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Statistical Energy Analysis is an accepted method for NVH model development at high frequencies. This paper explores hybrid methods to expand SEA to the mid and low frequency range where the assumption of high modal density is not valid. The newly developed hybrid method is particularly useful for structure-borne noise and vibration studies. The method is based on use of mobility functions to improve calculation of power input, modal densities and coupling factors. Comparison of the hybrid model predictions with measured data show good agreement down to 100 Hz.

Statistical Energy Analysis (SEA) is the standard analytical tool for predicting vehicle acoustic and vibration responses at high frequencies. SEA is commonly used to obtain the interior Sound Pressure Level (SPL) due to each individual noise or vibration source and to determine the contribution to the interior noise through each dominant transfer path. This supports cascading vehicle noise and vibration targets and early evaluation of the vehicle design to effectively meet NVH targets with optimized cost and weight. A common misconception is that SEA is only capable of predicting a general average interior SPL for the entire vehicle cabin and that the differences between different locations such as driver's ear, rear passenger's ear, lower interior points, etc., in the vehicle cannot be analytically determined by an SEA model.

Statistical energy analysis has become an accepted method to predict noise, vibration and harshness in motor vehicles. SEA provides a statistical estimate of the vehicle response. In most cases, the mean response prediction is used to evaluate different designs. It has generally been found that SEA predictions of the change in mean acoustic response due to a specific design change are in good agreement with the change in the actual measured results. However, there may be significant differences between the absolute value of the predicted mean and the value observed from measurement. These differences are associated with the statistical variability of the prediction. This paper explores the use of the variance of the SEA prediction. Determination of confidence intervals for absolute prediction accuracy is described. In addition, the variance associated with SEA prediction of design changes is discussed.

Statistical energy analysis is generally used to study the vibroacoustic response of systems with high modal densities. The most accurate predictions are obtained at high frequencies where the modal overlap is high and many modes contribute to the response in each frequency band. Under these conditions, the vibrational response is fairly uniformly distributed over frequency and over the spatial extent of the SEA subsystems. Validation of an SEA model at high frequencies can be accomplished by comparing the predictions of average subsystem response with an average formed from measured data at a relatively small number of locations. At lower frequencies, where the modal overlap is not high, the vibrational response shows significant variability over both frequency and location. Large variability makes validation of the models more difficult.