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A review on design of experiments and surrogate models in aircraft real-time and many-query aerodynamic analyses
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“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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“Different mathematical models have been usually used to construct surrogate models [26].”
MethodAI-Based Surrogate Models for the Food and Drink Manufacturing Industry: A Comprehensive Review
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“These types of analysis, particularly HF CFD, can be time consuming and/or require significant computational resources to perform which renders them inadequate for a typical gradient based or genetic algorithm (GA; Yondo et al. 2018) MDO procedure, where an extensive number of evaluations is required.”
MethodStructural Design Space Exploration Using Principal Component Analysis
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MethodHuang, M., Li, Z., Li, J., & Song, L. (2022) · Journal of Turbomachinery · doi:10.1115/1.4054067 · Cited by 10
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Katsuno, E. T., Lidtke, A. K., Düz, B., Rijpkema, D., Dantas, J. L. D., & Vaz, G. B. (2021) · Computers & Fluids · doi:10.1016/j.compfluid.2021.105129 · Cited by 9
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“Numerical solutions such as Reduced Order Models [38, 39], surrogate-models [40, 41] and multifidelity [42] can alleviate the computation burden.”
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“Radial basis functions are widely used in many fields for solving technical and non-technical problems (Liu et al. 2022; Yondo et al. 2018).”
BackgroundHigh-Efficiency Data Fusion Aerodynamic Performance Modeling Method for High-Altitude Propellers
Zhang, M., Jiao, J., Zhang, J., & Zhang, Z. (2024) · Drones · doi:10.3390/drones8060229 · Cited by 6
“Yondo et al. [20] provided an overview of the development of aerodynamic data surrogate model methods and the impact of sampling methods on the construction of databases in the aerospace field.”
MethodOn Constructing Efficient UAV Aerodynamic Surrogate Models for Digital Twins
Aláez, D., Prieto, M., Villadangos, J., & Astráin, J. J. (2024) · IEEE Transactions on Industrial Informatics · doi:10.1109/tii.2024.3431106 · Cited by 6
“Sampling the design space, i.e., selecting a good set of data points, is key in computer-based simulations, with the prevailing goal of maximizing the amount of information gained from a limited number of samples [15].”
BackgroundMetamodeling Approach for Wing Structural Sizing of Box-Wing Aircraft
Palaia, G., Salem, K. A., Binante, V., & Carrera, E. (2025) · Journal of Aircraft · doi:10.2514/1.c038131 · Cited by 6
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“Yondo et al.(22) summarized the metamodel-based optimization methods in aircraft design, in which they pointed out that the algorithm for sampling located in interesting regions should be addressed.”
BackgroundHuang, M. J., Li, Z., & Li, J. (2022) · Journal of Turbomachinery · doi:10.1115/1.4055441 · Cited by 6
Liu, X.-X., Xie, Q.-Z., Du, R., & Zhang, F. (2022) · Aerospace Science and Technology · doi:10.1016/j.ast.2022.107916 · Cited by 6
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“The existing metamodeling review extensively and focuses mainly in engineering optimization problem such as complexity of crashworthiness problem [17], simulation complex system of aircraft and aerodynamic design [18], architecture design of optimization [3], simulation-based complex engineering design and employing s…”
BackgroundDesign of computer experiments for developing seismic surrogate models
Burton, H. V., Xu, H., & Yi, Z. (2021) · Earthquake Spectra · doi:10.1177/87552930211033309 · Cited by 6
“The model-free approach chooses the sampling points based on the design space (e.g. distance-based or uniform), whereas the adaptive approach uses results generated by a predictive model (mechanistic and/or surrogate) to iteratively guide the sampling (Yondo et al., 2018).”
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“To evaluate the objective function, a space-filling method Design of Experiments (DOE) was utilized to generate sample points within the design space (Mack et al. 2007; Audet et al. 2000; Yondo et al. 2018).”
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“Thus, design space exploration using Design-ofExperiments (DoE) (Yondo et al., 2018) has been suggested and utilized in this work instead of DO.”
BackgroundMethodInfluentialZheng, C., Jin, Z., Dong, Q., & Yang, Z. (2024) · Advances in Mechanical Engineering · doi:10.1177/16878132231226056 · Cited by 5
“33 Factorial design enables an experimenter to study the effect of the design parameters on the objective coefficient.”
MethodTransient Surrogate Modeling for Thermal Management Systems
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Li, G., & Zhu, Q. (2021) · IEEE Sensors Journal · doi:10.1109/jsen.2021.3081439 · Cited by 5
Tullu, A., Lee, B.-S., & Hwang, H.-Y. (2020) · Applied Sciences · doi:10.3390/app10165499 · Cited by 5
Multi-stage Multi-fidelity Information Correction for Artificial Neural Network Based Meta-modelling
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“Naturally then, traditional interpolation or meta-modelling techniques such as Polynomial Response Surface Modelling (RSM), Radial Basis Functions, Kriging, Support Vector Regression and Artificial Neural Networks may be employed [18], [9] (assuming sufficient samples are available).”
MethodAmin, M. M., & Kiani, A. (2019) · Journal of Applied Fluid Mechanics · doi:10.29252/jafm.13.01.30253 · Cited by 5
karafi, Y., Moussaoui, Z., & Majd, B. A. E. (2025) · Expert Systems with Applications · doi:10.1016/j.eswa.2025.128624 · Cited by 4
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Goal-Oriented Feature Extraction: A Novel Approach to Enhance Data-Driven Surrogate Models
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Li, X., Liu, M., & Li, S. (2022) · International Journal of Green Energy · doi:10.1080/15435075.2022.2131433 · Cited by 4
“The detailed steps of CGE-LE algorithm are shown as follows: Step 1: n samplesX ¼ ½x1; x2; � � � ; xn�Tare generated from the design space [lb, ub] using Latin Hypercube Sampling (LHS) (Yondo et al. 2018).”
MethodInfluentialOptimal Design of Validation Experiment for Material Deterioration
Song, X., Sun, D., & Liang, X. (2023) · Materials · doi:10.3390/ma16175854 · Cited by 4
“Design of experiment (DoE) [26,27] has been used to improve experimental performance in engineering and has been studied extensively.”
MethodUncertain data in initial boundary value problems: Impact on short and long time predictions
Nordström, J. (2024) · Journal of Computational Physics · doi:10.1016/j.jcp.2024.112891 · Cited by 4
“However, that the quality of the input data may be problematic have also been realised e.g. in aerospace [31–33], nuclear physics [34,35], oil prospecting [36–39], weather forecasts [40–42] and climate predictions [43–46] to name a few.”
BackgroundDataset for Electric Machine Performance Indices Prediction With Handling of Geometric Constraints
Li, J., Tan, N. M. L., Fu, W. N., & Xu, Z. (2024) · IEEE Transactions on Industry Applications · doi:10.1109/tia.2024.3510212 · Cited by 4
“These methods primarily consider the randomness and non-repeatability encountered in field experiments or computer simulations [24].”
Cost-Aware Adaptive Design of Experiment with Nonstationary Surrogate Model for Wind Tunnel Testing
Choi, U., Kim, J., & Ahn, J. (2020) · International Journal of Aeronautical and Space Sciences · doi:10.1007/s42405-020-00250-1 · Cited by 4
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Kocaman, E. S., Chen, B., & Pinho, S. T. (2020) · Composite Structures · doi:10.1016/j.compstruct.2020.112532 · Cited by 4
A Multi-Objective Factorial Design Methodology for Aerodynamic Off-Takes and Ducts
Spanelis, A., & Walker, A. D. (2022) · Aerospace · doi:10.3390/aerospace9030130 · Cited by 4
“[16] presented a review of DoE and surrogate models for aerody-”
MethodGalindo, J., Navarro, R., Moya, F. N., & Peiró, A. C. (2023) · Applied Sciences · doi:10.3390/app13116361 · Cited by 4
Optimal Design of Validation Experiments Based on Area Metric Factor and Fuzzy Expert System
Suo, B., Sun, D., Zhang, B., & Liang, X. (2019) · IEEE Access · doi:10.1109/access.2019.2928885 · Cited by 4
“Design of experiments (DoE) can be divided into two general categories, namely classical DoE (such as Full and fractional-factorial designs, Central composite designs and Box-Behnken designs, Optimal designs, Orthogonal arrays experiments) and modern DoE (such as Random designs, Quasi-random designs, Projections-based…”
MethodThurman, C. S., & Somero, J. R. (2020) · Ocean Engineering · doi:10.1016/j.oceaneng.2020.107566 · Cited by 4
Overview of Stochastic Model Updating in Aerospace Application Under Uncertainty Treatment
Bi, S., & Beer, M. (2021) · SpringerBriefs in statistics · doi:10.1007/978-3-030-83640-5_8 · Cited by 4
Increasing the efficiency of simulation-based design explorations via metamodelling
Brunetti, G. L. (2020) · Journal of Building Performance Simulation · doi:10.1080/19401493.2019.1707875 · Cited by 4
“…2014; Østergård, Jensen, and Maagaard 2018; Prada, Gasparella, and Baggio 2018), the environmental domain (Villa-Vialaneix et al. 2012; Razavi, Tolson, and Burn 2012; Fienen, Nolan, and Feinstein 2016) and engineering design (Simpson et al. 2001; Queipo et al. 2005; Yondo, Andres, and Valero 2018).”
BackgroundSurrogate Modeling for Accelerating Optimization of Complex Systems in Chemical Engineering
Zhou, J., & Ren, J. (2024) · doi:10.1002/9783527845491.ch10 · Cited by 4
Mirakhori, G., Doucet, J., Chidami, S., Blais, B., & Chaouki, J. (2025) · Chemical Engineering Science · doi:10.1016/j.ces.2025.122173 · Cited by 3
Peter, J., Bennehard, Q., Heib, S., Hantrais-Gervois, J.-L., & Moëns, F. (2025) · Computers & Fluids · doi:10.1016/j.compfluid.2025.106838 · Cited by 3
Field Validation of Hydraulic Model-Based Leak Localization Methods in Water Distribution Networks
Liu, Y., Pecci, F., & Stoianov, I. (2025) · Journal of Water Resources Planning and Management · doi:10.1061/jwrmd5.wreng-7100 · Cited by 3
On the integration of geometry agnostic variational-autoencoders into large-scale SVD based models
Eiximeno, B., Miró, A., Kutz, J. N., Rodriguez, I., & Lehmkuhl, O. (2025) · Computers & Fluids · doi:10.1016/j.compfluid.2025.106797 · Cited by 3
Wu, K., Zhang, F., Zhang, Y. H., Yan, Y., & Butt, S. I. (2021) · Applied Soft Computing · doi:10.1016/j.asoc.2021.107883 · Cited by 3
A Study on the Surrogate-Based Optimization of Flexible Wings Considering a Flutter Constraint
Lunghitano, A., Afonso, F., & Suleman, A. (2024) · Applied Sciences · doi:10.3390/app14062384 · Cited by 3
“With this goal in mind, different approaches are considered in the literature, namely, multi-fidelity models [3,4], conventional [5] and machine learning-based surrogate models [6], and reduced-order models [7].”
BackgroundRehman, M. U., Machchhar, R. J., & Bertoni, A. (2024) · Proceedings of the Design Society · doi:10.1017/pds.2024.273 · Cited by 3
“Yet another method that is commonly used in SoS simulation is Surrogate or meta-modeling (Yondo et al., 2018).”
Design of Experiment for Facilitating Effective Research Program Development
Kopiika, N., Blikharskyy, Y., Khmil, R., & Tereshko, A. (2024) · Lecture notes in civil engineering · doi:10.1007/978-3-031-67576-8_24 · Cited by 3
Meta pseudo label tabular-related regression model for surrogate modeling
Kim, S., & Kim, J. (2024) · Expert Systems with Applications · doi:10.1016/j.eswa.2024.125520 · Cited by 3
Non-intrusive aerodynamic shape optimisation with a discrete empirical interpolation method
Marques, S. P., Kob, L. L., Robinson, T., Yao, W., & Sun, L. (2021) · AIAA SciTech Forum · doi:10.2514/6.2021-0172 · Cited by 3
A non-intrusive model order reduction approach for parameterized time-domain Maxwell's equations
Li, K., Huang, T., Li, L., Zhao, Y., & Lanteri, S. (2022) · Discrete and Continuous Dynamical Systems - B · doi:10.3934/dcdsb.2022084 · Cited by 3
Khan, K. H., Kapania, R. K., & Schetz, J. A. (2023) · AIAA AVIATION Forum · doi:10.2514/6.2023-3944 · Cited by 3
Robust Aerodynamic Parameter Estimation of Unmanned Aircraft Based on Two-step Identification
Kimathi, S., & Lantos, B. (2023) · Periodica Polytechnica Electrical Engineering and Computer Science · doi:10.3311/ppee.21948 · Cited by 3
Durkin, A., Vinestock, T., & Guo, M. (2024) · Carbon Capture Science & Technology · doi:10.1016/j.ccst.2024.100319 · Cited by 4
Metadata
- 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
- Venue
- Progress in Aerospace Sciences
- Publisher
- Elsevier BV
- Published
- 2018-01
- Volume
- 96
- Pages
- 23-61
- ISSN
- 0376-0421 (ISSN-L)
- 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
- 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
- Physical Sciences › Engineering › Computational Mechanics › 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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