Evaluation of novel-objective functions in the design optimization of a transonic rotor by using deep learning

• Published in: Engineering applications of computational fluid mechanics Vol. 15; no. 1; pp. 561 - 583
• Main Authors: Zeinalzadeh, A., Pakatchian, M.R.
• Format: Journal Article
• Language: English
• Published: Hong Kong Taylor & Francis 01.01.2021; Taylor & Francis Ltd; Taylor & Francis Group
• Subjects: Airfoil design optimization; axial compressor; Boundary layers; Deep learning; Design factors; Design optimization; Flat roofs; MISES; Multilayer perceptrons; Multilayers; Multiple objective analysis; Pressure distribution; Rotor blades; Rotor blades (turbomachinery); Suction; Supercritical airfoils; supercritical pressure distribution; Supercritical pressures; transonic rotor; Turbocompressors

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...