Physics-Guided Deep Learning for Real-time Simulation of Unsteady Multiphase Flows in Dynamic Hydraulic Systems
Keywords:
Physics-guided deep learning, real-time simulation, unsteady multiphase flows, dynamic hydraulic systemsAbstract
Accurate and real-time prediction of complex fluid dynamics, particularly unsteady multiphase flows in adaptive hydraulic infrastructures, is crucial for optimizing performance, ensuring safety, and enabling intelligent control. Traditional Computational Fluid Dynamics (CFD) methods, while powerful, often incur prohibitive computational costs and time for such dynamic, high-fidelity simulations. This article explores the emerging paradigm of physics-guided deep learning, specifically focusing on hybrid physics-informed neural solvers, as a transformative approach to overcome these limitations. We delve into the theoretical underpinnings of Physics-Informed Neural Networks (PINNs), their advancements, and how hybrid models integrate sparse data with fundamental physical laws (e.g., Navier-Stokes equations) to enable rapid and accurate predictions of turbulent, unsteady multiphase flow phenomena. The discussion highlights their potential for real-time operational insights, predictive maintenance, and adaptive control in complex hydraulic systems such as dams, spillways, and smart water networks. Key challenges related to scalability, training stability, and real-world deployment are also addressed, alongside promising future research directions in this rapidly evolving field.
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Copyright (c) 2025 Dr. Katrin Vogel
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