Advancing Aerodynamic Coefficient Prediction: A Hybrid Model Integrating Deep Learning and Optimization Techniques

The aerospace industry increasingly relies on predictive models for aerodynamic coefficients to enhance design, performance, and optimization. While traditional methods like Computational Fluid Dynamics (CFD) and wind tunnel simulations offer accurate predictions, they are computationally intensive and time-consuming. This study explores a novel approach that fuses advanced Deep Learning (DL) architectures with Optimization Techniques to achieve faster and more accurate predictions of aerodynamic coefficients. Building on the foundation of Convolutional Neural Networks (CNNs), we introduce hybrid models that integrate Evolutionary Algorithms and Gradient-Based Optimization to improve the accuracy, generalization, and adaptability of predictions. The proposed framework is validated on datasets derived from CFD simulations and wind tunnel experiments, demonstrating superior accuracy, reduced computational cost, and robust performance across diverse aerodynamic conditions. This study highlights the potential of combining DL and optimization methods as a transformative tool for real-time aerodynamic analysis, paving the way for more efficient Aerospace Design and decision-making. Future research directions include expanding the model to handle complex geometries and dynamic flight conditions.

@article{Zerouaoui2025,
  title     = {Advancing Aerodynamic Coefficient Prediction: A Hybrid Model Integrating Deep Learning and Optimization Techniques},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {16},
  number    = {6},
  year      = {2025},
  publisher = {The Science and Information Organization},
  author    = {Jad Zerouaoui and Rachid Ed-daoudi and Badia Ettaki and El Mahjoub Chakir},
  doi       = {10.14569/IJACSA.2025.0160652},
  url       = {https://doi.org/10.14569/IJACSA.2025.0160652}
}