test data is introduced and the fast running models are employed in the process.
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Sun, J., He, J., Vlahopoulos, N., and van Ast, P., "Model Update and Statistical Correlation Metrics for Automotive Crash Simulations," SAE Technical Paper 2007-01-1744, 2007, https://doi.org/10.4271/2007-01-1744.Also In
Reliability and Robust Design in Automotive Engineering, 2007
Number: SP-2119; Published: 2007-04-16
Number: SP-2119; Published: 2007-04-16
References
- Hasselman T. K. Anderson M. C. Gan W. “Principal Components Analysis for Nonlinear Model Correlation, Updating and Uncertainty Evaluation, ” Proceedings of the 16 th International Modal Analysis Conference Santa Barbara, CA February 3-5 1998
- Bayarri M. J. Berger J. O. Higdon D. Kennedy M. C. Kottas A. Paulo R. Sacks J. Cafeo J. A. Cavendish J. Lin C. H. Tu J. “A framework for Validating Computer Models, ” report for NSF Project Number DMS-0073952
- Barney P. Ferregut C. Perez L. E. Hunter N. F. Paez T. L. “Statistical validation of system models, ” 1997 Proceedings of IEEE 501 510
- Anderson M.C. Hasselman T.K. Crawford J.E. “A Toolbox for Validation of Nonlinear Finite Element Methods, ” 6 th International LS-DYNA Users Conference Minneapolis, MN 2000
- Anderson M.C. Gan W. Hasselman T.K. “Statistical Analysis of Modeling Uncertainty and Predictive Accuracy for Nonlinear Finite Element Methods, ” Proceedings of the 7 th Shock and Vibration Symposium Minneapolis, MN 1998
- Hasselman T. K. Anderson M. C. Zimmerman D. C. “Fast Running Approximations of High Fidelity Physics Based Models, ” Proceedings of the 69 th Shock and Vibration Symposium Minneapolis/St. Paul, MN 1998
- Wall M. E. Rechtsteiner A. Rocha L. M. “Chapter 5: Singular Value Decomposition and Principal Component Analysis in A Practical Approach to Microarray Data Analysis, ” Kluwer Norwell, MA 2003 91 109
- Paez T. L. Hunter N. F. Cafeo J. A. “A Karhunen-Loeve framework for modeling structural randomness, ” Proceedings of SPIE - The international society for Optical engineering 4753 893 899
- Sun J. Vlahopoulos N. Stabryla T. J. Goetz R. Van De Velde R. “Simulations under uncertainty for occupant safety for a vehicle subjected to a blast load, ” 2006 SAE Congress, SAE Paper 2006-01-0762
- Sun J. Vlahopoulos N. Hu K. “Model update under uncertainty and error estimation in shock applications, ” SAE Paper No. 2005-01-2373 , 2005 SAE Noise and Vibration Conference Traverse City, MI
- Wehrwein D. Mourelatos Z. P. “Reliability based Design Optimization of Dynamic Vehicle Performance using Bond Graphs and Time Dependent Metamodels, ” 2006 SAE World Congress, SAE Paper 2006-01-0109
- Smith C. L. “Uncertainty Propagation using Taylor Series Expansion and a Spreadsheet, ” Journal of the Idaho Academy of Science 30 2 93 105 December 1994
- Masri S. Smyth A. Traina M. “Probabilistic Representation and Transmission of Nonstationary Processes in Multi-Degree-of-Freedom Systems, ” Journal of Applied Mechanics, ASME 65 1998 398 409
- Paez T. Hunter N. “Representation of Random Shock via the Karhumen Loeve Expansion, ” Proceedings of the 2000 Shock and Vibration Symposium SAVIAC, Shock and Vibration Information and Analysis Center 2000
- Paez T. Hunter N. Cafeo J. “A Karhumen Loeve Framework for Modeling Structural Randomness, ” Proceedings of International Modal Analysis Conference XX 2002 Los Angeles, California
- Moeller M. J. Thomas R. S. Chen S-E Chandra N. Lemk P. “NVH CAE Quality Metrics, ” Proceedings of 1999 SAE Noise and Vibration Conference, SAE paper 1999-01-1791
- Cressie N. “Spatial Prediction and Ordinary Kriging, ” Mathematical Geology 20 4 405 421 1988
- Sacks J. Welch W.J. Mitchell J.J. Wynn H.P. “Design and Analysis of Computer Experiments, ” Statistical Science 4 4 409 435 1989
- Sacks J. Welch W.J. Schiller S.B. “Designs for Computer Experiments, ” Technometrics 31 1 41 47 1989
- Currin C. Mitchell T. Morris M. Ylvisaker D. “A Bayesian Approach to the Design and Analysis of Computer Experiments, ” ORNL Technical Report 6498, available from the National Technical Information Service Springfield, VA 1988