RR
R. Reggie Johanes
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Path performance optimization has proven to be a powerful tool in solving a wide variety of optimal control problems in the aerospace field. However, the numerical complexity of such methodologies often prevents the possibility to optimize the performance of high-fidelity flight mechanics models characterized by coupled, non-linear, and/or high-order dynamic and aero-propulsive models. This research has explored the impact of reduced-order modeling on the optimal path performance obtainable with surrogates of the high-fidelity flight mechanics model. The developed methodology revolves around the creation of different reduced-order models that retain the characteristics of a full-order flight mechanics model to different degrees of fidelity, while being manageable by an optimal control solver. The methodology has been applied to obtain minimum-time landing trajectories for the UNIFIER19 C7A, a hybrid-electric aircraft featuring over-the-wing distributed propulsion, previously developed under the UNIFIER19 project. Results show that the reduced-order models can be used to generate flyable trajectories, as verified by tracking the resulting landing approach paths using the base high-fidelity model. On the other hand, the value of the objective function differs widely depending on the reduced-order model used, indicating that the modeling choice has a significant impact on the optimal performance prediction.
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Path performance optimization has proven to be a powerful tool in solving a wide variety of optimal control problems in the aerospace field. However, the numerical complexity of such methodologies often prevents the possibility to optimize the performance of high-fidelity flight mechanics models characterized by coupled, non-linear, and/or high-order dynamic and aero-propulsive models. This research has explored the impact of reduced-order modeling on the optimal path performance obtainable with surrogates of the high-fidelity flight mechanics model. The developed methodology revolves around the creation of different reduced-order models that retain the characteristics of a full-order flight mechanics model to different degrees of fidelity, while being manageable by an optimal control solver. The methodology has been applied to obtain minimum-time landing trajectories for the UNIFIER19 C7A, a hybrid-electric aircraft featuring over-the-wing distributed propulsion, previously developed under the UNIFIER19 project. Results show that the reduced-order models can be used to generate flyable trajectories, as verified by tracking the resulting landing approach paths using the base high-fidelity model. On the other hand, the value of the objective function differs widely depending on the reduced-order model used, indicating that the modeling choice has a significant impact on the optimal performance prediction.
Master thesis
(2024)
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R. Reggie Johanes, Carmine Varriale, F. Oliviero, Johannes Soikkeli, M.F.M. Hoogreef, E. van Kampen
Trajectory optimization has proven to be a powerful tool in solving a wide variety of optimal control problems in the aerospace field. However, in many cases, numerical complexities prevent the analysis of optimal trajectories for high-fidelity models, particularly due to the inherent difficulty of transcribing high-order dynamic systems. This research project proposes a methodology incorporating reduced-order modeling that retains the most critical dynamic characteristics from a full-order model while allowing the resulting simplification to be manageable for a trajectory optimization solver. The study applies this methodology to evaluate optimal landing trajectories for the UNIFIER19 C7A, a hybrid-electric aircraft equipped with a distributed electric propulsion system that was previously developed under the UNIFIER19 project. Results show that the reduced-order models generated for the aircraft can be used to generate flyable trajectories, verified by tracking the resulting landing approach paths using the base high-fidelity model. It is envisioned that this methodology will also be applicable to other aircraft models and mission phases.
...
Trajectory optimization has proven to be a powerful tool in solving a wide variety of optimal control problems in the aerospace field. However, in many cases, numerical complexities prevent the analysis of optimal trajectories for high-fidelity models, particularly due to the inherent difficulty of transcribing high-order dynamic systems. This research project proposes a methodology incorporating reduced-order modeling that retains the most critical dynamic characteristics from a full-order model while allowing the resulting simplification to be manageable for a trajectory optimization solver. The study applies this methodology to evaluate optimal landing trajectories for the UNIFIER19 C7A, a hybrid-electric aircraft equipped with a distributed electric propulsion system that was previously developed under the UNIFIER19 project. Results show that the reduced-order models generated for the aircraft can be used to generate flyable trajectories, verified by tracking the resulting landing approach paths using the base high-fidelity model. It is envisioned that this methodology will also be applicable to other aircraft models and mission phases.