Some improvements of hybrid trim method for a helicopter rotor in forward flight

Abstract

The main difficulty of the trimming for helicopter rotor CFD (Computational Fluid Dynamics) lies in the high computational cost and low computational efficiency. The BET/CFD hybrid trim method, which uses the low-fidelity BET (Blade Element Theory) to calculate the Jacobian matrix and high-fidelity CFD method to get the aerodynamic force and moment for comparison with the target values, has been widely used in industry due to its high efficiency and low computational cost compared with the pure CFD method. However, this method also has two disadvantages: a complex C81 table needs to be established before the BET works; because of the low accuracy in computing the Jacobian matrix, the number of trim iterations is too large and the trimming may not even converge. In this paper, two improvements of the hybrid trim method have been proposed. Firstly, an Artificial Neural Networks (ANN) model has been developed to predict the aerodynamic coefficients in the BET. The ANN model greatly improves the efficiency and works for all kinds of airfoils without any extra operation. The second improvement is to divide the trimming process into two stages: the preliminary stage in which the Jacobian matrix is calculated using the BET with an ANN model, and the accurate stage in which the Jacobian matrix is obtained by the CFD method. The two-stage method avoids the second shortcoming mentioned above. The AH-1G rotor and ONERA 7A rotor in forward flight have been investigated to validate the present method. The trim results agree well with the flight test results and other computational results.