Efficient Reduced-Order Aerodynamic Modeling in Low-Reynolds-Number Incompressible Flows
Haojie Liu,Xiumin Gao,Zhaolin Chen,Fan Yang
Abstract: This study aims to develop a non-intrusive reduced-order aerodynamic modeling framework for agile motion across a broad operating range in low-Reynolds-number incompressible flows. Since most previous reduced-order models are only valid in the neighborhood of the operating point, large amounts of high-fidelity input-output data have to be generated to cover the interested operating range, which requires expensive computational resources. To tackle such an issue, a signal interpolation approach by combining the discrete empirical interpolation method (DEIM) with Kriging technique is proposed to approximate the aerodynamic responses to a prescribed linear ramp-step maneuver with remarkable accuracy, instead of performing computational fluid dynamics (CFD) simulation at each operating point. The interpolated input-output data can be used to establish low-dimensional state-space aerodynamic models, by identifying the stability derivatives first and subsequently capturing the remaining transient dynamics via the eigensystem realization algorithm (ERA). To demonstrate the proposed framework, unsteady lift coefficients of a two-dimensional flat plate pitching about the leading edge are investigated over the range with Reynolds numbers 100∼500 and base angles of attack 0~10 deg. Numerical results show good agreement between the reduced-order models and high-fidelity CFD simulations.
原文链接：https://www.sciencedirect.com/science/article/pii/S1270963821007094?via%3Dihub
