Resumen
The variable cycle engine switches working modes by way of changing variable-geometry components to achieve the dual advantages of high unit thrust and low specific fuel consumption. Due to the lack of a large amount of rig test data and the complex modeling of rotating components, the incomplete characteristics of the variable-geometry rotating components lead to the non-convergence of the component-level model of the variable cycle engine, which makes it difficult to design the follow-up control system. Aiming at this problem, a characteristics modeling method of variable-geometry rotating components for variable cycle engine based on reference characteristic curves is proposed in this paper. This method establishes a neural network estimation model for the offset coefficients of key component operating points based on the characteristic law of the maturely designed variable-geometry rotating component. Combining the neural network model and the reference characteristic curves of the variable-geometry component to be designed, the offset positions of the operating points for positive and negative guide vane angles are determined. Instead of directly connecting operating points to generate characteristic lines, this paper solves the Bezier curve optimization problem based on sequential quadratic programming (SQP) to smoothly fit characteristic lines. Thereby, component characteristics that conform to the actual variable-geometry characteristics can be quickly established in the absence of rig test data. The simulations show that the characteristics of the variable-geometry rotating components established by the proposed method have satisfactory accuracy and reliability, which further improves the operation stability of the component-level model of the variable cycle engine.