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Technical Paper

Validation of the Coupled PC-CRASH - MADYMO Occupant Simulation Model

2000-03-06
2000-01-0471
During recent years the accident simulation program PCCRASH was developed, which allows to simulate the vehicles movement before, during and after the impact. ...Within SAE 1999-01-0444 a new coupling interface of PC-CRASH and the software MADYMO, developed by TNO in the Netherlands was published. During last year's publication only few validation cases, mainly related to rear end impacts could be demonstrated. ...One major emphasis was set on the influence of the crash pulse, which cannot be derived in PC-CRASH. In this way the paper demonstrates the possibilities as well as the limitations of the numerical model.
Technical Paper

Development of CAE Methodology for Rollover Sensing Algorithm

2009-04-20
2009-01-0828
The Rollover CAE model is developed for Rollover sensing algorithm in this paper. By using suggested CAE model, it is possible to make sensing data of rollover test matrix and these data can be used for calibration of rollover sensing algorithm. Developed vehicle model consists of three parts: a vehicle parts, an occupant parts and a ground boundary conditions. The vehicle parts include detailed suspension model and FE structure model. The occupant parts include ATD (anthropomorphic test device) male dummy and restraint systems: Curtain Airbag and Seat-Belt. We find analytical value of the suspension model through correlation with vehicle drop test, simulate this model under the conditions of untripped (Embankment, Corkscrew) and tripped (Curb-Trip, Soil-Trip) rollover scenarios. Comparison of the simulation and experimental data shows that the simulation results of suggested CAE model can be substituted for the experimental ones in calibration of rollover sensing algorithm.
Journal Article

A Bayesian Approach to Cross-Validation in Pedestrian Accident Reconstruction

2011-04-12
2011-01-0290
In statistical modeling, cross-validation refers to the practice of fitting a model with part of the available data, and then using predictions of the unused data to test and improve the fitted model. In accident reconstruction, cross-validation is possible when two different measurements can be used to estimate the same accident feature, such as when measured skidmark length and pedestrian throw distance each provide an estimate of impact speed. In this case a Bayesian cross-validation can be carried out by (1) using one measurement and Bayes theorem to compute a posterior distribution for the impact speed, (2) using this posterior distribution to compute a predictive distribution for the second measurement, and then (3) comparing the actual second measurement to this predictive distribution. An actual measurement falling in an extreme tail of the predictive distribution suggests a weakness in the assumptions governing the reconstruction.
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