Evaluation of novel-objective functions in the design optimization of a transonic rotor by using deep learning - Zeinalzadeh, A.; Pakatchian, M.R. (Engineering applications of computational fluid mechanics, 2021)
Design optimization of transonic airfoils for rotary blades is a challenging subject that remarkably affects the stage and overall performance of axial-flow compressors. This paper describes a surrogate-based multi-objective optimization process over a transonic rotary blade. This blade works in the first high-pressure stage of a pre-designed industrial axial compressor. It experiences a massive separation behind an impinging shock wave over its suction side, resulting in very low efficiency of the whole stage. The key components of the current approach involve the application of novel-objective functions over the pressure distribution of airfoils, called the location of the shock wave and a flat-roof-top factor, to design supercritical airfoils. Moreover, to ensure the advantages of having an attached boundary layer and a high efficient blade, the area of separated boundary layer is also defined alongside other well-known objective functions related to the polar loss diagram. Notably, a sequential feed-forward multi-layer perceptron is designed to construct a mapping between airfoil geometrical variables and the objective functions. A numerical simulation of the whole compressor has shown an efficiency improvement of about 10% and 0.17% for the first stage and the whole compressor, respectively, and an attached boundary layer with a supercritical pressure distribution when employing the optimized rotor blade at the design stage.