Fast Inverse Design of Transonic Airfoils by Combining Deep Learning and Efficient Global Optimization

Feng Deng, Jianmiao Yi

Aerospace 2023 article cited by 11

Source Open-access PDF More in Engineering & Materials

importance 4/10 solved Computational Fluid Dynamics and Aerodynamics aerospace engineering stage: research

Problem

Inverse design of transonic airfoils—finding a shape that produces a prescribed pressure distribution—is computationally slow.

Why it matters

Faster airfoil design shortens aircraft design cycles and reduces engineering cost.

What the AI did

A WGAN generates well-posed transonic pressure distributions, an EGO algorithm selects one matching prescribed features (suction peak, shock position), and a DCNN maps it to the airfoil shape.

Results

Airfoil shape with desired pressure distribution found in around one minute on a desktop computer (abstract truncated).

Impact

Per the authors, test cases verify the method finds target airfoils in about one minute.

Record

Venue	Aerospace
Published	2023
Work type	article
Authors	Feng Deng, Jianmiao Yi
Citations	11 (OpenAlex, at import time)
References	21
Model or method	WGAN and deep convolutional neural network (DCNN), coupled with EGO
Organization	not stated in source
Primary source	https://doi.org/10.3390/aerospace10020125
Open access	gold
Keywords	inverse design, transonic airfoil, WGAN, deep learning, efficient global optimization

Importance rating

4 / 10

real but narrower advance

Abstract frames the problem as slow inverse design of transonic airfoils; benefit is faster, cheaper aircraft design cycles. No scale or impact figures given; a real but narrow engineering advance.

Basis: inferred. Judged from the source text, which gives no explicit scale. Stated scale: not stated in source. Method and limits.

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