Integrating Neural Networks and Particle Image Velocimetry for Advanced Digital Twins in Experimental Fluid Mechanics

Digital twins are revolutionizing industries by leveraging machine learning and data-driven models to create dynamic synchronized representations of physical systems. These virtual counterparts operate in real time, bridging the gap between the physical and digital worlds to simulate, predict, optimize, and control system behaviors, thereby enhancing the performance and efficiency of their physical analogs. Their transformative potential is particularly evident in manufacturing, where they contribute to precision metrology and quality assurance. This research explores the implementation of digital twins in experimental fluid mechanics, focusing on real-time data integration from in-line coherent imaging setups. By incorporating real-time sensor data or validated datasets, the goal is to develop dynamic models that accurately represent the behavior of the system under varying operating conditions. The thesis emphasizes the use of advanced deep learning algorithms, including artificial neural networks (ANNs) and Vision Transformers (ViT), to create an end-to-end model for analyzing Particle Image Velocimetry (PIV) data. Convolutional neural network (CNNs) blocks, based on optical flow estimation techniques, are used to extract flow patterns by learning spatial features and correlations from PIV images. To refine predictions by capturing temporal dependencies and transient behaviors, iterative recurrent CNN blocks are integrated. However, these deep learning models, typically trained on synthetic datasets with reference results derived from analytical equations or high-fidelity numerical models, face challenges in robustness and generalization when applied to real-world industrial scenarios. To address this, experimental PIV datasets, focused on flow past a circular cylinder, were generated to evaluate the performance of RAFT-PIV (Recurrent All-Pairs Field Transforms), the state-of-the-art CNN model for optical flow estimation (Paper A). These datasets included variations in key parameters such as particle size, ...