Integrated CFD–ANN Framework for Predicting Blade Deformation and Aerodynamic Performance

 

1. Introduction

The integration of Computational Fluid Dynamics (CFD) with Artificial Neural Networks (ANN) represents a transformative approach in modern engineering research. Traditional CFD methods, while accurate, are computationally intensive, especially for complex blade deformation and aerodynamic response analysis. By coupling CFD with ANN models, researchers can achieve faster predictions without compromising accuracy, enabling efficient optimization of blade design in renewable energy and turbomachinery applications.

2. CFD-Based Aerodynamic Modeling

CFD plays a critical role in capturing complex airflow behavior around rotating blades, including turbulence, pressure distribution, and wake interactions. In this research context, CFD simulations provide high-fidelity datasets that describe aerodynamic loads and deformation patterns under varying operating conditions. These simulations form the foundational knowledge base for training intelligent predictive models.

3. Artificial Neural Networks for Deformation Prediction

ANNs are employed to learn nonlinear relationships between aerodynamic forces and structural deformation. Once trained, the ANN can rapidly predict blade responses under new conditions, significantly reducing computational time. This data-driven capability is particularly valuable for iterative design and real-time performance assessment.

4. Fluid–Structure Interaction Integration

The coupling of aerodynamic forces with structural deformation is essential for accurate blade performance evaluation. The integrated CFD–ANN framework effectively captures fluid–structure interaction effects, allowing researchers to analyze how airflow-induced stresses influence blade integrity, efficiency, and lifespan in energy systems.

5. Applications in Renewable Energy Systems

This hybrid modeling approach has strong implications for renewable energy technologies, especially wind turbine blade optimization. Accurate deformation and aerodynamic predictions support improved energy capture, reduced material fatigue, and enhanced operational reliability, contributing to more sustainable and cost-effective energy generation.

6. Future Research Directions

Future research can expand this framework by incorporating deep learning architectures, real-time sensor data, and multi-objective optimization techniques. Such advancements will further enhance predictive accuracy and support the development of intelligent, adaptive energy systems aligned with smart city and global sustainability goals.

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