Neuralfoil New!

XFoil has been the industry standard for rapid aerodynamic analysis of airfoils. However, as modern aerospace engineering demands faster, more robust design cycles, traditional panel methods often struggle with convergence issues and computational bottlenecks. Enter NeuralFoil , a cutting-edge open-source Python tool that bridges the gap between classical physics and modern machine learning. What is NeuralFoil? NeuralFoil is a physics-informed machine learning (PIML) surrogate model designed for the near-instantaneous aerodynamic analysis of airfoils. It doesn't just "guess" based on data; it embeds fundamental aerodynamic principles—like symmetries and known limit cases—directly into its neural architecture. Key Performance Highlights NeuralFoil is engineered to be a drop-in, high-performance alternative to XFoil, offering: Extreme Speed

It can handle an 18-dimensional space of airfoil shapes, Reynolds numbers ranging from 10210 squared 101010 to the tenth power , and a full 360-degree range of angles of attack.

Where NeuralFoam truly shines is when you are generating random shapes for optimization (e.g., using CST methods or PARSEC). neuralfoil

You don't need a Fortran compiler. You just need Python.

You can install the package directly via PyPI or explore the source code on GitHub. XFoil has been the industry standard for rapid

NeuralFoil: The AI Revolution in Airfoil Design and Aerodynamics

Enter NeuralFoil , a revolutionary open-source Python tool that leverages to deliver aerodynamic results up to 1,000x faster than XFoil without sacrificing accuracy. What is NeuralFoil? What is NeuralFoil

import numpy as np import matplotlib.pyplot as plt from neuralfoil import get_aero_from_airfoil_name

Link to the repo is in the comments.

It is approximately 10x to 30x faster than XFoil for a single analysis and up to 1,000x faster for large batch (multipoint) analyses.