Efficiency Enhancement of Aeroelastic Optimization Process Using Parametric Reduced-Order Modeling-Reference-Cited by-同舟云学术

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Efficiency Enhancement of Aeroelastic Optimization Process Using Parametric Reduced-Order Modeling

Published:2018-03 Issue:2 Volume:31 Page:04018004
ISSN:0893-1321
Container-title:Journal of Aerospace Engineering
language:en
Short-container-title:J. Aerosp. Eng.

Author:

Lee Saeil¹,Kim Taehyoun²,Srivastava Shashank³

Affiliation:

1. Research Fellow, Dept. of Mechanical Engineering, National Univ. of Singapore, 5 Engineering Dr. 2, Singapore 117579 (corresponding author).

2. Associate Professor, Dept. of Mechanical Engineering, National Univ. of Singapore, 9 Engineering Dr. 1, Singapore 117576.

3. Ph.D. Student, Dept. of Mechanical Engineering, National Univ. of Singapore, 3 Engineering Dr. 2, Singapore 117578.

Publisher

American Society of Civil Engineers (ASCE)

Subject

Mechanical Engineering,Aerospace Engineering,General Materials Science,Civil and Structural Engineering

Link

http://ascelibrary.org/doi/pdf/10.1061/%28ASCE%29AS.1943-5525.0000805

Reference36 articles.

1. Augmented Lagrangian Genetic Algorithm for Structural Optimization

2. Interpolation Method for Adapting Reduced-Order Models and Application to Aeroelasticity

3. On the use of the P–TFE method for panel flutter optimization

4. Numerical Analysis of Store-Induced Limit-Cycle Oscillation

5. Aeroelastic tailoring using fiber orientation and topology optimization

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1. Genetic-Algorithm-Guided Development of Parametric Aeroelastic Reduced-Order Models with State-Consistence Enforcement;AIAA Journal;2023-09

2. A sequential algorithm for decoupling the multidisciplinary constraints of hypersonic vehicle structural optimization design in a thermal environment;Structural and Multidisciplinary Optimization;2023-08

3. Genetic Algorithm-Guided Parametric Aeroelastic Reduced-Order Model with State-Consistence Enforcement;AIAA SCITECH 2023 Forum;2023-01-19

4. Hybridised differential evolution and equilibrium optimiser with learning parameters for mechanical and aircraft wing design;Knowledge-Based Systems;2022-03

5. Finding characteristically rich nonlinear solution space: a statistical mechanics approach;International Journal for Numerical Methods in Engineering;2020-05-30

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