Efficient Aerodynamic and Aeroacustic Optimization of Propeller Sections Using Bayesian Methods

Andreas Kümmel; Christian Breitsamter

doi:10.1007/978-3-030-79561-0_35

Efficient Aerodynamic and Aeroacustic Optimization of Propeller Sections Using Bayesian Methods

Andreas Kümmel, Christian Breitsamter

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Technical University of Munich

Research output: Chapter in Book/Report/Conference proceeding › Chapter › peer-review

Abstract

Due to various design variables, like the blade shape and operating conditions, the optimization of propellers is a complex task. The aerodynamic and aeroacoustic design optimization is conducted by using an efficient machine learning optimization approach, the Bayesian optimization. On the one hand, this allows for a fast and efficient optimization process. On the other hand, the knowledge about the optimization problem is extend and the influence of the individual design variables can be identified. Exemplarily, the radial airfoil sections of the propeller blade are optimized regarding the glide ratio and the aeroacoustic footprint.

Original language	English
Title of host publication	Notes on Numerical Fluid Mechanics and Multidisciplinary Design
Publisher	Springer Science and Business Media Deutschland GmbH
Pages	365-375
Number of pages	11
DOIs	https://doi.org/10.1007/978-3-030-79561-0_35
State	Published - 2021

Publication series

Name	Notes on Numerical Fluid Mechanics and Multidisciplinary Design
Volume	151
ISSN (Print)	1612-2909
ISSN (Electronic)	1860-0824

Keywords

Aeroacoustics
Bayesian optimization
Propeller design

Access to Document

10.1007/978-3-030-79561-0_35

Cite this

Kümmel, A., & Breitsamter, C. (2021). Efficient Aerodynamic and Aeroacustic Optimization of Propeller Sections Using Bayesian Methods. In Notes on Numerical Fluid Mechanics and Multidisciplinary Design (pp. 365-375). (Notes on Numerical Fluid Mechanics and Multidisciplinary Design; Vol. 151). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-3-030-79561-0_35

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title = "Efficient Aerodynamic and Aeroacustic Optimization of Propeller Sections Using Bayesian Methods",

abstract = "Due to various design variables, like the blade shape and operating conditions, the optimization of propellers is a complex task. The aerodynamic and aeroacoustic design optimization is conducted by using an efficient machine learning optimization approach, the Bayesian optimization. On the one hand, this allows for a fast and efficient optimization process. On the other hand, the knowledge about the optimization problem is extend and the influence of the individual design variables can be identified. Exemplarily, the radial airfoil sections of the propeller blade are optimized regarding the glide ratio and the aeroacoustic footprint.",

keywords = "Aeroacoustics, Bayesian optimization, Propeller design",

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Kümmel, A & Breitsamter, C 2021, Efficient Aerodynamic and Aeroacustic Optimization of Propeller Sections Using Bayesian Methods. in Notes on Numerical Fluid Mechanics and Multidisciplinary Design. Notes on Numerical Fluid Mechanics and Multidisciplinary Design, vol. 151, Springer Science and Business Media Deutschland GmbH, pp. 365-375. https://doi.org/10.1007/978-3-030-79561-0_35

Efficient Aerodynamic and Aeroacustic Optimization of Propeller Sections Using Bayesian Methods. / Kümmel, Andreas; Breitsamter, Christian.
Notes on Numerical Fluid Mechanics and Multidisciplinary Design. Springer Science and Business Media Deutschland GmbH, 2021. p. 365-375 (Notes on Numerical Fluid Mechanics and Multidisciplinary Design; Vol. 151).

Research output: Chapter in Book/Report/Conference proceeding › Chapter › peer-review

TY - CHAP

T1 - Efficient Aerodynamic and Aeroacustic Optimization of Propeller Sections Using Bayesian Methods

AU - Kümmel, Andreas

AU - Breitsamter, Christian

PY - 2021

Y1 - 2021

N2 - Due to various design variables, like the blade shape and operating conditions, the optimization of propellers is a complex task. The aerodynamic and aeroacoustic design optimization is conducted by using an efficient machine learning optimization approach, the Bayesian optimization. On the one hand, this allows for a fast and efficient optimization process. On the other hand, the knowledge about the optimization problem is extend and the influence of the individual design variables can be identified. Exemplarily, the radial airfoil sections of the propeller blade are optimized regarding the glide ratio and the aeroacoustic footprint.

AB - Due to various design variables, like the blade shape and operating conditions, the optimization of propellers is a complex task. The aerodynamic and aeroacoustic design optimization is conducted by using an efficient machine learning optimization approach, the Bayesian optimization. On the one hand, this allows for a fast and efficient optimization process. On the other hand, the knowledge about the optimization problem is extend and the influence of the individual design variables can be identified. Exemplarily, the radial airfoil sections of the propeller blade are optimized regarding the glide ratio and the aeroacoustic footprint.

KW - Aeroacoustics

KW - Bayesian optimization

KW - Propeller design

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T3 - Notes on Numerical Fluid Mechanics and Multidisciplinary Design

SP - 365

EP - 375

BT - Notes on Numerical Fluid Mechanics and Multidisciplinary Design

PB - Springer Science and Business Media Deutschland GmbH

ER -

Efficient Aerodynamic and Aeroacustic Optimization of Propeller Sections Using Bayesian Methods

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Keywords

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