Search Results

This paper documents input data conditioning, input parameter selection, structure, training, and validation of neural network models of the Active Aeroelastic Wing aircraft. Neural networks can account for uncharacterized nonlinear effects and retain generalization capability. Model inputs include aircraft rates, accelerations, and control surface positions. Linear loads models were developed for network training starting points. The models were trained with rolls, loaded reversals, windup turns, and individual control surface doublets for load excitation. Data results from all loads models at Mach 0.90 and altitude of 15,000 ft. show an average model prediction error reduction of 18.6 percent.

A numerical methodology based on the finite element and boundary element methods is presented for computing the noise radiated from an elastically supported structure subject to turbulent boundary layer excitation. The new algorithm utilizes the fluctuating wall pressure in order to define the excitation on the structural-acoustic system. The developments target wind noise prediction for the sound radiated by the side glass window of an automobile. The glass-seal assembly is modeled as a flexible plate mounted on an elastic foundation with stiffness and damping characteristics. Numerical predictions are compared successfully to wind tunnel test data. Parametric analyses are performed in order to identify the characteristics of the seal that can lead to noise reduction.

A formulation that accounts for manufacturing variability in the analysis of structural/acoustic systems is presented. The methodology incorporates the concept of fast probability integration with finite element (FEA) and boundary element analysis (BEA) for producing the probabilistic acoustic response of a structural/acoustic system. The advanced mean value method is used for integrating the system probability density function. FEA and BEA are combined for producing the acoustic response that constitutes the performance function. The probabilistic acoustic response is calculated in terms of a cumulative distribution function. The new methodology is used to illustrate the difference between the results from a probabilistic analysis that accounts for manufacturing uncertainty, and an equivalent deterministic simulation through applications. The probabilistic computations are validated by comparison to Monte Carlo simulations.

In this paper an algorithm is developed for combining finite element analysis and boundary element techniques in order to compute the noise radiated from a panel subjected to boundary layer loading. The excitation is presented in terms of the auto and cross power spectral densities of the fluctuating wall pressure. The structural finite element model for the panel is divided into a number of sub-panels. A uniform fluctuating pressure is applied as excitation on each sub-panel separately. The corresponding vibration is computed, and is utilized as excitation for an acoustic boundary element analysis. The acoustic response is computed at any data recovery point of interest. The relationships between the acoustic response and the pressure excitation applied at each particular sub-panel constitute a set of transfer functions.