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Turbulent boundary layers are a significant source of vibration and noise for vehicles moving through a fluid medium. Describing the forcing function for this noise source is an active area of research. Empirical models are commonly used in system noise models. Two common models as discussed by Mellen [1, 2] are separable and non-separable models. The separable models are in a class generally known as Corcos models [3]. The separable models postulate that the wall pressure space time statistics are a function of time times a function of downstream separation times a function of cross stream separation. The non-separable models postulate that the space time statistics are a function of time times a function of space with the downstream and cross stream separation being coupled. Two examples of the non-separable models are Chase [4, 5] and Smol'yakov-Tkachenko [6].

This paper reports on a case study involving the use of SEA methods in the acoustic design of an advanced design luxury sedan. The power of the analytical method was used to advantage in a case of a vehicle with very challenging NVH targets. Three practical issues are highlighted; review of a method to handle adding components that contribute acoustic absorption, presentation of data to aid vehicle content decisions, and design sensitivity analysis. This effort demonstrates an example in which SEA modeling provided relevant and timely input to the vehicle design team to aid decision making for sound package content.

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.

Statistical Energy Analysis (SEA) is being actively pursued in the automotive industry as a tool for vehicle high frequency noise and vibration analysis. A D-class passenger car SEA model has been developed for this purpose. This paper describes the development of load cases for the SEA model to simulate road noise on rumble road. Chassis roll test with rough shells was performed to simulate rumble road noise. Sound radiation from tire patch and vibration transmission through spindles were measured to construct the SEA load cases. Correlation between SEA model predictions and measured data was examined. Test and SEA result comparisons have shown that simulation of airborne road noise requires only a trimmed body SEA model, while simulation of structure-borne road noise may require SEA modeling of chassis components.

Statistical Energy Analysis (SEA) is a tool for estimating the response of complex dynamic systems at high modal density. This tool is seeing ever wider application in a range of industries, including aerospace industry, marine industry, and building trades. The automotive industry is beginning to explore the application of SEA to high frequency vehicle acoustic design. The SEA model of vibrational power transmission has a direct analogy to thermal power transmission (diffusion). As thermal power flow is proportional to temperature difference, vibrational power flow is proportional to modal energy difference. In this paper the thermal analogy is exploited to visualize the SEA results. This is accomplished by color coding a finite element representation of the structure. In this paper, the thermal analogy is used to correlate test data with SEA model results. This is accomplished by constructing a test based modal power thermogram.