Inferring High-Pressure Homogenizing Valve Dynamics Using Machine Learning Techniques.

High-pressure homogenizing valves are critical in a variety of industrial applications, including food processing and pharmaceuticals, where precise fluid dynamics under extreme pressures must be achieved to ensure performance and reliability. Traditional Computational Fluid Dynamics (CFD) methods, while essential, often face convergence issues and require extensive computational resources to accurately simulate the typical small-scale and high-pressure working regimes of these valves. In this thesis is proposes a novel approach of this problem that uses Physics-Informed Generative Adversarial Networks (GANs) to enhance the accuracy and efficiency of CFD simulations for these valves. The study evaluates multiple GAN architectures, including standard GANs, Conditional GANs (CGANs), and a Conditional Wasserstein GAN with Gradient Penalty (WGAN-GP), incorporating physics-informed constraints that embed conservation laws directly into the learning process. Statistical methods were employed to verify whether these conservation laws were violated in the generated data, ensuring in this way the consistency with physical principles. The CFD simulations used for this work were provided by the company overseeing the project. These simulations were rendered in 2D sections, focusing on pressure and velocity fields, with a dataset composed of 9 training simulations and 2 simulations withheld for testing. The quality of the generated simulations was assessed using the Fréchet Inception Distance (FID) to compare the real and generated data distributions. Our results show that the Conditional WGAN-GP, though requiring longer training times and greater computational resources, produced simulations that adhered most closely to the physical constraints, accurately capturing the complex fluid behavior within the valve. In contrast, standard GANs delivered faster training times but were slightly less precise, making them better suited for rapid, preliminary assessments. Additionally, a non-physics-informed pix2pix model was employed to generate valve simulations based on specific geometries. Despite the absence of explicit physical constraints, the pix2pix model exhibited robust performance, even when generating out-of-sample simulations. Its speed and adaptability make it a valuable tool for early design phases, where quick, accurate simulations are vital. Although these GAN models are not intended to replace traditional CFD simulations, they show considerable promise as supplementary tools. They can streamline the simulation workflow, offering faster results and meaningful insights that enhance overall efficiency in industrial CFD applications. Future research will focus on integrating physics-based constraints into the pix2pix model to combine computational speed with physical consistency, as well as expanding its application to 3D simulations and fluid motion generation using the Navier-Stokes equations as part of the physics-informed framework.