Aircraft design optimization made fast through modern automatic differentiation

Overview

AeroSandbox ✈️

by Peter Sharpe ( )

Downloads Monthly Downloads Build Status

Overview

AeroSandbox is a Python package for design optimization of engineered systems such as aircraft.

At its heart, AeroSandbox is an optimization suite that combines the ease-of-use of familiar NumPy syntax with the power of modern automatic differentiation.

This automatic differentiation dramatically improves optimization performance on large problems: design problems with tens of thousands of decision variables solve in seconds on a laptop.

AeroSandbox also comes with dozens of end-to-end-differentiable aerospace physics models, allowing you to simultaneously optimize an aircraft's aerodynamics, structures, propulsion, mission trajectory, stability, and more.

VLM Image VLM simulation of a glider, aileron deflections of +-30°. Runtime of 0.35 sec on a typical laptop (i7-8750H).

PANEL Image Panel simulation of a wing (extruded NACA2412, α=15°, AR=4). Note the strong three-dimensionality of the flow near the tip.

Getting Started

Installation

Use pip install aerosandbox[full] for a complete install.

For a lightweight installation with minimal dependencies, use pip install aerosandbox. All optimization, numerics, and physics models are included this headless install, but some visualization dependencies are not installed.

Tutorials, Examples, and Documentation

To get started, check out the tutorials folder here! All tutorials are viewable in-browser, or you can open them as Jupyter notebooks by cloning this repository.

For a more detailed and theory-heavy introduction to AeroSandbox, please see this thesis.

For a yet-more-detailed developer-level description of AeroSandbox modules, please see the developer README.

You can print documentation and examples for any AeroSandbox object by using the built-in help() function (e.g., help(asb.Airplane)). AeroSandbox code is also documented extensively in the source and contains hundreds of unit test examples, so examining the source code can also be useful.

Usage Details

One final point to note: as we're all sensible and civilized here, all inputs and outputs to AeroSandbox are expressed in base SI units, or derived units thereof (e.g, m, N, kg, m/s, J, Pa).

The only exception to this rule is when units are explicitly noted via variable name suffix. For example:

  • battery_capacity -> Joules
  • battery_capacity_watt_hours -> Watt-hours.

All angles are in radians, except for α and β which are in degrees due to long-standing aerospace convention. (In any case, units are marked on all function docstrings.)

If you wish to use other units, consider using aerosandbox.tools.units to convert easily.

Project Details

Contributing

Please feel free to join the development of AeroSandbox - contributions are always so welcome! If you have a change you'd like to make, the easiest way to do that is by submitting a pull request.

The text file CONTRIBUTING.md has more details for developers and power users.

If you've already made several additions and would like to be involved in a more long-term capacity, please message me! Contact information can be found next to my name near the top of this README.

Donating

If you like this software, please consider donating to support development via PayPal or GitHub Sponsors! I'm a grad student, so every dollar that you donate helps wean me off my diet of instant coffee and microwaved ramen noodles.

Bugs

Please, please report all bugs by creating a new issue at https://github.com/peterdsharpe/AeroSandbox/issues!

Versioning

AeroSandbox loosely uses semantic versioning, which should give you an idea of whether or not you can probably expect backward-compatibility and/or new features from any given update. However, the code is a work in progress and things change rapidly - for the time being, please freeze your version of AeroSandbox for any serious deployments. Commercial users: I'm more than happy to discuss consulting work for active AeroSandbox support if this package proves helpful!

Citation

If you find AeroSandbox useful in a research publication, please cite it using the following BibTeX snippet:

@mastersthesis{aerosandbox,
    title = {AeroSandbox: A Differentiable Framework for Aircraft Design Optimization},
    author = {Sharpe, Peter D.},
    school = {Massachusetts Institute of Technology},
    year = {2021}
}

License

MIT License, full terms here.

Stargazers over time

Stargazers over time

Owner
Peter Sharpe
MIT AeroAstro PhD Candidate | Engineering design optimization, aircraft design, and aerodynamics. Hello and welcome to my GitHub! :)
Peter Sharpe
Non-Homogeneous Poisson Process Intensity Modeling and Estimation using Measure Transport

Non-Homogeneous Poisson Process Intensity Modeling and Estimation using Measure Transport This GitHub page provides code for reproducing the results i

Andrew Zammit Mangion 1 Nov 08, 2021
A CV toolkit for my papers.

PyTorch-Encoding created by Hang Zhang Documentation Please visit the Docs for detail instructions of installation and usage. Please visit the link to

Hang Zhang 2k Jan 04, 2023
Implementation of DocFormer: End-to-End Transformer for Document Understanding, a multi-modal transformer based architecture for the task of Visual Document Understanding (VDU)

DocFormer - PyTorch Implementation of DocFormer: End-to-End Transformer for Document Understanding, a multi-modal transformer based architecture for t

171 Jan 06, 2023
Read and write layered TIFF ImageSourceData and ImageResources tags

Read and write layered TIFF ImageSourceData and ImageResources tags Psdtags is a Python library to read and write the Adobe Photoshop(r) specific Imag

Christoph Gohlke 4 Feb 05, 2022
Official code of our work, AVATAR: A Parallel Corpus for Java-Python Program Translation.

AVATAR Official code of our work, AVATAR: A Parallel Corpus for Java-Python Program Translation. AVATAR stands for jAVA-pyThon progrAm tRanslation. AV

Wasi Ahmad 26 Dec 03, 2022
TorchFlare is a simple, beginner-friendly, and easy-to-use PyTorch Framework train your models effortlessly.

TorchFlare TorchFlare is a simple, beginner-friendly and an easy-to-use PyTorch Framework train your models without much effort. It provides an almost

Atharva Phatak 85 Dec 26, 2022
Geometric Vector Perceptron --- a rotation-equivariant GNN for learning from biomolecular structure

Geometric Vector Perceptron Code to accompany Learning from Protein Structure with Geometric Vector Perceptrons by B Jing, S Eismann, P Suriana, RJL T

Dror Lab 85 Dec 29, 2022
Customizable RecSys Simulator for OpenAI Gym

gym-recsys: Customizable RecSys Simulator for OpenAI Gym Installation | How to use | Examples | Citation This package describes an OpenAI Gym interfac

Xingdong Zuo 14 Dec 08, 2022
Unofficial PyTorch reimplementation of the paper Swin Transformer V2: Scaling Up Capacity and Resolution

PyTorch reimplementation of the paper Swin Transformer V2: Scaling Up Capacity and Resolution [arXiv 2021].

Christoph Reich 122 Dec 12, 2022
Adversarial Attacks on Probabilistic Autoregressive Forecasting Models.

Attack-Probabilistic-Models This is the source code for Adversarial Attacks on Probabilistic Autoregressive Forecasting Models. This repository contai

SRI Lab, ETH Zurich 25 Sep 14, 2022
Source code for "Progressive Transformers for End-to-End Sign Language Production" (ECCV 2020)

Progressive Transformers for End-to-End Sign Language Production Source code for "Progressive Transformers for End-to-End Sign Language Production" (B

58 Dec 21, 2022
O-CNN: Octree-based Convolutional Neural Networks for 3D Shape Analysis

O-CNN This repository contains the implementation of our papers related with O-CNN. The code is released under the MIT license. O-CNN: Octree-based Co

Microsoft 607 Dec 28, 2022
DI-smartcross - Decision Intelligence Platform for Traffic Crossing Signal Control

DI-smartcross DI-smartcross - Decision Intelligence Platform for Traffic Crossin

OpenDILab 213 Jan 02, 2023
As-ViT: Auto-scaling Vision Transformers without Training

As-ViT: Auto-scaling Vision Transformers without Training [PDF] Wuyang Chen, Wei Huang, Xianzhi Du, Xiaodan Song, Zhangyang Wang, Denny Zhou In ICLR 2

VITA 68 Sep 05, 2022
TANL: Structured Prediction as Translation between Augmented Natural Languages

TANL: Structured Prediction as Translation between Augmented Natural Languages Code for the paper "Structured Prediction as Translation between Augmen

98 Dec 15, 2022
Tensorflow implementation of Semi-supervised Sequence Learning (https://arxiv.org/abs/1511.01432)

Transfer Learning for Text Classification with Tensorflow Tensorflow implementation of Semi-supervised Sequence Learning(https://arxiv.org/abs/1511.01

DONGJUN LEE 82 Oct 22, 2022
Pytorch implementation of FlowNet 2.0: Evolution of Optical Flow Estimation with Deep Networks

flownet2-pytorch Pytorch implementation of FlowNet 2.0: Evolution of Optical Flow Estimation with Deep Networks. Multiple GPU training is supported, a

NVIDIA Corporation 2.8k Dec 27, 2022
Official implement of Evo-ViT: Slow-Fast Token Evolution for Dynamic Vision Transformer

Evo-ViT: Slow-Fast Token Evolution for Dynamic Vision Transformer This repository contains the PyTorch code for Evo-ViT. This work proposes a slow-fas

YifanXu 53 Dec 05, 2022
Official Pytorch Implementation of 'Learning Action Completeness from Points for Weakly-supervised Temporal Action Localization' (ICCV-21 Oral)

Learning-Action-Completeness-from-Points Official Pytorch Implementation of 'Learning Action Completeness from Points for Weakly-supervised Temporal A

Pilhyeon Lee 67 Jan 03, 2023
Replication Package for "An Empirical Study of the Effectiveness of an Ensemble of Stand-alone Sentiment Detection Tools for Software Engineering Datasets"

Replication Package for "An Empirical Study of the Effectiveness of an Ensemble of Stand-alone Sentiment Detection Tools for Software Engineering Data

2 Oct 06, 2022