Efficient Steady and Unsteady Flow Modeling for Arbitrarily Mis-Staggered Bladerow Under Influence of Inlet Distortion

Abstract

Accurate and efficient predictions of the steady and unsteady flow responses due to the blade-to-blade variation as well as due to the non-axisymmetric inlet distortion have been continually pursued. Computation of two problems concurrently has been rarely done in the past partly because of the need to perform whole annulus bladerow simulations, despite the advances in the current state-of-the-art methods with the phaseshift single passage simulations. The current work attempts to deal with this challenge by developing a new computational approach based on the principle of the multiscale method in the framework of a commercial solver (CFX). The methodology formulation relies on summation of the constituent source terms, each of which corresponds to a particular flow perturbation. The source term element corresponding to the blade-to-blade variation effect is linearly superimposed as in the classical Influence Coefficient Method. Only the relative positions between the reference blade and its neighbor matter in this method, thus enables an arbitrarily mis-staggered bladerow to be computed efficiently. In addition, the source term arisen due to the inlet distortion is calculated based on spatial Fourier transform. A key enabler is that the source term can be pre-computed using a small set of identical blade passages. The source term is then propagated to different spatial and temporal locations depending on the combination of the mis-staggering pattern and the inlet distortion. The multiscale treatment makes it possible to predict a high-resolution flow field effects on the base coarse mesh as if the fine mesh is solved, while achieving a computational gain. The source term summation method proposed in the current work has been validated using a uniformly staggered bladerow, and an arbitrarily mis-staggered bladerow in a clean inflow condition as well as that subject to an inlet distortion.