Deep Learning Applied to Fracture Mechanics Boundary Pressure Vessel and Support

This comprehensive study presents the application of deep learning algorithms to fracture mechanics problems, in the context of industrial applications. The primary aim is to significantly enhance the efficiency of sensitivity analysis for structural systems, prioritizing accuracy and secure decision-making processes. The research’s core is centered on training sophisticated neural networks to accurately predict the evolution of Stress Intensity Factors (SIF) in pressure vessels under the influence of time-dependent thermal and mechanical loads. To assemble the necessary datasets for this analysis, traditional fracture mechanics calculations are conducted in line with the RCC-M code, as referenced in [5]. One of the key challenges addressed in this study is the irregularity of time steps and the varying durations of loads applied to the system. Addressing this complexity requires extensive data preprocessing and the development of a custom network architecture, tailored to handle these irregularities efficiently. In the quest to achieve optimal performance, a model that combines convolutional layers and recurrent layers has been used. The decision to utilize neural networks is further justified by the development of a model that accounts for geometric variabilities. This enhancement significantly improves the network’s adaptability and versatility, making it more suited for real-world industrial applications. Further adding to the model’s robustness, the study incorporates insights from intricate 3D finite element simulations into the training process of the final neural network. This integration enriches the network’s ability to make nuanced predictions, capturing the complexities of fracture mechanics in a more comprehensive manner. The study also delves into the implications of these findings for the industry. The integration of deep learning into fracture mechanics can improve the way structural assessments are conducted, offering a more rapid yet equally reliable alternative to conventional methods. This advancement not only promises to improve the safety and durability of industrial structures but also has the potential to streamline the design and maintenance processes, leading to cost-effective and time-efficient practices. In conclusion, this research is part of a new era in industrial applications of fracture mechanics, leveraging the power of deep learning to offer transformative solutions. The integration of neural networks, capable of handling complex data sets and simulating real-world scenarios, marks a significant milestone in the field. The findings of this study have far-reaching implications, promising to enhance the safety, efficiency, and reliability of structural systems across various industries.