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NeuralFoil: An Airfoil Aerodynamics Analysis Tool Using Physics-Informed Machine Learning
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NeuralFoil: An Airfoil Aerodynamics Analysis Tool Using Physics-Informed Machine Learning
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NeuralFoil is an open-source Python-based tool for rapid aerodynamics analysis of airfoils, similar in purpose to XFoil. Speedups ranging from 8x to 1,000x over XFoil are demonstrated, after controlling for equivalent accuracy. NeuralFoil computes both global and local quantities (lift, drag, velocity distribution, etc.) over a broad input space, including: an 18-dimensional space of airfoil shapes, possibly including control deflections; a 360 degree range of angles of attack; Reynolds numbers from $10^2$ to $10^{10}$; subsonic flows up to the transonic drag rise; and with varying turbulence parameters. Results match those of XFoil closely: the mean relative error of drag is 0.37% on simple cases, and remains as low as 2.0% on a test dataset with numerous post-stall and transitional cases. NeuralFoil facilitates gradient-based design optimization, due to its $C^\infty$-continuous solutions, automatic-differentiation-compatibility, and bounded computational cost without non-convergence issues. NeuralFoil is a hybrid of physics-informed machine learning techniques and analytical models. Here, physics information includes symmetries that are structurally embedded into the model architecture, feature engineering using domain knowledge, and guaranteed extrapolation to known limit cases. This work also introduces a new approach for surrogate model uncertainty quantification that enables robust design optimization. This work discusses the methodology and performance of NeuralFoil with several case studies, including a practical airfoil design optimization study including both aerodynamic and non-aerodynamic constraints. Here, NeuralFoil optimization is able to produce airfoils nearly identical in performance and shape to expert-designed airfoils within seconds; these computationally-optimized airfoils provide a useful starting point for further expert refinement.
Forward citations
Cited by 5 Pith papers
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CoFINN: Conservation Flux Informed Neural Networks for Physics Problems Governed by Conservation Laws
Embedding finite-volume HLLC flux conservation into CNN training cuts airfoil drag prediction error substantially versus pure pixel MAE, especially in low-data regimes.
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AirfoilGen: A valid-by-construction and performance-aware latent diffusion model for airfoil generation
AirfoilGen presents a latent diffusion model with a novel circle-sweeping representation that produces geometrically valid airfoils conditioned on aerodynamic performance metrics, supported by a new dataset of over 20...
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AirfoilGen: A valid-by-construction and performance-aware latent diffusion model for airfoil generation
AirfoilGen generates valid airfoils with explicit control over aerodynamic performance using a novel circle-sweeping representation and a transformer-encoded conditional latent diffusion model.
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In-Context Black-Box Optimization with Unreliable Feedback
FICBO pretrains a feedback-aware transformer with a structured prior on feedback distortion to adaptively exploit or ignore unreliable auxiliary signals during in-context black-box optimization.
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Agentic Risk-Aware Set-Based Engineering Design
Multi-agent LLM system applies set-based design and Conditional Value-at-Risk to explore and risk-filter airfoil designs with human manager coordination.
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