An Aerospace Vehicle Environment for Designing Future Aircraft

SUAVE is a conceptual level aircraft design environment built with the ability to analyze and optimize both conventional and unconventional designs. This capability is achieved in part by allowing analysis information for aircraft to be drawn from multiple sources. Many other software tools for aircraft conceptual design rely on fixed empirical correlations and other handbook approximation. SUAVE instead provides a framework that can be used to design aircraft featuring advanced technologies by augmenting relevant correlations with physics-based methods.

Blended Wing Body

SUAVE is an open source suite constructed as a modular set of analysis tools written in Python. Additional capabilities can be incorporated using extensible interfaces and prototyped with a top-level script. The flexibility of the environment allows the creation of arbitrary mission profiles, unconventional propulsion networks, and right-fidelity at right-time discipline analyses. SUAVE is currently being developed in the Aerospace Design Lab at Stanford University.


We've recently presented papers on the technical background of SUAVE as applied to the analysis and optimization of aerospace vehicles. The first paper describes the models available and the motivations for the programming structures used in the package. The second paper details the schematics of setting up optimization problems and sample results.

T. Lukaczyk, A. Wendorff, E. Botero, T. MacDonald, T. Momose, A. Variyar, J. M. Vegh, M. Colonno, T. Economon, J. J. Alonso, T. Orra, C. Ilario, "SUAVE: An Open-Source Environment for Multi-Fidelity Conceptual Vehicle Design", 16th AIAA Multidisciplinary Analysis and Optimization Conference, Dallas, TX, June 2015.

E. Botero, A. Wendorff, T. MacDonald, A. Variyar, J. M. Vegh, T. Lukaczyk, J. J. Alonso, T. Orra, C. Ilario da Silva. "SUAVE: An Open-Source Environment for Conceptual Vehicle Design and Optimization", 54th AIAA Aerospace Sciences Meeting, San Diego, CA, January 2016.