An Improved CFD-synthesis Method for Predicting Inlet Dynamic Distortion

Abstract

This article presents an improved computational fluid dynamics (CFD)-synthesis method to predict dynamic distortion. A steady-state flow field is derived from a CFD solution, through which are acquired total pressure, density, turbulence kinetic energy and others in steady-state at an aerodynamic interface plane (AIP). Back-propagation artificial neural network (BP ANN) is used to find out the relationship between the measured flight turbulence and the CFD-computed turbulence parameters. The dynamic pressure is obtained by incorporating CFD-found total steady-state pressure with fluctuating pressure. Finally, the dynamic distortion is predicted by means of the synthesized dynamic pressure. The fairly good agreement between the computed inlet surface pressure and the flight test data bears out the reliability of CFD solution used in this article. To validate the proposed method, six sets of flight test data are used and the results show that the predicted dynamic distortion is well in line with the distortion displayed in flight tests. An examination of the traditional method is also accomplished and the comparison also shows that the proposed method is superior to the traditional one in higher consistency with flight test data.