NeuralFoil comes with 8 different neural network models, with increasing levels of complexity: pip install neuralfoilįor example usage of NeuralFoil, see the AeroSandbox tutorials. NeuralFoil aims to be lightweight, with minimal dependencies and a tight, efficient, and easily-understood code-base (less than 500 lines of user-facing code). It also has many nice features (e.g., smoothness, vectorization, all in Python+NumPy) that make it much easier to use. Due to the wide variety of training data and the embedding of several physics-based invariants, this accuracy is seen even on out-of-sample airfoils (i.e., airfoils it wasn't trained on). NeuralFoil is ~10x faster than XFoil for a single analysis, and ~1000x faster for multipoint analysis, all with minimal loss in accuracy compared to XFoil. And, it's guaranteed to return an answer (no non-convergence issues), it's vectorized, and it's $C^\infty$-continuous (all very useful for gradient-based optimization). Using the AeroSandbox extension, NeuralFoil can give you viscous, compressible airfoil aerodynamics for (nearly) any airfoil, with control surface deflections, across $360^\circ$ angle of attack, at any Reynolds number, all nearly instantly (~5 milliseconds). NeuralFoil is available here as a pure Python+NumPy standalone, but it is also available within AeroSandbox, which extends it with many more advanced features. Under the hood, NeuralFoil consists of physics-informed neural networks trained on tens of millions of XFoil runs. NeuralFoil is a tool for rapid aerodynamics analysis of airfoils, similar to XFoil.
0 Comments
Leave a Reply. |
AuthorWrite something about yourself. No need to be fancy, just an overview. ArchivesCategories |