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The turbulent boundary layer (TBL) that forms on the outside of a commercial airplane in flight is a significant source of noise. During cruise, the TBL can be the dominant source of noise. Because it is a significant contributor to the interior noise, it is desirable to predict the noise due to the TBL. One modeling approach for the acoustic prediction is statistical energy analysis (SEA). This technique has been adopted by North American commercial airplane manufacturers. The flow over the airplane is so complex that a fully resolved pressure field required for noise predictions is not currently analytically or numerically tractable. The current practice is to idealize the flows as regional and use empirical models for the pressure distribution. Even at this level of idealization, modelers do not agree on appropriate models for the pressure distributions. A description of the wall pressure is insufficient to predict the structural response. A structural model is also required.

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.

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.

The turbulent boundary layer (TBL) that forms on the outer skin of the aircraft in flight is a significant source of interior noise. However, the existing quiet test facilities capable of measuring the TBL wall pressure fluctuations tend to be at low Mach numbers. The objective of this study was to develop a new inlet for an existing six inch square (or 6×6) flow duct that would be adequately free from facility noise to study the TBL wall pressure fluctuations at higher, subsonic Mach numbers. First, the existing flow duct setup was used to measure the TBL wall pressure fluctuations. Then the modified inlet was successfully used to make similar measurements up to Mach number of 0.6. These measurements will be used in the future to validate wall pressure spectrum models for interior noise analysis programs such as statistical energy analysis (SEA) and dynamic energy analysis (DEA).

Previously part of a larger OEM, Spirit AeroSystems became a standalone company 5 years ago and is currently a Tier One supplier of aerostructures. Products include fuselage components, wing structures, engine struts and nacelles, and at the request of various OEMs, fully stuffed fuselages and podded engines where all of the wiring, heating, duct work, etc. is installed prior to delivery. While operating as part of the Propulsion Structures and Systems Business Unit, the design, testing and analysis services provided by the acoustics lab potentially impact all programs at all stages of development because of increasing noise regulations and material certification requirements for implementation in high noise environments.

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.

Wind noise can be a significant event for automotive design engineers. The greenhouse glass plays an important role in the wind noise process. Robust estimates of the greenhouse glass damping are necessary for both understanding and modeling the role of the glass in the wind noise process. One unanswered question is whether the aerodynamic loads affect the window glass damping. To make this determination a method to assess the operational damping is required. The civil engineering community uses the random decrement technique to assess operational damping due to wind loads. The random decrement technique has been shown to be a normalized autocorrelation function. In this paper the damping is estimated directly from the autocorrelation function. In the first section the relationship between the damping and autocorrelation function is examined for white noise excitation. A single oscillator is examined as the first case. Extension to higher modal densities is discussed.

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].