RAPID LIFT AND DRAG PREDICTION TOOL WITH MULTI-LAYER PERCEPTRON MODEL FOR SUPERCRITICAL AIRFOILS

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Date

2023-1-20

Author

Atlı, Caner

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In the early stages of aircraft design, selecting the shape of parts such as wings and tails can be time-consuming. Typically, tools that can provide rapid predictions of aerodynamic coefficients are used in these stages to reduce the duration of the preliminary design phase. However, available tools and research cannot provide accurate and reliable solutions in transonic and fully turbulent flows. A new tool is developed to provide aerodynamic properties such as drag, lift, maximum lift, and drag divergence Mach number by utilizing machine learning methodologies. The tool consist of a reliable shape descriptor for airfoil shape based on Class Shape Function Transformation. Then a Multilayer Perceptron machine learning method is developed using high-accuracy CFD solution database and airfoil shape descriptions. Then, new airfoil shapes and aerodynamic data is produced consistently and accurately in the transonic and turbulent flow regime. In the end, we have achieved a solution accuracy with an R2 score of 0.9935 for validation set lift coefficient using the new tool. Predicted aerodynamic coefficients are drag coefficient, lift coefficient and drag divergence Mach number. The tool provides solutions for 27000 cases within seconds or angle of attack range of -2° to 8°.

Subject Keywords

Deep Learning, Rapid Predictions, Drag Divergence, Preliminary Design

URI

https://hdl.handle.net/11511/102113

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Graduate School of Natural and Applied Sciences, Thesis

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Citation Formats

C. Atlı, “RAPID LIFT AND DRAG PREDICTION TOOL WITH MULTI-LAYER PERCEPTRON MODEL FOR SUPERCRITICAL AIRFOILS,” M.S. - Master of Science, Middle East Technical University, 2023.