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Journal Article

Distribution of Belt Anchorage Locations in the Second Row of Passenger Cars and Light Trucks

Seat belt anchorage locations have a strong effect on occupant protection. Federal Motor Vehicle Safety Standard (FMVSS) 210 specifies requirements for the layout of the anchorages relative to the seating reference point and seat back angle established by the SAE J826 H-point manikin. Sled testing and computational simulation has established that belt anchorage locations have a strong effect on occupant kinematics, particularly for child occupants using the belt as their primary restraint. As part of a larger study of vehicle geometry, the locations of the anchorage points in the second-row, outboard seating positions of 83 passenger cars and light trucks with a median model year of 2005 were measured. The lower anchorage locations spanned the entire range of lap belt angles permissible under FMVSS 210 and the upper anchorages (D-ring locations) were distributed widely as well.
Journal Article

An Ensemble Approach for Model Bias Prediction

Model validation is a process of determining the degree to which a model is an accurate representation of the real world from the perspective of the intended uses of the model. In reliability based design, the intended use of the model is to identify an optimal design with the minimum cost function while satisfying all reliability constraints. It is pivotal that computational models should be validated before conducting the reliability based design. This paper presents an ensemble approach for model bias prediction in order to correct predictions of computational models. The basic idea is to first characterize the model bias of computational models, then correct the model prediction by adding the characterized model bias. The ensemble approach is composed of two prediction mechanisms: 1) response surface of model bias, and 2) Copula modeling of a series of relationships between design variables and the model bias, between model prediction and the model bias.
Journal Article

A Bayesian Inference based Model Interpolation and Extrapolation

Model validation is a process to assess the validity and predictive capabilities of a computer model by comparing simulation results with test data for its intended use of the model. One of the key difficulties for model validation is to evaluate the quality of a computer model at different test configurations in design space, and interpolate or extrapolate the evaluation results to untested new design configurations. In this paper, an integrated model interpolation and extrapolation framework based on Bayesian inference and Response Surface Models (RSM) is proposed to validate the designs both within and outside of the original design space. Bayesian inference is first applied to quantify the distributions' hyper-parameters of the bias between test and CAE data in the validation domain. Then, the hyper-parameters are extrapolated from the design configurations to untested new design. They are then followed by the prediction interval of responses at the new design points.

Digital Human Modeling for Vehicle and Workplace Design

This book presents seven case studies in which digital human models were used to solve different types of physical problems associated with proposed human-machine interaction tasks. This book includes contributions from researchers at Ford, Boeing, DaimlerChrysler, General Motors, the U.S. Air Force, and others.
Technical Paper

Dynamic Validation of a Computer Simulation for Vehicle Crash

The present paper describes two crash tests designed to validate a computer simulation developed for predicting the large dynamic plastic response of vehicle structures under crash conditions. The test structures were idealized quarter scale models consisting of frame and rigid body elements. Both direct and oblique pole impacts are reported. Impact speed was 30 MPH. Predicted and experimental results are compared for the crush displacements, impact force at the pole barrier, and acceleration histories at two points on the “passenger compartment” mass. Good agreement is obtained for the symmetric test. Results for the oblique test are not as uniformly good, but quantitative agreement is still satisfactory. Comparison of dynamic variables are sensitive to both the filtering of the raw test data and the numerical integration procedure employed in the simulation.