The increasing demands for efficient and reliable performance in aerospace engineering have driven significant advancements in the structural analysis of airfoil components. Airfoils, being critical elements of aircraft and turbine designs, experience complex stress distributions under varying operational conditions. Accurately predicting these stresses is essential to ensure structural integrity, optimize performance, and extend the lifespan of the components. Traditional methods of stress analysis, such as Finite Element Analysis (FEA), are widely used but often involve intensive computational resources and time. To address these challenges, this report explores a Convolutional Neural Network (CNN)-based approach for stress prediction in airfoil structures, offering a novel and efficient solution. Convolutional neural networks have demonstrated remarkable capabilities in extracting spatial features from data, making them particularly suitable for tasks involving image processing and pattern recognition. Leveraging these strengths, the CNN-based method for stress prediction transforms the stress analysis problem into a data-driven learning task.
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