Framework for the Multi-Objective Design Optimization of Aerocapture Missions
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| 发表在: | Aerospace vol. 12, no. 5 (2025), p. 387 |
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MDPI AG
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| 在线阅读: | Citation/Abstract Full Text + Graphics Full Text - PDF |
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| 024 | 7 | |a 10.3390/aerospace12050387 |2 doi | |
| 035 | |a 3211845592 | ||
| 045 | 2 | |b d20250101 |b d20251231 | |
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| 100 | 1 | |a Urraza Atue Segundo | |
| 245 | 1 | |a Framework for the Multi-Objective Design Optimization of Aerocapture Missions | |
| 260 | |b MDPI AG |c 2025 | ||
| 513 | |a Journal Article | ||
| 520 | 3 | |a Developing spacecraft for efficient aerocapture missions demands managing extreme aerothermal environments, precise controls, and atmospheric uncertainties. Successful designs must integrate vehicle airframe considerations with trajectory planning, adhering to launcher dimension constraints and ensuring robustness against atmospheric and insertion uncertainties. To advance robust multi-objective optimization in this field, a new framework is presented, designed to rapidly analyze and optimize non-thrusting, fixed angle-of-attack aerocapture-capable spacecraft and their trajectories. The framework employs a three-degree-of-freedom atmospheric flight dynamics model incorporating planet-specific characteristics. Aerothermal effects are approximated using established Sutton–Graves, Tauber–Sutton, and Stefan–Boltzmann relations. The framework computes the resulting post-atmospheric pass orbit using an orbital element determination algorithm to estimate fuel requirements for orbital corrective maneuvers. A novel algorithm that consolidates multiple objective functions into a unified cost function is presented and demonstrated to achieve superior optima with computational efficiency compared to traditional multi-objective optimization approaches. Numerical examples demonstrate the methodology’s effectiveness and computational cost at optimizing terrestrial and Martian aerocapture maneuvers for minimum fuel, heat loads, peak heat transfers, and an overall optimal trajectory, including volumetric considerations. | |
| 653 | |a Fuels | ||
| 653 | |a Design optimization | ||
| 653 | |a Atmospheric flight | ||
| 653 | |a Trajectory optimization | ||
| 653 | |a Success | ||
| 653 | |a Orbital maneuvers | ||
| 653 | |a Cost function | ||
| 653 | |a Decision making | ||
| 653 | |a Computational efficiency | ||
| 653 | |a Missions | ||
| 653 | |a Computing costs | ||
| 653 | |a Algorithms | ||
| 653 | |a Multiple objective analysis | ||
| 653 | |a Spacecraft | ||
| 653 | |a Airframes | ||
| 653 | |a Aerocapture | ||
| 653 | |a Trajectory planning | ||
| 653 | |a Uncertainty | ||
| 700 | 1 | |a Bruce, Paul | |
| 773 | 0 | |t Aerospace |g vol. 12, no. 5 (2025), p. 387 | |
| 786 | 0 | |d ProQuest |t Advanced Technologies & Aerospace Database | |
| 856 | 4 | 1 | |3 Citation/Abstract |u https://www.proquest.com/docview/3211845592/abstract/embedded/J7RWLIQ9I3C9JK51?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text + Graphics |u https://www.proquest.com/docview/3211845592/fulltextwithgraphics/embedded/J7RWLIQ9I3C9JK51?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text - PDF |u https://www.proquest.com/docview/3211845592/fulltextPDF/embedded/J7RWLIQ9I3C9JK51?source=fedsrch |