A lightweight Python tool for aerostructural optimization of aircraft wings using OpenMDAO.
OpenAeroStruct is a Python-based tool for performing coupled aerostructural optimization of aircraft wings. It combines aerodynamic analysis using a vortex-lattice method with structural analysis using a spatial beam model, wrapped with NASA's OpenMDAO framework for gradient-based optimization. The tool enables engineers to efficiently design wing structures that balance aerodynamic performance with structural integrity.
Aerospace engineers, researchers, and students working on aircraft wing design and multidisciplinary optimization who need an accessible, open-source tool for aerostructural analysis.
OpenAeroStruct provides a lightweight, open-source alternative to expensive commercial aerostructural optimization software, making advanced multidisciplinary design optimization accessible for research and education while maintaining sufficient fidelity for meaningful design studies.
OpenAeroStruct is a lightweight tool that performs aerostructural optimization using OpenMDAO.
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Leverages NASA's established OpenMDAO framework for gradient-based optimization, providing a robust and efficient foundation for multidisciplinary design as highlighted in the documentation and journal article.
Supports both tubular and wingbox configurations, offering flexibility for different wing design studies, with visual examples provided in the README figures.
Includes fuel-weight, structural-weight, point-mass, and thrust loads, enabling realistic aerostructural analysis for aircraft wings as detailed in the cited conference papers.
Provides plotting capabilities to visualize analysis and optimization results directly, simplifying post-processing without external software, as shown in the README example images.
Relies on vortex-lattice method and spatial beam models, which are simplified and may lack accuracy for complex aerodynamic phenomena or detailed structural behaviors, limiting use in high-precision engineering.
The README explicitly states it's provided 'as is without any guaranteed support,' meaning users may face challenges with bugs or setup without dedicated developer assistance.
Requires familiarity with OpenMDAO's framework, which can have a steep learning curve for users new to multidisciplinary optimization, increasing initial setup and customization effort.