neuralfoil
NeuralFoil: An Airfoil Aerodynamics Analysis Tool Using Physics-Informed Machine Learning
Sharpe, Peter, Hansman, R. John
In conceptual aircraft design, the problem of shaping a typical wing is usually decomposed into two parts: planform design and airfoil design. The latter, which is the focus of this work, is a multidisciplinary design problem that requires consideration of a variety of aerodynamic, structural, and manufacturing objectives and constraints. A non-exhaustive list of major considerations could include: Profile drag across the expected operating range of the airfoil (spanning lift coefficients, Reynolds numbers, and Mach numbers), including adequate off-design performance [1]; Pitching moment and aft-camber coefficients, which can drive tail sizing (modifying trim drag), affect divergence speed; Hinge moments and control effectiveness of any control surfaces, which drive actuator design and weight; Stall behavior, which can affect handling qualities and safety; Thickness at various points, in order to accommodate fuel volume and required structural members to resist failure (e.g., by bending, buckling, divergence, flutter, or control reversal);[2] Sensitivity to boundary layer performance, freestream turbulence, and trips, all of which impose constraints on surface finish, cleanliness, and manufacturing tolerances [3-5]; Peak suction pressures, which affect the critical Mach number in transonic applications or cavitation in hydrodynamic applications; Shock stability and buffet considerations in transonic applications; Manufacturability, which might include flat-bottom airfoil sections, strictly-convex airfoil shapes (e.g., to
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