Concurrent Trajectory Optimization and Aircraft Design for the Air Cargo Challenge Competition
A coupled aerostructural aircraft design and trajectory optimization framework is developed for the Air Cargo Challenge competition to maximize the expected score based on cargo carried, altitude achieved and distance traveled. Its modular architecture makes it easily adaptable to any problem where...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-07-01
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| Series: | Aerospace |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2226-4310/9/7/378 |
| _version_ | 1797441521960615936 |
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| author | Nuno M. B. Matos Andre C. Marta |
| author_facet | Nuno M. B. Matos Andre C. Marta |
| author_sort | Nuno M. B. Matos |
| collection | DOAJ |
| description | A coupled aerostructural aircraft design and trajectory optimization framework is developed for the Air Cargo Challenge competition to maximize the expected score based on cargo carried, altitude achieved and distance traveled. Its modular architecture makes it easily adaptable to any problem where the performance depends not only on the design of the aircraft but also on its flight trajectory. It is based on OpenAeroStruct, an aerostructural solver that uses analytic derivatives for efficient gradient-based optimization. A trajectory optimization module using a collocation method is coupled with the option of using b-splines to increase computational efficiency together with an experimentally-based power decay model that accurately determines the aircraft propulsive response to control input depending on the battery discharge level. The optimization problem totaled 206 variables and 283 constraints and was solved in less than 7 h on a standard computer with 12% reduction when using b-splines for trajectory control variables. The results revealed the need to consider the multi-objective total score to account for the different score components and highlighted the importance of the payload level and chosen trajectory. The wing area should be increased within allowable limits to maximize payload capacity, climb to maximum target height should be the focus of the first 60 s of flight and full throttle should be avoided in cruise to reduce losses and extend flight distance. The framework proved to be a valuable tool for students to easily obtain guidelines for both the model aircraft design and control to maximize the competition score. |
| first_indexed | 2024-03-09T12:25:20Z |
| format | Article |
| id | doaj.art-f39942db35aa45b391bb823c2f33568f |
| institution | Directory Open Access Journal |
| issn | 2226-4310 |
| language | English |
| last_indexed | 2024-03-09T12:25:20Z |
| publishDate | 2022-07-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Aerospace |
| spelling | doaj.art-f39942db35aa45b391bb823c2f33568f2023-11-30T22:36:29ZengMDPI AGAerospace2226-43102022-07-019737810.3390/aerospace9070378Concurrent Trajectory Optimization and Aircraft Design for the Air Cargo Challenge CompetitionNuno M. B. Matos0Andre C. Marta1IDMEC, Instituto Superior Técnico, Universidade de Lisboa, 1049-001 Lisboa, PortugalIDMEC, Instituto Superior Técnico, Universidade de Lisboa, 1049-001 Lisboa, PortugalA coupled aerostructural aircraft design and trajectory optimization framework is developed for the Air Cargo Challenge competition to maximize the expected score based on cargo carried, altitude achieved and distance traveled. Its modular architecture makes it easily adaptable to any problem where the performance depends not only on the design of the aircraft but also on its flight trajectory. It is based on OpenAeroStruct, an aerostructural solver that uses analytic derivatives for efficient gradient-based optimization. A trajectory optimization module using a collocation method is coupled with the option of using b-splines to increase computational efficiency together with an experimentally-based power decay model that accurately determines the aircraft propulsive response to control input depending on the battery discharge level. The optimization problem totaled 206 variables and 283 constraints and was solved in less than 7 h on a standard computer with 12% reduction when using b-splines for trajectory control variables. The results revealed the need to consider the multi-objective total score to account for the different score components and highlighted the importance of the payload level and chosen trajectory. The wing area should be increased within allowable limits to maximize payload capacity, climb to maximum target height should be the focus of the first 60 s of flight and full throttle should be avoided in cruise to reduce losses and extend flight distance. The framework proved to be a valuable tool for students to easily obtain guidelines for both the model aircraft design and control to maximize the competition score.https://www.mdpi.com/2226-4310/9/7/378multidisciplinary design optimizationmulti-objective optimizationaerostructural designbattery discharge modelcollocation methodoptimal control |
| spellingShingle | Nuno M. B. Matos Andre C. Marta Concurrent Trajectory Optimization and Aircraft Design for the Air Cargo Challenge Competition Aerospace multidisciplinary design optimization multi-objective optimization aerostructural design battery discharge model collocation method optimal control |
| title | Concurrent Trajectory Optimization and Aircraft Design for the Air Cargo Challenge Competition |
| title_full | Concurrent Trajectory Optimization and Aircraft Design for the Air Cargo Challenge Competition |
| title_fullStr | Concurrent Trajectory Optimization and Aircraft Design for the Air Cargo Challenge Competition |
| title_full_unstemmed | Concurrent Trajectory Optimization and Aircraft Design for the Air Cargo Challenge Competition |
| title_short | Concurrent Trajectory Optimization and Aircraft Design for the Air Cargo Challenge Competition |
| title_sort | concurrent trajectory optimization and aircraft design for the air cargo challenge competition |
| topic | multidisciplinary design optimization multi-objective optimization aerostructural design battery discharge model collocation method optimal control |
| url | https://www.mdpi.com/2226-4310/9/7/378 |
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