A review on design of experiments and surrogate models in aircraft real-time and many-query aerodynamic analyses

Yondo, Raul; Andrés, Esther; Valero, Eusebio

doi:10.1016/j.paerosci.2017.11.003

Cited by 311Advanced Multi-Objective Optimization AlgorithmsProbabilistic and Robust Engineering DesignComputational Fluid Dynamics and Aerodynamics

A review on design of experiments and surrogate models in aircraft real-time and many-query aerodynamic analyses

Yondo, Raul, Andrés, Esther, Valero, Eusebio·2018·Progress in Aerospace Sciences

DOI 10.1016/j.paerosci.2017.11.003

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Aerodynamic efficiency improvements need turbulence modeling that relies heavily on ML-based surrogate models.
“Aerodynamic efficiency improvements need turbulence modeling that is often computationally intensive and relies heavily on ML-based surrogate models [865].”
From Tackling climate change with machine learning (2023) · cites this paper as [865]

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Flagged first, then by citations

9 of the 548 references couldn’t be matched to a known paper.

Sorted by citations

Machine learning in aerodynamic shape optimization
Li, J., Du, X., & Martins, J. R. R. A. (2022) · Progress in Aerospace Sciences · doi:10.1016/j.paerosci.2022.100849 · Cited by 376 · see citing works
“As shown in Table 2, commonly-used surrogate models include the polynomial response surface method, kriging, and neural networks [343, 344].”
Method
Surrogate modelling for sustainable building design – A review
Westermann, P., & Evins, R. (2019) · Energy and Buildings · doi:10.1016/j.enbuild.2019.05.057 · Cited by 345 · see citing works
Overview of Surrogate Modeling in Chemical Process Engineering
McBride, K., & Sundmacher, K. (2019) · Chemie Ingenieur Technik · doi:10.1002/cite.201800091 · Cited by 320 · see citing works
Data-driven modeling for unsteady aerodynamics and aeroelasticity
Kou, J., & Zhang, W. (2021) · Progress in Aerospace Sciences · doi:10.1016/j.paerosci.2021.100725 · Cited by 249 · see citing works
Strategies towards a more sustainable aviation: A systematic review
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Tackling climate change with machine learning
Rolnick, D., Donti, P. L., Kaack, L. H., Kochanski, K., Lacoste, A., Sankaran, K., Ross, A. S., Milojevic-Dupont, N., Jaques, N., Waldman-Brown, A., et al. (2023) · ACM Computing Surveys · doi:10.1145/3485128 · Cited by 336 · see citing works
Efficient Aerodynamic Shape Optimization with Deep-Learning-Based Geometric Filtering
Li, J., Zhang, M., Martins, J. R. R. A., & Shu, C. (2020) · AIAA Journal · doi:10.2514/1.j059254 · Cited by 172 · see citing works
Surrogate-assisted global sensitivity analysis: an overview
Cheng, K., Lü, Z., Ling, C., & Zhou, S. (2020) · Structural and Multidisciplinary Optimization · doi:10.1007/s00158-019-02413-5 · Cited by 165 · see citing works
A radial basis function surrogate model assisted evolutionary algorithm for high-dimensional expensive optimization problems
Chen, G., Zhang, K., Xue, X., Zhang, L., Yao, C., Wang, J., & Yao, J. P. (2021) · Applied Soft Computing · doi:10.1016/j.asoc.2021.108353 · Cited by 143 · see citing works
Recent Advances in Surrogate Modeling Methods for Uncertainty Quantification and Propagation
Wang, C., Qiang, X., Xu, M., & Wu, T. (2022) · Symmetry · doi:10.3390/sym14061219 · Cited by 143 · see citing works
Stochastic Model Updating with Uncertainty Quantification: An Overview and Tutorial
Bi, S., Beer, M., Cogan, S., & Mottershead, J. E. (2023) · Mechanical Systems and Signal Processing · doi:10.1016/j.ymssp.2023.110784 · Cited by 143 · see citing works
A review on the recent developments in thermal management systems for hybrid-electric aircraft
Coutinho, M., Bento, D., Souza, A., Cruz, R., Afonso, F., Lau, F. J. P., Suleman, A., Barbosa, F. R., Gandolfi, R., Affonso, W., et al. (2023) · Applied Thermal Engineering · doi:10.1016/j.applthermaleng.2023.120427 · Cited by 128 · see citing works
Non-intrusive reduced-order modeling for fluid problems: A brief review
Yu, J. Z., Yan, C. Y., & Guo, M. (2019) · Proceedings of the Institution of Mechanical Engineers Part G Journal of Aerospace Engineering · doi:10.1177/0954410019890721 · Cited by 119 · see citing works
“For more a priori sampling approaches, the readers are referred to the review work by Yondo et al.70”
Method
Review of machine learning-based surrogate models of groundwater contaminant modeling
Luo, J., Ma, X., Ji, Y., Li, X., Song, Z. F., & Lu, W. (2023) · Environmental Research · doi:10.1016/j.envres.2023.117268 · Cited by 104 · see citing works
A comprehensive review of Design of experiment (DOE) for water and wastewater treatment application - Key concepts, methodology and contextualized application
Lee, B. C. Y., Mahtab, M. S., Neo, T. H., Farooqi, I. H., & Khursheed, A. (2022) · Journal of Water Process Engineering · doi:10.1016/j.jwpe.2022.102673 · Cited by 99 · see citing works
Uncertainty analysis of impact of geometric variations on turbine blade performance
Wang, X., & Zou, Z. (2019) · Energy · doi:10.1016/j.energy.2019.03.140 · Cited by 90 · see citing works
Multifidelity Genetic Transfer: An Efficient Framework for Production Optimization
Yin, F., Xue, X., Zhang, C., Zhang, K., Han, J., Liu, B., Wang, J., & Yao, J. P. (2021) · SPE Journal · doi:10.2118/205013-pa · Cited by 89 · see citing works
Data-based approach for wing shape design optimization
Li, J., & Zhang, M. (2021) · Aerospace Science and Technology · doi:10.1016/j.ast.2021.106639 · Cited by 83 · see citing works
Response surface methodology for process optimization in livestock wastewater treatment: A review
Reza, A., Chen, L., & Mao, X. (2024) · Heliyon · doi:10.1016/j.heliyon.2024.e30326 · Cited by 81 · see citing works
“…RSM application for optimization have focused on fields like analytical chemistry [43], natural product research [44], engineering [45], aerodynamic analyses [46], food industry [47], product design [19], biofuel production [48], metabolomics-related studies [49], and pharmaceutical industry [50].”
Background
Surrogate-based optimization for mixed-integer nonlinear problems
Kim, S. H., & Boukouvala, F. (2020) · Computers & Chemical Engineering · doi:10.1016/j.compchemeng.2020.106847 · Cited by 78 · see citing works
Deep Learning for Multifidelity Aerodynamic Distribution Modeling from Experimental and Simulation Data
Li, K., Kou, J., & Zhang, W. (2022) · AIAA Journal · doi:10.2514/1.j061330 · Cited by 75 · see citing works
“Currently, these data-driven models have been widely used in flow reconstruction [20, 21], flight dynamics [22], aeroelasticity [23], flow control [24, 25], and optimization design [26].”
Method
The artificial intelligence reformation of sustainable building design approach: A systematic review on building design optimization methods using surrogate models
Elwy, I., & Hagishima, A. (2024) · Energy and Buildings · doi:10.1016/j.enbuild.2024.114769 · Cited by 76 · see citing works
Surrogate modeling: tricks that endured the test of time and some recent developments
Viana, F. A. C., Gogu, C., & Goel, T. (2021) · Structural and Multidisciplinary Optimization · doi:10.1007/s00158-021-03001-2 · Cited by 72 · see citing works
“Yondo et al. [224] has presented an excellent overview of different DOE techniques.”
Background
An Empirical Survey on Explainable AI Technologies: Recent Trends, Use-Cases, and Categories from Technical and Application Perspectives
Nagahisarchoghaei, M., Nur, N., Cummins, L., Nur, N., Karimi, M. M., Nandanwar, S., Bhattacharyya, S., & Rahimi, S. (2023) · Electronics · doi:10.3390/electronics12051092 · Cited by 76 · see citing works
A Review of Proxy Modeling Highlighting Applications for Reservoir Engineering
Bahrami, P., Moghaddam, F. S., & James, L. A. (2022) · Energies · doi:10.3390/en15145247 · Cited by 68 · see citing works
“ROMs can be grouped [15] based on the type of system (linear, partially linear or non-linear, parametric or non-parametric, and time-dependent or time-independent).”
Background
Log-Sum-Exp Neural Networks and Posynomial Models for Convex and Log-Log-Convex Data
Calafiore, G. C., Gaubert, S., & Possieri, C. (2019) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2019.2910417 · Cited by 64 · see citing works
Surrogate modelling for an aircraft dynamic landing loads simulation using an LSTM AutoEncoder-based dimensionality reduction approach
Lazzara, M., Chevalier, M., Colombo, M., Garcia, J. G., Lapeyre, C., & Teste, O. (2022) · Aerospace Science and Technology · doi:10.1016/j.ast.2022.107629 · Cited by 57 · see citing works
Surrogate-Model-Based Design and Optimization
Jiang, P., Zhou, Q., & Shao, X. (2019) · Springer tracts in mechanical engineering · doi:10.1007/978-981-15-0731-1_7 · Cited by 55 · see citing works
A numerical study on choosing the best configuration of the blade for vertical axis wind turbines
Zhang, T.-T., Wang, Z., Huang, W., Ingham, D. B., Ma, L., & Pourkashanian, M. M. (2020) · Journal of Wind Engineering and Industrial Aerodynamics · doi:10.1016/j.jweia.2020.104162 · Cited by 53 · see citing works
Aeroacoustic airfoil shape optimization enhanced by autoencoders
Kou, J., Botero-Bolívar, L., Ballano, R., Mariño, O. A., de Santana, L. D., Valero, E., & Ferrer, E. (2023) · Expert Systems with Applications · doi:10.1016/j.eswa.2023.119513 · Cited by 52 · see citing works
Self-citation
Big data and smart aviation information management system
Dou, X. (2020) · Cogent Business & Management · doi:10.1080/23311975.2020.1766736 · Cited by 50 · see citing works
“Its role is to facilitate the intelligent and automated management of crew and cabin passenger service to reduce management costs and operational errors and improve management performance (Gupta et al., 2018; Yondo et al., 2018).”
Background
Surrogate Model Monte Carlo simulation for stochastic flutter analysis of wind turbine blades
Li, S., & Caracoglia, L. (2019) · Journal of Wind Engineering and Industrial Aerodynamics · doi:10.1016/j.jweia.2019.02.004 · Cited by 46 · see citing works
Comparative Study of Metamodeling and Sampling Design for Expensive and Semi-Expensive Simulation Models Under Uncertainty
Parnianifard, A., Azfanizam, A. S., Ariffin, M., & Ismail, M. (2019) · SIMULATION · doi:10.1177/0037549719846988 · Cited by 46 · see citing works
“In a recent review paper by Yondo et al., 12 the latest developments of metamodel-based optimization approaches including various metamodeling techniques and sampling design methods have been comprehensively reviewed, with an emphasis on aircraft aerodynamic design.”
Background
Sustainable aircraft design — A review on optimization methods for electric propulsion with derived optimal number of propulsors
Pelz, P. F., Leise, P., & Meck, M. (2021) · Progress in Aerospace Sciences · doi:10.1016/j.paerosci.2021.100714 · Cited by 46 · see citing works
Numerical investigation of minimum drag profiles in laminar flow using deep learning surrogates
Chen, L., Çakal, B. A., Hu, X., & Thuerey, N. (2021) · Journal of Fluid Mechanics · doi:10.1017/jfm.2021.398 · Cited by 43 · see citing works
Multi-Objective Optimization of Cascade Blade Profile Based on Reinforcement Learning
Qin, S., Wang, S., Wang, L., Wang, C., Sun, G., & Zhong, Y. (2020) · Applied Sciences · doi:10.3390/app11010106 · Cited by 41 · see citing works
“Surrogate models can provide aerodynamic information rapidly, which reduces the computation during optimization and shortens optimization period [52,53].”
Background
Invertible Neural Networks for Airfoil Design
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Authors: Yondo, R., Andrés, E., & Valero, E.
Affiliations: Universidad Politécnica de Madrid (ES)
Ingeniería de Sistemas para la Defensa de España (ES)
Corresponding author: Yondo, Raul
ORCID: Andrés, Esther
Valero, Eusebio
Venue: Progress in Aerospace Sciences
Publisher: Elsevier BV
Published: 2018-01
Volume: 96
Pages: 23-61
ISSN: 0376-0421 (ISSN-L)
DOI: 10.1016/j.paerosci.2017.11.003
OpenAlex: W2776493547
Cited by: 311
References: 548
Citations by year: 2026: 29 · 2025: 50 · 2024: 44 · 2023: 32 · 2022: 49 · 2021: 41 · 2020: 39 · 2019: 26 · 2018: 1
FWCI: 13.56
Type: journal article
Language: en
Open access: closed
Licence: www.elsevier.com/tdm/userlicense/1.0/ (tdm)
Locations: Progress in Aerospace Sciences (publishedVersion)
Funders: European Commission Seventh Framework Programme (FP7-PEOPLE-2013-ITN, 608087)
Topics: Physical Sciences › Computer Science › Computational Theory and Mathematics › Advanced Multi-Objective Optimization Algorithms
Social Sciences › Decision Sciences › Statistics, Probability and Uncertainty › Probabilistic and Robust Engineering Design
Physical Sciences › Engineering › Computational Mechanics › Computational Fluid Dynamics and Aerodynamics
Keywords: surrogate models, aircraft aerodynamic design, surrogate-based optimization, experimental design, sampling strategy, dimensionality reduction, sensitivity analysis, uncertainty quantification, multi-fidelity modeling, civil transport aircraft, computational complexity reduction

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