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
Vehicles Counting using YOLOv4 + DeepSORT + Flask + Ngrok

A project for counting vehicles using YOLOv4 + DeepSORT + Flask + Ngrok

Duong Tran Thanh 37 Dec 16, 2022
Blender add-on: Add to Cameras menu: View → Camera, View → Add Camera, Camera → View, Previous Camera, Next Camera

Blender add-on: Camera additions In 3D view, it adds these actions to the View|Cameras menu: View → Camera : set the current camera to the 3D view Vie

German Bauer 11 Feb 08, 2022
Learning from History: Modeling Temporal Knowledge Graphs with Sequential Copy-Generation Networks

CyGNet This repository reproduces the AAAI'21 paper “Learning from History: Modeling Temporal Knowledge Graphs with Sequential Copy-Generation Network

CunchaoZ 89 Jan 03, 2023
[SIGGRAPH Asia 2021] DeepVecFont: Synthesizing High-quality Vector Fonts via Dual-modality Learning.

DeepVecFont This is the homepage for "DeepVecFont: Synthesizing High-quality Vector Fonts via Dual-modality Learning". Yizhi Wang and Zhouhui Lian. WI

Yizhi Wang 17 Dec 22, 2022
Deploying PyTorch Model to Production with FastAPI in CUDA-supported Docker

Deploying PyTorch Model to Production with FastAPI in CUDA-supported Docker A example FastAPI PyTorch Model deploy with nvidia/cuda base docker. Model

Ming 68 Jan 04, 2023
Language Models for the legal domain in Spanish done @ BSC-TEMU within the "Plan de las Tecnologías del Lenguaje" (Plan-TL).

Spanish legal domain Language Model ⚖️ This repository contains the page for two main resources for the Spanish legal domain: A RoBERTa model: https:/

Plan de Tecnologías del Lenguaje - Gobierno de España 12 Nov 14, 2022
Deep Halftoning with Reversible Binary Pattern

Deep Halftoning with Reversible Binary Pattern ICCV Paper | Project Website | BibTex Overview Existing halftoning algorithms usually drop colors and f

Menghan Xia 17 Nov 22, 2022
A general python framework for single object tracking in LiDAR point clouds, based on PyTorch Lightning.

Open3DSOT A general python framework for single object tracking in LiDAR point clouds, based on PyTorch Lightning. The official code release of BAT an

Kangel Zenn 172 Dec 23, 2022
A PyTorch implementation of the paper Mixup: Beyond Empirical Risk Minimization in PyTorch

Mixup: Beyond Empirical Risk Minimization in PyTorch This is an unofficial PyTorch implementation of mixup: Beyond Empirical Risk Minimization. The co

Harry Yang 121 Dec 17, 2022
Spatial-Temporal Transformer for Dynamic Scene Graph Generation, ICCV2021

Spatial-Temporal Transformer for Dynamic Scene Graph Generation Pytorch Implementation of our paper Spatial-Temporal Transformer for Dynamic Scene Gra

Yuren Cong 119 Jan 01, 2023
Convolutional neural network that analyzes self-generated images in a variety of languages to find etymological similarities

This project is a convolutional neural network (CNN) that analyzes self-generated images in a variety of languages to find etymological similarities. Specifically, the goal is to prove that computer

1 Feb 03, 2022
Generate pixel-style avatars with python.

face2pixel Generate pixel-style avatars with python. Run: Clone the project: git clone https://github.com/theodorecooper/face2pixel install requiremen

Theodore Cooper 2 May 11, 2022
Implementation of the paper titled "Using Sampling to Estimate and Improve Performance of Automated Scoring Systems with Guarantees"

Using Sampling to Estimate and Improve Performance of Automated Scoring Systems with Guarantees Implementation of the paper titled "Using Sampling to

MIDAS, IIIT Delhi 2 Aug 29, 2022
Optimizaciones incrementales al problema N-Body con el fin de evaluar y comparar las prestaciones de los traductores de Python en el ámbito de HPC.

Python HPC Optimizaciones incrementales de N-Body (all-pairs) con el fin de evaluar y comparar las prestaciones de los traductores de Python en el ámb

Andrés Milla 12 Aug 04, 2022
TLoL (Python Module) - League of Legends Deep Learning AI (Research and Development)

TLoL-py - League of Legends Deep Learning Library TLoL-py is the Python component of the TLoL League of Legends deep learning library. It provides a s

7 Nov 29, 2022
On the adaptation of recurrent neural networks for system identification

On the adaptation of recurrent neural networks for system identification This repository contains the Python code to reproduce the results of the pape

Marco Forgione 3 Jan 13, 2022
End-to-End Object Detection with Fully Convolutional Network

This project provides an implementation for "End-to-End Object Detection with Fully Convolutional Network" on PyTorch.

472 Dec 22, 2022
Demonstrates iterative FGSM on Apple's NeuralHash model.

apple-neuralhash-attack Demonstrates iterative FGSM on Apple's NeuralHash model. TL;DR: It is possible to apply noise to CSAM images and make them loo

Lim Swee Kiat 11 Jun 23, 2022
Code repository for the paper "Doubly-Trained Adversarial Data Augmentation for Neural Machine Translation" with instructions to reproduce the results.

Doubly Trained Neural Machine Translation System for Adversarial Attack and Data Augmentation Languages Experimented: Data Overview: Source Target Tra

Steven Tan 1 Aug 18, 2022
[Arxiv preprint] Causality-inspired Single-source Domain Generalization for Medical Image Segmentation (code&data-processing pipeline)

Causality-inspired Single-source Domain Generalization for Medical Image Segmentation Arxiv preprint Repository under construction. Might still be bug

Cheng 31 Dec 27, 2022