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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

Nonlinear Optimization in Vehicular Crash Reconstruction

2015-04-14
2015-01-1433
This paper presents a reconstruction technique in which nonlinear optimization is used in combination with an impact model to quickly and efficiently find a solution to a given set of parameters and conditions to reconstruct a collision. These parameters and conditions correspond to known or prescribed collision information (generally from the physical evidence) and can be incorporated into the optimized collision reconstruction technique in a variety of ways including as a prescribed value, through the use of a constraint, as part of a quality function, or possibly as a combination of these means. This reconstruction technique provides a proper, effective, and efficient means to incorporate data collected by Event Data Recorders (EDR) into a crash reconstruction. The technique is presented in this paper using the Planar Impact Mechanics (PIM) collision model in combination with the Solver utility in Microsoft Excel.
Book

Rollover Accident Reconstruction

2018-08-07
According to the National Highway Traffic Safety Administration, “of the nearly 9.1 million passenger car, SUV, pickup and van crashes in 2010, only 2.1% involved a rollover. However, rollovers accounted for nearly 35% of all deaths from passenger vehicle crashes. In 2010 alone, more than 7,600 people died in rollover crashes.” Rollover accidents continue to be a leading contributor of vehicle deaths. While this continues to be true, it is pertinent to understand the entire crash process. Each stage of the accident provides valuable insight into the application of reconstruction methodologies. Rollover Accident Reconstruction focuses on tripped, single vehicle rollover crashes that terminate without striking a fixed object.
Technical Paper

Bayesian Uncertainty Quantification for Planar Impact Crashes via Markov Chain Monte Carlo Simulation

2016-04-05
2016-01-1481
A continuing topic of interest is how to best use information from Event Data Recorders (EDR) to reconstruct crashes. If one has a model which can predict EDR data from values of the target variables of interest, such as vehicle speeds at impact, then in principle one can invert this model to estimate the target values from EDR measurements. In practice though this can require solving a system of nonlinear equations and a reasonably flexible method for carrying this out involves replacing the inverse problem with nonlinear least-squares (NLS) minimization. NLS has been successfully applied to two-vehicle planar impact crashes in order to estimate impact speeds from different combinations of EDR, crush, and exit angle measurements, but an open question is how to assess the uncertainty associated with these estimates. This paper describes how Markov Chain Monte Carlo (MCMC) simulation can be used to quantify uncertainty in planar impact crashes.
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.
Book

Motorcycle Accident Reconstruction

2018-12-10
In a recent National Highway Traffic Safety Administration (NHTSA) report, about one out of every 7 fatalities on the road involved a motorcycle. Itis clear that motorcyclists are more vulnerable and much more likely to be injured or killed in a crash than are passengers in a car accident. Motorcycle Accident Reconstruction purposefully pulls together as much of the relevant accident reconstruction literature and science as possible to present definitive literature that meets the needs of the crash reconstruction industry. The reader will learn to analyze physical evidence, understand what it means, and how to incorporate math and physics into an investigation. Topics featured in this book include: Case studies utilizing event data recorder data Photogrammetry analysis Determining motorcycle speed at the time of an accident The book provides a unique roadmap for the motorcycle accident reconstructionist user.
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