Reduced-order modeling of dynamic stall using neuro-fuzzy inference system and orthogonal functions

Abstract

To consider stall flutter in the design procedure of a blade, accurate models of flow loading are needed. This paper first presents a numerical simulation of an airfoil undergoing a deep dynamic stall employing a computational fluid dynamics code. Overset and polyhedral grid techniques are adopted to accurately simulate the flow field at high angles of attack. Having validated the simulation, the occurrence of stall flutter over a pitching airfoil with an increase in amplitude and frequency of oscillations is examined. The results express that the amplitude of the lift and pitching moment depends on the amplitude of the forced oscillation and there are higher harmonics of the pitching moment compared to the forced oscillation frequency content, both indicating the nonlinearity of aerodynamic lift and pitching moment. Subsequently, a nonlinear reduced model of the dynamic stall is derived using a fuzzy inference system (FIS) and the adaptive network-based FIS (ANFIS). Due to the unsatisfactory results of modeling, especially at post-stall angles of attack, the Gram–Schmidt orthogonalization technique is used to construct a more complex structure of the input variables. The new higher-order input variables have been re-employed by FIS and ANFIS. The results show that excellent modeling is achieved by ANFIS between the new structure of the inputs and the corresponding aerodynamic coefficients using only 10% of input–output data. Having found an appropriate relation, the proposed reduced-order model could properly predict the aerodynamic response of the pitching airfoil at two reduced frequencies.