Efficient Training Data Generation for Reduced-Order Modeling in a Transonic Flight Regime

Haojie Liu ,Yonghui Zhao

doi:10.1155/2018/4083538

Abstract

In this study, a time-dependent surrogate approach is presented to generate the training data for identifying the reduced-order model of an unsteady aerodynamic system with the variation of mean angle of attack and Mach number in a transonic flight regime. For such a purpose, a finite set of flight samples are selected to cover the flight range of concern at first. Subsequently, the unsteady aerodynamic outputs of the system under given inputs of filtered white Gaussian noise at these flight samples are simulated via CFD technique which solves Euler equations. The unsteady aerodynamic outputs, which are viewed as a time-dependent function of flight parameters, can be approximated via the Kriging technique at each time step. By this way, the training data for any combination of flight parameters in the range of concern can be obtained without performing any further CFD simulations. To illustrate the accuracy and validity of the training data generated via the proposed approach, the constructed data are used to identify the reduced-order aerodynamic models of a NACA 64A010 airfoil via a robust subspace identification algorithm. The unsteady aerodynamics and aeroelastic responses under various flight conditions in a transonic flight regime are computed. The results agree well with those obtained by using the training data of CFD technique.

Highlights

The techniques of computational fluid dynamics (CFD) have been widely used to simulate both linear and nonlinear flow fields for various flight vehicles. It is still timeconsuming for any high-fidelity CFD techniques to simulate the unsteady aerodynamic loads due to the broad variation of parameters, such as different combinations of mean angle of attack and Mach number
Under each flight condition defined by M∞,i and α0,i, an unsteady aerodynamic computation needs to be performed via direct CFD simulation under the given filtered white Gaussian noise (FWGN) excitation
The Kriging technique is used in the approach to approximate the relationship between the unsteady aerodynamic outputs of the system and the flight parameters

Summary

Introduction

The techniques of computational fluid dynamics (CFD) have been widely used to simulate both linear and nonlinear flow fields for various flight vehicles. A surrogate-based recurrence framework ROM was developed to model the unsteady aerodynamics on a rotating airfoil [14] It is time-consuming to generate the training data for each combination of parameters in the parameter space. Almost all aforementioned unsteady aerodynamic ROMs, which take flight parameter variations into account, require enough training data to capture the dynamic characteristics of the unsteady aerodynamic systems It is time-consuming to generate the training data via any direct CFD simulations when both Mach number and mean angle of attack are taken into consideration. The motivation of this study is to generate the training data efficiently for a range of flight parameters including mean angle of attack and Mach number in a transonic flight regime For such a purpose, a time-dependent surrogate approach is proposed.

Theoretical Background

Case Studies

14: The aeroelastic responses of the airfoil