MPFBench: A Large Scale Dataset for SciML of Multi-Phase-Flows: Droplet and Bubble Dynamics

Mehdi Shadkhah, Ronak Tali, Ali Rabeh, Ethan Herron, Cheng-Hau Yang, Abhisek Upadhyaya, Adarsh Krishnamurthy, Chinmay Hegde, Aditya Balu, Baskar Ganapathysubramanian

Keywords: Scientific Machine Learning (SciML), Multiphase Flow, Complex Physics Simulation, Lattice Boltzmann Method (LBM), Droplet Dynamics, Bubble Dynamics

TL;DR: SciML benchmark with 11000 two-phase flow LBM simulations

Abstract: Multiphase fluid dynamics, such as falling droplets and rising bubbles, are critical to many industrial applications. However, simulating these phenomena efficiently is challenging due to the complexity of instabilities, wave patterns, and bubble breakup. This paper investigates the potential of scientific machine learning (SciML) to model these dynamics using neural operators and foundation models. We apply sequence-to-sequence techniques on a comprehensive dataset generated from 11,000 simulations, comprising 1 million time snapshots, produced with a well-validated Lattice Boltzmann method (LBM) framework. The results demonstrate the ability of machine learning models to capture transient dynamics and intricate fluid interactions, paving the way for more accurate and computationally efficient SciML-based solvers for multiphase applications.

Primary Area: datasets and benchmarks

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Submission Number: 12095