Integration of Computational Techniques for System Modeling and Data Assimilation in Industrial and Life Science Problems

This thesis presents a series of innovative studies demonstrating the development and application of advanced computational and machine learning methodologies to address real-world, three-dimensional problems with significant practical relevance. Unlike conventional research, which often relies on idealized or simplified scenarios, this work directly tackles complex, practical challenges found in everyday engineering and biomedical contexts. The first part of the thesis focuses on the development of a comprehensive system model for a washer-dryer, incorporating both analytical and machine learning approaches to predict the work cycle of a fan/heater module. The second part investigates the wake flow fields of wind turbines, a key factor in optimizing renewable energy generation in wind farms. Through advanced simulations and dimensionality reduction techniques, the analysis explores the effects of ice accretion, structural flexibility, and turbulence, offering actionable insights for enhancing wind farm layouts and turbine performance. The third and fourth chapters introduce a novel approach to cooling system optimization, leveraging neural networks for flow prediction and deep reinforcement learning for intelligent control. Finally, the thesis addresses a critical biomedical challenge: predicting boundary conditions in cardiovascular flows using data assimilation. By integrating the full Navier-Stokes equations with observational data, the research achieves unprecedented accuracy in modeling patient-specific blood flow dynamics, with direct implications for diagnostic and therapeutic applications. Collectively, this thesis advances the state-of-the-art in computational modeling by applying cutting-edge techniques to practical, high-dimensional, and non-linear problems in diverse domains. The methodologies and insights presented herein establish a foundation for tackling complex real-world problems.