Artificial Intelligence-Based Stress Prediction for Rotor Blisk in Gas Turbine Engines

Gas turbine engines are critical components in aerospace, power generation, and industrial applications, consisting of complex rotating and stationary parts subject to extreme mechanical, thermal, and aerodynamic loads. A key component in modern gas turbines is the rotor blisk, which combines the blades and disk into a single unit. Due to its complex geometry and harsh operating conditions, the rotor blisk experiences significant mechanical stresses that must be accurately calculated to ensure reliability, safety, and optimal performance. Traditional methods, such as finite element analysis (FEA), are widely used to calculate stress distributions under various loading conditions. However, FEA is computationally expensive, especially when analyzing multiple scenarios for different operating conditions. This computational cost can become a bottleneck in iterative design studies and real-time decision making. To address this challenge, this study proposes a novel approach that uses deep learning to predict stresses in rotor blisks under varying loads. A deep neural network (DNN) was trained on FEA-generated stress data to learn the relationships between input parameters and resulting stress distributions. The AI-based model was validated using unseen load scenarios for radial, axial, and tangential stress distributions and maximum-minimum stress results, with a maximum deviation of 6% to 15% from FEA results. In addition, the Artificial Intelligence (AI) approach reduced the computational cost by 13,000 times faster than FEA by predicting results instead of solving complex equations. The AI approach enables rapid stress predictions and facilitates real-time design iteration and optimization. These results highlight the transformative potential of AI in engineering simulation, enabling faster, more efficient structural assessments and advancing the optimization of gas turbine components in the aerospace and energy industries.