2026-09-19T23:49:40Zhttps://dspace.mit.edu/server/oai/requestoai:dspace.mit.edu:1721.1/1400232022-02-08T03:35:23Zcom_1721.1_7582com_1721.1_7581col_1721.1_131023
Hansman, R. John
Sharpe, Peter
Massachusetts Institute of Technology. Department of Aeronautics and Astronautics
2022-02-07T15:19:33Z
2022-02-07T15:19:33Z
2021-09
2021-09-16T17:14:13.119Z
https://hdl.handle.net/1721.1/140023
https://orcid.org/0000-0001-9899-9171
This work presents a new computational framework for conceptual aircraft design called AeroSandbox. This framework leverages modern techniques for automatic differentiation developed in the optimal control and machine learning communities. By combining these efficient gradient calculations with robust optimizers such as IPOPT, multidisciplinary aircraft design problems of practical interest can be solved in seconds. We demonstrate this speed with several canonical aircraft design problems in this work, showing that performance and flexibility equals or exceeds that of state-of-the-art tools in many cases.
This framework's modular approach to engineering analysis allows sophisticated aerospace problems to be constructed by connecting plug-and-play building blocks in code. This decreases the time required to go from a qualitative vehicle and mission concept to a quantitative, optimized performance estimate. The framework's emphasis on rapid development time and run time enables an engineer to interactively pose design questions, enabling human insight to be more readily applied to the computational design process.
S.M.
Massachusetts Institute of Technology
In Copyright - Educational Use Permitted
Copyright retained by author(s)
https://rightsstatements.org/page/InC-EDU/1.0/
AeroSandbox: A Differentiable Framework for Aircraft Design Optimization
Thesis
application/pdf
Master
Master of Science in Aeronautics and Astronautics
Publication
unknown
unknown
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<Abstract>This work presents a new computational framework for conceptual aircraft design called AeroSandbox. This framework leverages modern techniques for automatic differentiation developed in the optimal control and machine learning communities. By combining these efficient gradient calculations with robust optimizers such as IPOPT, multidisciplinary aircraft design problems of practical interest can be solved in seconds. We demonstrate this speed with several canonical aircraft design problems in this work, showing that performance and flexibility equals or exceeds that of state-of-the-art tools in many cases.
This framework's modular approach to engineering analysis allows sophisticated aerospace problems to be constructed by connecting plug-and-play building blocks in code. This decreases the time required to go from a qualitative vehicle and mission concept to a quantitative, optimized performance estimate. The framework's emphasis on rapid development time and run time enables an engineer to interactively pose design questions, enabling human insight to be more readily applied to the computational design process.</Abstract>
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