Application of Machine Learning in non-Newtonian Flows

With the abundance of available data and advancements in machine learning, there is a growing need to shift toward data-driven and optimization-based approaches for solving problems that were traditionally solved using CFD techniques. Neural networks, especially, offer great potential in capturing the chaotic nature of dynamic phenomena. On the other hand, established physical laws serve as benchmarks of knowledge, validating the results obtained through computational and data-driven methods. In this work, we have implemented the latest Physics Informed Neural Networks (PINN) to solve non-newtonian flows and compared their accuracies with theoretical/numerical solutions and available experimental data. Additionally, we have used numerical simulations to generate data for non-newtonian flow over spheres under a range of operational parameters. We have shown how data from numerical experiments can be used to predict properties, like drag, which would otherwise have much higher computational costs. Finally, we have demonstrated that machine learning could be used to optimize the shape of objects in non-newtonian flows without the requirement for performing numerical simulations over the entire domain of different shape parameters.