Improved Models for Prediction of Locally Intense Aeroacoustic Loads and Vibration Environments

ATA Engineering, Inc. proposes an STTR program to develop innovative tools and methods that will significantly improve the accuracy of random vibration response predictions for aerospace structures under critical inhomogeneous aeroacoustic loads. This will allow more accurate predictions of structural responses to be made, potentially reducing vehicle weight and cost and improving the reliability of these structures. Empirical wind tunnel test data will be used as a basis to develop innovative methods to characterize the surface fluctuating pressures encountered by launch vehicles during ascent, and then to accurately predict the random vibration environment caused by these loads. The wind tunnel test will measure both the surface fluctuating pressure and the resulting vibration in a flexible panel. Phase I will be spent performing pre-test engineering to reduce Phase II risk, generating drawings for construction of test articles, and deriving the test matrix for the Phase II wind tunnel test. The objective of Phase I is to demonstrate that the proposed Phase II wind tunnel test will be able to provide the test data necessary to improve predictions of fluctuating pressures and random vibration during ascent. Phase II will be used to perform the wind tunnel tests for compression corners, expansion corners, and protuberances. The vibration and fluctuating pressure data from these tests will be used to develop more accurate models to predict the auto- and cross-spectra of surface fluctuating pressures during ascent, followed by the development of coupling models to predict the resulting spacecraft structural vibrations. A critical improvement over current methods will be the inclusion of a statistical basis which will enable prediction of both mean and maximum expected environments. The experimental data in Phase II will also be made available to other researchers performing unsteady computational fluid dynamics simulations as validation data.