Article

Three dimensional evolutionary aerodynamic design optimization with CMA-ES

Authors:

Martina Hasenjäger,

Bernhard Sendhoff,

Toyotaka Sonoda,

Toshiyuki ArimaAuthors Info & Claims

GECCO '05: Proceedings of the 7th annual conference on Genetic and evolutionary computation

Pages 2173 - 2180

https://doi.org/10.1145/1068009.1068366

Published: 25 June 2005 Publication History

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Abstract

In this paper, we present the application of evolutionary optimization methods to a demanding, industrially relevant engineering domain, the three-dimensional optimization of gas turbine stator blades. This optimization problem is high-dimensional search and computationally very expensive. We show that, despite of its difficulty, the problem is feasible. Our approach not only successfully optimizes the aerodynamic design but also yields interesting results from an engineering point of view.

References

[1]

T. Arima, T. Sonoda, M. Shirotori, A. Tamura, and K. Kikuchi. A numerical investigation of transonic axial compressor rotor flow using a low-Reynolds-number k - ε turbulence model. ASME Journal of Turbomachinery, 121:44--58, 1999.

Crossref

Google Scholar

[2]

T. Bäck, D. B. Fogel, and T. Michalewicz, editors. Evolutionary Computation 1: Basic Algorithms and Operators. Institute of Physics, 2000.

Crossref

Google Scholar

[3]

C. A. Coello Coello, D. A. Van Veldhuizen, and G. B. Lamont. Evolutionary Algorithms for Solving Multi-Objective Problems. Kluwer, 2002.

Digital Library

Google Scholar

[4]

K. Deb. Multi-Objective Optimization Using Evolutionary Algorithms. Wiley, 2002.

Digital Library

Google Scholar

[5]

G. Farin. Curves and Surfaces for Computer-Aided Geometric Design. Academic Press, San Diego, 4 edition, 1997.

Digital Library

Google Scholar

[6]

A. Geist, A. Beguelin, J. Dongarra, W. Jiang, R. Mancheck, and V. Sunderam. PVM: Parallel Virtual Machine: A Users' Guide and Tutorial for Network Parallel Computing. MIT Press, 1994.

Digital Library

Google Scholar

[7]

N. Hansen and A. Ostermeier. Adapting arbitrary normal mutation distributions in evolution strategies: The covariance matrix adaptation. In Proceedings of the 1996 IEEE International Conference on Evolutionary Computation, pages 312--317, 1996.

Crossref

Google Scholar

[8]

N. Hansen and A. Ostermeier. Completely derandomized self-adaptation in evolution strategies. Evolutionary Computation, 9(2):159--195, 2001.

Digital Library

Google Scholar

[9]

N. Kuno and T. Sonoda. Flow characteristics in a transonic ultra-low-aspect-ratio axial turbine vane. Journal of Propulsion and Power, 20(4):596--603, 2004.

Crossref

Google Scholar

[10]

MPI: A message-passing interface standard, http://www-unix.mcs.anl.gov/mpi/.

Google Scholar

[11]

M. Olhofer, Y. Jin, and B. Sendhoff. Adaptive encoding for aerodynamic shape optimization using evolution strategies. In Congress on Evolutionary Computation (CEC), pages 576--583, Seoul, Korea, 2001. IEEE Press.

Crossref

Google Scholar

[12]

A. Osyczka. Evolutionary Algorithms For Single And Multicriteria Design Optmization. Physica-Verlag, 2002.

Google Scholar

[13]

M. L. Shelton, B. A. Gregory, S. H. Lamson, H. L. Moses, R. L. Doughty, and T. Kiss. Optimization of a transonic turbine airfoil using artificial intelligence, CFD and cascade testing. ASME Paper 93-GT-161, 1993.

Google Scholar

[14]

D. C. Wilcox. Reassessment of the scale-determining equation for advanced turbulence models. AIAA Journal, 26:1299--1310, 1988.

Crossref

Google Scholar

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Ishikawa TNishida HTagawa Y(2023)Design Exploration of Initial Bubbles for Higher-Speed Laser-Induced MicrojetsIEEE Access10.1109/ACCESS.2023.323872211(8268-8274)Online publication date: 2023
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Zou MZhu XTian YWang JChen H(2023)PRETTYInformation Sciences: an International Journal10.1016/j.ins.2022.12.020623:C(748-766)Online publication date: 1-Apr-2023
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Juangphanich PDe Maesschalck CPaniagua G(2019)Turbine Passage Design Methodology to Minimize Entropy Production—A Two-Step Optimization StrategyEntropy10.3390/e2106060421:6(604)Online publication date: 18-Jun-2019
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Index Terms

Three dimensional evolutionary aerodynamic design optimization with CMA-ES
1. Applied computing
  1. Physical sciences and engineering
    1. Aerospace
2. Computing methodologies
  1. Artificial intelligence
    1. Search methodologies
      1. Heuristic function construction

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Published In

GECCO '05: Proceedings of the 7th annual conference on Genetic and evolutionary computation

June 2005

2272 pages

ISBN:1595930108

DOI:10.1145/1068009

Editor:
Hans-Georg Beyer
Vorarlberg University of Applied Sciences, Austria
,
General Chair:
Una-May O'Reilly
CSAIL, MIT

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 25 June 2005

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GECCO05

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GECCO05: Genetic and Evolutionary Computation Conference

June 25 - 29, 2005

Washington DC, USA

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Overall Acceptance Rate 1,669 of 4,410 submissions, 38%

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Ishikawa TNishida HTagawa Y(2023)Design Exploration of Initial Bubbles for Higher-Speed Laser-Induced MicrojetsIEEE Access10.1109/ACCESS.2023.323872211(8268-8274)Online publication date: 2023
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Zou MZhu XTian YWang JChen H(2023)PRETTYInformation Sciences: an International Journal10.1016/j.ins.2022.12.020623:C(748-766)Online publication date: 1-Apr-2023
https://dl.acm.org/doi/10.1016/j.ins.2022.12.020
Juangphanich PDe Maesschalck CPaniagua G(2019)Turbine Passage Design Methodology to Minimize Entropy Production—A Two-Step Optimization StrategyEntropy10.3390/e2106060421:6(604)Online publication date: 18-Jun-2019
https://doi.org/10.3390/e21060604
Beşkirli M(2019)Yüksek Boyutlu Test Fonksiyonlarında Ağaç Tohum Algoritmasının Performans AnaliziEuropean Journal of Science and Technology10.31590/ejosat.636416(93-101)Online publication date: 31-Oct-2019
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Hagg A(2017)Hierarchical surrogate modeling for illumination algorithmsProceedings of the Genetic and Evolutionary Computation Conference Companion10.1145/3067695.3082495(1407-1410)Online publication date: 15-Jul-2017
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Omidvar MLi XTang K(2015)Designing benchmark problems for large-scale continuous optimizationInformation Sciences: an International Journal10.1016/j.ins.2014.12.062316:C(419-436)Online publication date: 20-Sep-2015
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Gülcü ŞKodaz H(2015)A novel parallel multi-swarm algorithm based on comprehensive learning particle swarm optimizationEngineering Applications of Artificial Intelligence10.1016/j.engappai.2015.06.01345:C(33-45)Online publication date: 1-Oct-2015
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Preen RBull L(2014)Towards the evolution of vertical-axis wind turbines using supershapesEvolutionary Intelligence10.1007/s12065-014-0116-47:3(155-167)Online publication date: 28-Oct-2014
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Pedersen GButz MPelikan MBranke J(2010)Evolving robust controller parameters using covariance matrix adaptationProceedings of the 12th annual conference on Genetic and evolutionary computation10.1145/1830483.1830708(1251-1258)Online publication date: 7-Jul-2010
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