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An age-dependent, serious-to-fatal (AIS3+), thoracic risk curve was derived and evaluated for frontal impacts. The study consisted of four parts. In Part 1, two datasets of post mortem human subjects (PMHS) were generated for statistical and sensitivity analyses. In Part 2, logistic regression analyses were conducted. For each dataset, two statistical methods were applied: (1) a conventional maximum likelihood method, and (2) a modified maximum likelihood method. Therefore, four statistical models were derived — one for each dataset/statistical method combination. For all of the resulting statistical models (risk curves), the linear combination of maximum normalized sternum deflection and age of the PMHS was identified as a feasible predictor of AIS3+ thoracic injury probability. In Part 3, the PMHS-based risk curves were transformed into test-dummy-based risk curves. In Part 4, validation studies were conducted for each risk curve.

Finite Element Analysis (FEA) models are routinely being adopted as a means of up-front design for automotive body structure design. FEA models play two important functions: first as a means of assessing design versus an absolute target; secondly they are used to assess the performance of design alternatives required to meet targets. Means of assessing model capability versus task is required to feed appropriate information into the design process. Being able to document model capability improves the credibility of the FEA model information. A prior paper addressed assessing the absolute performance of model technology using a metric based on a statistical hypotheses test that determines membership in a reference set. This paper extends the use of quality technology to determining the capability of the FEA model to span the design space using Designed Experiments.

Statistical Energy Analysis (SEA) models are routinely being adopted in up-front automotive sound package design. SEA models serve two important functions. First they provide a means of assessing noise and vibration performance relative to absolute targets. Secondly, they are used to assess various alternative designs or changes required to meet targets. This paper addresses how to objectively evaluate both the absolute and relative predictive capability of SEA models. The absolute prediction is assessed using a hypothesis test to determine membership of the analytical prediction relative to a set of test data. The relative prediction is assessed using hardware-designed experiments to estimate design sensitivities. Both have been found useful to drive model improvement efforts. Being able to objectively document model capability also improves the credibility of SEA model predictions and the design information they deliver.

The problem of NVH CAE model correlation in light of test and product variation has been addressed. An objective metric based on statistical hypothesis testing has been proposed and evaluated. This technique has been shown to work for frequency response functions. The hypothesis test answers the question ‘Are the involved frequency response functions statistically different than those in a reference set?’ This paper demonstrates that vehicles are uniquely identifiable by their frequency response functions. Under certain restrictive assumptions, the average gross error normalized by the ensemble variance is chi-squared distributed. Using a chi-squared test, the probability that a NVH CAE prediction is a member of a reference (test) set can be estimated. Within the context of a reference (test) set, this metric represents the limit to predictability. The metric was applied to examples including two midsize car NVH CAE models.

Noise and vibration engineers make many frequency domain measurements during the development of an automobile. These measurements are used to develop prototype hardware or, in some cases, to increase the “degree of belief” of a computer model. In the case of hardware development, the engineer frequently must evaluate competing designs while, in the case of computer modeling, the engineer often must investigate the fidelity of his/her assumptions. In either instance, the engineer will perform some type of experiment to answer the question(s) of interest. Many experiments, however, may be compromised by undesired variability associated with the data. This variability may arise from sampling uncertainty, transducer noise and digital signal processing bias to list a few. While most engineers consider this variability, they often do not account for other sources of variability such as experimental set-up, car to car differences and environmental conditions.

Body structure design and development is becoming more and more critical for noise, vibration and harshness (NVH) vehicle performance. Many body structure design alternatives are studied analytically and in hardware during a vehicle program. Because of design and fabrication time, body structure hardware development can be very expensive and extremely time consuming. Consequently, the use of experimental design techniques for vehicle NVH development are becoming more popular to bring quality products to the market faster. This paper demonstrates how an experimental approach was used to develop the body structure. Initially, a hardware experiment was used to assess the effects of four parts groups for Body Chassis NVH. Then, to further study the major parts groups, a computational experiment was performed. The result of these two experiments (hardware and computational) was used to recommend design concepts which reduced interior noise levels and improved body chassis NVH.

Automotive outside rear view mirror shape has become an important consideration in achieving wind noise and aerodynamic performance objectives. This paper describes a two step process used to develop a mirror shape which meets both wind noise and aerodynamic objectives. First, basic understanding of door mounted verses sail mounted mirrors and shape parameters was obtained by evaluating selected shapes and studying their physical measurements relative to their measured responses. Relationships between the wind noise and drag responses revealed performance range limitations for sail mounted mirrors. Second, a central composite experimental design was utilized to more closely investigate door mounted mirror shape parameters to determine optimal mirror performance potential. The resulting empirical models developed were used to determine the best overall solution.

Vehicle sound quality has become an important basic performance requirement. Traditionally, automobile noise studies were focused on quietness. It is now necessary for the automobile to be more than quiet. The sound must be pleasing. This paper describes a development process to improve both vehicle noise level and sound quality. Formal experimental design techniques were utilized to quantify various hardware effects. A-weighted sound pressure level, Speech Intelligibility, and Composite Rating of Preference were the three descriptors used to characterize the vehicle's sound quality. Engineering knowledge augmented with graphical and statistical techniques were utilized during data analysis. The individual component contributions to each of the sound quality descriptors were also quantified in this study.