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Towards Efficient Multi-disciplinary...

Babcock, Tucker.

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Towards Efficient Multi-disciplinary Optimization of Electric Aircraft Motors.

Record Type:	Electronic resources : Monograph/item
Title/Author:	Towards Efficient Multi-disciplinary Optimization of Electric Aircraft Motors./
Author:	Babcock, Tucker.
Published:	Ann Arbor : ProQuest Dissertations & Theses, : 2024,
Description:	121 p.
Notes:	Source: Dissertations Abstracts International, Volume: 85-12, Section: B.
Contained By:	Dissertations Abstracts International85-12B.
Subject:	Aerospace engineering. -
Online resource:	https://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=31142033
ISBN:	9798383059258

Towards Efficient Multi-disciplinary Optimization of Electric Aircraft Motors.
Babcock, Tucker.

Towards Efficient Multi-disciplinary Optimization of Electric Aircraft Motors. - Ann Arbor : ProQuest Dissertations & Theses, 2024 - 121 p.

Source: Dissertations Abstracts International, Volume: 85-12, Section: B.

Thesis (Ph.D.)--Rensselaer Polytechnic Institute, 2024.

The use of numerical optimization techniques is becoming an instrumental tool used in the design of new aircraft. As the aviation industry explores novel configurations of electrically powered aircraft, new multi-disciplinary analysis tools will be needed that are able to analyze and optimize candidate designs. In particular, the electric motors used in these new aircraft's propulsion systems require special attention: the complex interaction between the motor's electromagnetic and thermal behavior can have significant impacts on the motor's performance and operational safety. Simply put, if a motor gets too hot, its performance will degrade and it may even fail catastrophically. Further, additional system efficiency gains can be found if the motor is designed simultaneously with the rest of the aircraft.In this thesis, I introduce the first high-fidelity fully-coupled analytically-differentiated electro-thermal motor analysis and optimization framework. The framework efficiently computes derivatives through the multi-disciplinary forward analysis with the coupled adjoint method. These efficient derivative computations enable the use of scalable gradient-based optimization methods for electric motor problems, all while accurately capturing the coupled electro-thermal physics.Next, I use this electro-thermal motor optimization framework to explore the importance of modeling the electro-thermal physics with a fully coupled feedback model. I perform a multi-objective electric motor optimization study that seeks to minimize motor mass and maximize efficiency, constrained by motor power and maximum motor winding and magnet temperatures. In the study, I consider a feedback-coupled model as well as two feedforward-coupled models; each using a different reference temperature when computing the electromagnetic performance. The results of this optimization study show that feedback coupling is indeed important to model, particularly for cases when it is not obvious to a practitioner how they should trade motor mass and efficiency.{A0}Subsequently, I further demonstrate the merit of the coupled electro-thermal motor analysis framework by including it in a conceptual aircraft design study. This study combines the analytically differentiated electric motor model with models for an aircraft's inverter, gearbox, and propeller, and explores how the aircraft's system-level parameters (e.g. cruise velocity) impact its maximum range. This study illustrates how an engineer may use analytically differentiated analysis tools in a conceptual design framework, as it allows them to perform high-level trade studies of optimal designs.Finally, the last part of this thesis introduces a novel multi-fidelity optimization algorithm. While we as engineers often want to be able to use the highest fidelity analysis models available, such models are often cost-prohibitive to use early in the design process when several different concepts and configurations may be considered. To that end, I have developed a multi-fidelity optimization algorithm that uses the estimated error between the low- and high-fidelity analyses to efficiently globalize the optimization. I compare the algorithm to existing state-of-the-art methods on a series of benchmark problems where I show that it performs favorably.{A0}

ISBN: 9798383059258Subjects--Topical Terms:

1002622
Aerospace engineering.
Subjects--Index Terms:

Numerical optimization techniques

Towards Efficient Multi-disciplinary Optimization of Electric Aircraft Motors.
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260 1 $a Ann Arbor : $b ProQuest Dissertations & Theses, $c 2024
300 $a 121 p.
500 $a Source: Dissertations Abstracts International, Volume: 85-12, Section: B.
500 $a Advisor: Hicken, Jason E.
502 $a Thesis (Ph.D.)--Rensselaer Polytechnic Institute, 2024.
520 $a The use of numerical optimization techniques is becoming an instrumental tool used in the design of new aircraft. As the aviation industry explores novel configurations of electrically powered aircraft, new multi-disciplinary analysis tools will be needed that are able to analyze and optimize candidate designs. In particular, the electric motors used in these new aircraft's propulsion systems require special attention: the complex interaction between the motor's electromagnetic and thermal behavior can have significant impacts on the motor's performance and operational safety. Simply put, if a motor gets too hot, its performance will degrade and it may even fail catastrophically. Further, additional system efficiency gains can be found if the motor is designed simultaneously with the rest of the aircraft.In this thesis, I introduce the first high-fidelity fully-coupled analytically-differentiated electro-thermal motor analysis and optimization framework. The framework efficiently computes derivatives through the multi-disciplinary forward analysis with the coupled adjoint method. These efficient derivative computations enable the use of scalable gradient-based optimization methods for electric motor problems, all while accurately capturing the coupled electro-thermal physics.Next, I use this electro-thermal motor optimization framework to explore the importance of modeling the electro-thermal physics with a fully coupled feedback model. I perform a multi-objective electric motor optimization study that seeks to minimize motor mass and maximize efficiency, constrained by motor power and maximum motor winding and magnet temperatures. In the study, I consider a feedback-coupled model as well as two feedforward-coupled models; each using a different reference temperature when computing the electromagnetic performance. The results of this optimization study show that feedback coupling is indeed important to model, particularly for cases when it is not obvious to a practitioner how they should trade motor mass and efficiency.{A0}Subsequently, I further demonstrate the merit of the coupled electro-thermal motor analysis framework by including it in a conceptual aircraft design study. This study combines the analytically differentiated electric motor model with models for an aircraft's inverter, gearbox, and propeller, and explores how the aircraft's system-level parameters (e.g. cruise velocity) impact its maximum range. This study illustrates how an engineer may use analytically differentiated analysis tools in a conceptual design framework, as it allows them to perform high-level trade studies of optimal designs.Finally, the last part of this thesis introduces a novel multi-fidelity optimization algorithm. While we as engineers often want to be able to use the highest fidelity analysis models available, such models are often cost-prohibitive to use early in the design process when several different concepts and configurations may be considered. To that end, I have developed a multi-fidelity optimization algorithm that uses the estimated error between the low- and high-fidelity analyses to efficiently globalize the optimization. I compare the algorithm to existing state-of-the-art methods on a series of benchmark problems where I show that it performs favorably.{A0}
590 $a School code: 0185.
650 4 $a Aerospace engineering. $3 1002622
650 4 $a Computer science. $3 523869
650 4 $a Mathematics. $3 515831
650 4 $a Electromagnetics. $3 3173223
650 4 $a Mechanical engineering. $3 649730
653 $a Numerical optimization techniques
653 $a Aviation industry
653 $a Feedback coupling
653 $a Electro-thermal motor analysis
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710 2 $a Rensselaer Polytechnic Institute. $b Aeronautical Engineering. $3 2093993
773 0 $t Dissertations Abstracts International $g 85-12B.
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791 $a Ph.D.
792 $a 2024
793 $a English
856 4 0 $u https://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=31142033