FAST AEROBASE | Aircraft and Engine Registry Open-Source Database

A transparent resource including over 400 aircraft and 200 engine data.

Overview

Future Aircraft Sizing Tool (FAST) utilizes historical data driven regressions to make predicitions about aircraft parameters before and during the sizing process. This data is packaged as a standalone database, called FAST AEROBASE. AEROBASE includes over 400 aircraft and 200 engines, spanning a wide range of sizes, entry-into-service years, and configurations. Compiled from publicly available, trusted sources such as FAA and EASA type certificate data sheets (TCDS), airport planning manuals, and manufacturer specifications, it provides a clear, data-driven perspective on the evolution of key performance parameters.

Exploring AEROBASE: Historical Trends in Aircraft Design

For a detailed analysis of the trends captured in AEROBASE, refer to our recent study:

  • Provisional Citation: Acar, H., Arnson, M., Tsai, M., & Cinar, G., “Historical Trends and Future Projections of Key Performance Parameters in Aircraft Design,” under review, Journal of Aircraft, 2024.

This paper examines the evolution of key performance parameters (KPPs)—including operational empty weight (OEW/MTOW), thrust-to-weight ratio (T/W), fuel efficiency (TSFC), and lift-to-drag ratio (L/D)—using AEROBASE’s comprehensive dataset of over 400 aircraft and 200 engines.

By analyzing historical data, the study identifies the drivers behind observed trends and provides insights into:

  • Structural efficiency and weight trends across different aircraft sizes and mission profiles,
  • Improvements in propulsion efficiency and their practical limits,
  • The evolution of aerodynamic performance and emerging challenges for conventional aircraft configurations,
  • How historical trends can inform future projections.

The findings offer a robust understanding of how technological advancements, physical constraints, and market forces have shaped aircraft design and performance over time, while providing informed projections to guide next-generation aircraft concepts.

For a deeper understanding of the data and findings, access the full manuscript here.

Accessing the FAST AEROBASE

All of the data used in the FAST regressions is available for free on the FAST Github, in the database package. The database is stored in three formats. The first is a large .xlsx workbook, with four different sheets. Each sheet stores information on: turbofan aircraft, turboprop aircraft, turbofan engines, or turboprop engines. The second format is two condensed .xlxs workbooks which can easily be imported into a statistical analysis tool such as JMP. The third format is a .mat file, which can be loaded into MATLAB. After loading, six variables will be present in the workspace: TurbofanAC, TurbopropAC, TurbofanEngines, TurbopropEngines, FanUnitsReference, and PropUnitsReference. These variables store the same information as the .xlxs formats, but in a set of nested structures. The UnitReference variables are identical to any specific aircraft in the AC variables, but store units instead of numerical values. Engine units are stored in Specs » Propulsion » Engine.

Websitescreenshot
Database files in the Database Package of the FAST Github repository. EAP_Databases_Offline.xlsx houses the entire database, as does IDEAS_DB.mat, while the database is split between turbofan and turboprop aircraft in the JMPInputSheet files (intented to be imported into statisitcal analysis software).

Get Started with FAST

FAST is open-source and freely available under an Apache 2.0 license. To start using FAST, visit the GitHub repository here. The repository contains detailed documentation, including a comprehensive user guide to help you get started with the software.

If you use FAST in your research or projects, please cite our work as follows (note that these are provisional citations, awaiting final release in 2025):

  • Aircraft Sizing: Mokotoff, P., Arnson, M., & Cinar, G., “FAST: A Future Aircraft Sizing Tool for Electrified Aircraft Design,” in press, AIAA SciTech Forum, 2025.
  • Regressions: Arnson, M. and Aljaber, R. and Cinar, G., “Predicting Conceptual Aircraft Design Parameters Using Gaussian Process Regressions on Historical Data,” in press, AIAA SciTech Forum, 2025.
  • Visualization: Khailany, N., Mokotoff, P., & Cinar, G., “Aircraft Geometry and Propulsion Architecture Visualization for the Future Aircraft Sizing Tool (FAST)”, in press, AIAA SciTech Forum, 2025.
  • Historical Trends: Acar, H., Arnson, M., Tsai, M., & Cinar, G., ‘‘Historical Trends and Future Projections of Key Performance Parameters in Aircraft Design’’, under review, Journal of Aircraft, 2024.

FAST Video Tutorials Explore our comprehensive video tutorials to get the most out of the Future Aircraft Sizing Tool (FAST). Hosted by the IDEAS Lab researchers, these tutorials cover everything from installation to advanced features. Visit our YouTube channel for all videos.

Stay tuned for more tutorials and updates.

FAST Video Tutorials Explore our comprehensive video tutorials to get the most out of the Future Aircraft Sizing Tool (FAST). Hosted by the IDEAS Lab researchers, these tutorials cover everything from installation to advanced features. Visit our YouTube channel for all videos.

Stay tuned for more tutorials and updates.

Watch the Playlist

Visit our YouTube channel for more tutorials.

Sign up for the FAST newsletter To stay informed about upcoming papers, new releases, and news about FAST, sign up for our newsletter here:

Funding and Acknowledgment: This work is sponsored by the NASA Aeronautics Research Mission Directorate and the Electrified Powertrain Flight Demonstration (EPFD) project, “Development of a Parametrically Driven Electrified Aircraft Design and Optimization Tool”. The IDEAS Lab would like to thank Ralph Jansen, Andrew Meade, Karin Bozak, Amy Chicatelli, Noah Listgarten, Dennis Rohn, and Gaudy Bezos-O’Connor from the NASA EPFD project for supporting this work and providing valuable technical input and feedback throughout the duration of the project.

Glenn Engineering and Research Support Contract (GEARS) Contract No. 80GRC020D0003

Maxfield Arnson
Maxfield Arnson
PhD Student and Graduate Research Assistant

Maxfield Arnson is a graduate student research assistant in the IDEAS Lab at the University of Michigan.

Hüseyin Acar
Hüseyin Acar
PhD student and Graduate Research Assistant

Hüseyin Acar is a Ph.D. candidate at the IDEAS Lab at U-M, focusing on quantifying the benefits and challenges of sustainable aviation technologies.

Paul Mokotoff
Paul Mokotoff
PhD Student and Graduate Research Assistant

Paul Mokotoff is a graduate student research assistant in the IDEAS Lab at the University of Michigan.

Gökçin Çınar
Gökçin Çınar
Assistant Professor of Aerospace Engineering