{"id":{"repo_id":"mit","oai_identifier":"oai:dspace.mit.edu:1721.1/127070"},"canonical_url":"https://search.dev.ndltd.org/etd/mit/oai:dspace.mit.edu:1721.1/127070","repository":{"repo_id":"mit","name":"MIT","base_url":"https://dspace.mit.edu/oai/request"},"display":{"title":"Integrated vehicle and mission design using convex optimization","abstract":"Convex optimization is used to solve the simultaneous vehicle and mission design problem. The objective of this work is to develop convex optimization architectures that allow both the vehicle and mission to be designed together. They allow the problem to be solved very quickly while maintaining similar fidelity to comparable methods. Multiple architectures are formulated, and the architectures are implemented and evaluated for a sounding rocket design problem and a hydrogen aircraft design problem. The methodology proves successful in designing the sounding rocket while taking into account the optimal trajectory and control strategy and extended to a multi-mission design case. The hydrogen aircraft was successfully designed, allowing for both the cryogenic tank design to be chosen in conjunction with the mission prole. For the rocket design problem, the integrated vehicle and mission problem can only be combined into alternating and integrated approach, and the integrated architecture for convergence to solution in 50% computation time while reaching similar solution. For the hydrogen aircraft case, a 50+% decrease in fuel burn was able to be achieved compared to regular kerosene with an integrated optimization approach. Future work includes studying the convergence properties as well as increasing the robustness of the architectures.","abstract_html":"Convex optimization is used to solve the simultaneous vehicle and mission design problem. The objective of this work is to develop convex optimization architectures that allow both the vehicle and mission to be designed together. They allow the problem to be solved very quickly while maintaining similar fidelity to comparable methods. Multiple architectures are formulated, and the architectures are implemented and evaluated for a sounding rocket design problem and a hydrogen aircraft design problem. The methodology proves successful in designing the sounding rocket while taking into account the optimal trajectory and control strategy and extended to a multi-mission design case. The hydrogen aircraft was successfully designed, allowing for both the cryogenic tank design to be chosen in conjunction with the mission prole. For the rocket design problem, the integrated vehicle and mission problem can only be combined into alternating and integrated approach, and the integrated architecture for convergence to solution in 50% computation time while reaching similar solution. For the hydrogen aircraft case, a 50+% decrease in fuel burn was able to be achieved compared to regular kerosene with an integrated optimization approach. Future work includes studying the convergence properties as well as increasing the robustness of the architectures.","abstract_has_math":false,"creators":["Lin, Beldon Chi."],"institution":"Massachusetts Institute of Technology","degree_name":"Master","degree_level":null,"degree_discipline":null,"degree_department":"Massachusetts Institute of Technology. Department of Aeronautics and Astronautics","school":null,"contributors":[],"advisors":["Olivier de Weck and Michele Carpenter."],"committee_chairs":[],"committee_members":[],"year":2020,"date_issued":"2020","date_published":"2020","updated_at":"2026-07-22T22:21:21Z","subjects":["Aeronautics and Astronautics."],"languages":["eng"],"rights":["MIT theses may be protected by copyright. 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The objective of this work is to develop convex optimization architectures that allow both the vehicle and mission to be designed together. They allow the problem to be solved very quickly while maintaining similar fidelity to comparable methods. Multiple architectures are formulated, and the architectures are implemented and evaluated for a sounding rocket design problem and a hydrogen aircraft design problem. The methodology proves successful in designing the sounding rocket while taking into account the optimal trajectory and control strategy and extended to a multi-mission design case. The hydrogen aircraft was successfully designed, allowing for both the cryogenic tank design to be chosen in conjunction with the mission prole. For the rocket design problem, the integrated vehicle and mission problem can only be combined into alternating and integrated approach, and the integrated architecture for convergence to solution in 50% computation time while reaching similar solution. For the hydrogen aircraft case, a 50+% decrease in fuel burn was able to be achieved compared to regular kerosene with an integrated optimization approach. Future work includes studying the convergence properties as well as increasing the robustness of the architectures."]},{"key":"dc:description.degree","label":"Dc Description Degree","values":["S.M."]},{"key":"dc:title","label":"Title","values":["Integrated vehicle and mission design using convex optimization"]}]}],"canonical_facts":{"dc:contributor.advisor":["Olivier de Weck and Michele Carpenter."],"dc:contributor.department":["Massachusetts Institute of Technology. Department of Aeronautics and Astronautics","Aero"],"dc:contributor.other":["Massachusetts Institute of Technology. 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The methodology proves successful in designing the sounding rocket while taking into account the optimal trajectory and control strategy and extended to a multi-mission design case. The hydrogen aircraft was successfully designed, allowing for both the cryogenic tank design to be chosen in conjunction with the mission prole. For the rocket design problem, the integrated vehicle and mission problem can only be combined into alternating and integrated approach, and the integrated architecture for convergence to solution in 50% computation time while reaching similar solution. For the hydrogen aircraft case, a 50+% decrease in fuel burn was able to be achieved compared to regular kerosene with an integrated optimization approach. 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