A Review of Intelligent Airfoil Aerodynamic Optimization Methods Based on Data-Driven Advanced Models
Abstract
:1. Introduction
2. Data-Driven Advanced Model in Critical Steps of Airfoil Aerodynamic Optimization
2.1. Geometric Parameterization
2.2. Aerodynamic Solving and Performance Evaluation
2.2.1. Aerodynamic Coefficient Evaluation
2.2.2. Flow Field Prediction
2.2.3. Transition Modeling and Turbulence Modeling
2.3. Optimization Model
2.3.1. Optimization Pattern
2.3.2. Optimization Strategy
3. Conclusions
- (a)
- Expanding the database for aerodynamic modeling. A large and sufficient amount of data is the basis for aerodynamic modeling. There is no sufficient publicly available dataset with abundant flow field characteristics for aerodynamic optimization. The quantity and quality of data limit the further development and application of models. In order to address the above problem, it is recommended to develop aerodynamic modeling strategies applicable for small-scale data, e.g., data augmentation and meta-learning-based modeling methods. The construction of a large-scale aerodynamic database might also be enhanced, and the data fusion of wind tunnel test, flight test, and numerical simulation data could be considered.
- (b)
- Improving the interpretability and generalization of advanced models. Most of the current aerodynamic models are “black box” models, making it hard for researchers to understand the learning principle and process in the network. Improving the interpretability of advanced models and transforming models from the original “black box” to “gray box” or even “white box” will help to enhance the understanding of aerodynamics and realize the update of knowledge in the progress of optimization. On the other hand, improving the generalizability of advanced models is also significant for the expansion of application scenes and the improvement of optimization design efficiency.
- (c)
- Enhancing the ability of advanced models to solve 3D complex configuration optimization. The “dimensional disasters” by three-dimensional complex configurations are a great challenge for optimization design. Compared to aerodynamic optimization of 2D configurations, the difficulty of each process of 3D complex configuration optimization increases significantly. Most of the current advanced models focus on improving the efficiency of solving existing problems, and advanced models should be developed to address the unsolved issue by the traditional methods, especially to facilitate the aerodynamic optimization of 3D complex configurations.
Author Contributions
Funding
Data Availability Statement
Conflicts of Interest
References
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Target | Typical Advanced Models | References |
---|---|---|
Aerodynamic coefficient evaluation | Response surface method | Ahn et al. [81] Giunta et al. [82] |
Kriging model | Han et al. [83,84] | |
ANN/DNN | Oktay et al. [85] Wang et al. [67] Bouhlel et al. [86] Li and Zhang et al. [87] | |
CNN | Zhang et al. [88] Yu et al. [89] Bakar et al. [90] | |
Physical informed machine learning | Sun et al. [91] | |
Flow field prediction | ANN/DNN | Renganathan et al. [28] |
CNN | Bhatnagar et al. [29] | |
LSTM | Mohan and Gaitonde [23] | |
Physical informed machine learning | Raissi et al. [45,46] | |
Field inversion and machine learning | Holland et al. [92] | |
VAE | Wang et al. [93] | |
GAN | Wu et al. [94] | |
GCN | Lan et al. [95] | |
Transition modeling and turbulence modeling | ANN/DNN | Tieghi et al. [96] Wang et al. [97,98] |
CNN | Zafar et al. [99] | |
Physical informed machine learning | Wang et al. [100] | |
Field inversion and machine learning | Yang et al. [101] Zhang et al. [102] | |
Symbolic regression | Zhao et al. [103] Wu and Zhang et al. [104] |
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Wang, L.; Zhang, H.; Wang, C.; Tao, J.; Lan, X.; Sun, G.; Feng, J. A Review of Intelligent Airfoil Aerodynamic Optimization Methods Based on Data-Driven Advanced Models. Mathematics 2024, 12, 1417. https://doi.org/10.3390/math12101417
Wang L, Zhang H, Wang C, Tao J, Lan X, Sun G, Feng J. A Review of Intelligent Airfoil Aerodynamic Optimization Methods Based on Data-Driven Advanced Models. Mathematics. 2024; 12(10):1417. https://doi.org/10.3390/math12101417
Chicago/Turabian StyleWang, Liyue, Haochen Zhang, Cong Wang, Jun Tao, Xinyue Lan, Gang Sun, and Jinzhang Feng. 2024. "A Review of Intelligent Airfoil Aerodynamic Optimization Methods Based on Data-Driven Advanced Models" Mathematics 12, no. 10: 1417. https://doi.org/10.3390/math12101417
APA StyleWang, L., Zhang, H., Wang, C., Tao, J., Lan, X., Sun, G., & Feng, J. (2024). A Review of Intelligent Airfoil Aerodynamic Optimization Methods Based on Data-Driven Advanced Models. Mathematics, 12(10), 1417. https://doi.org/10.3390/math12101417