Airfoil Analysis and Optimization Using a Petrov–Galerkin Finite Element and Machine Learning

نویسندگان

چکیده

For the analysis of low-speed incompressible fluid dynamics with turbulence around airfoils, we developed a finite element formulation based on stabilized pressure and velocity formulation. To shape optimization bidimensional this is applied using machine learning (TensorFlow) public domain global algorithms. The goal to maximize lift-over-drag ratio by class-shape function transformation (CST) parameterization technique learning. Specifically, propose equal-order three-node triangles for flow problem, standard approximate distance (ADF) required in stage, Spalart–Allmaras model. backward Euler time integration was employed. An implicit time-integration algorithm adopted, solution obtained Newton–Raphson method. This made possible symbolic form via Mathematica AceGen package. Three benchmarks are presented, Reynolds numbers up 1×107, demonstrating remarkable robustness. After assessment new element, used four angles attack calculate airfoil designs that maximized MMCL/CD.

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ژورنال

عنوان ژورنال: Aerospace

سال: 2023

ISSN: ['2226-4310']

DOI: https://doi.org/10.3390/aerospace10070638