ISBN 978-3-8439-5753-3

978-3-8439-5753-3, Reihe Informatik

Piet Jarmatz
Scalable Data-driven Methods for Advanced Molecular-continuum Flow Simulation

248 Seiten, Dissertation Helmut-Schmidt-Universität Hamburg (2026), Hardcover, B5

Zusammenfassung / Abstract

Fluid flow phenomena play an important role across a spectrum of disciplines in science and engineering. Since experiments are not always an efficient or feasible way to study them, computer simulations of these phenomena have turned out to be one of the essential tools for progress in this area. However, computational fluid dynamics is a challenging field, especially when characteristics on multiple length and time scales need to be captured. Coupled methods are one way to simulate such a flow. A molecular-continuum coupling simulation software is given by MaMiCo, an open source framework for massively parallel execution on high-performance computing systems.

This thesis presents extensions of MaMiCo by new features, leading to novel ways to enhance multiscale flow simulations based on data-driven analysis of the coupling data. First, thermal noise filtering methods make the coupling data more consistent across models on different scales. A novel space-time formulation of the non-local means algorithm is introduced, outperforming conventional filtering methods. Second, machine learning-based surrogates reduce computational cost of expensive fine-scale solvers. An advanced hybrid model architecture is presented where a convolutional autoencoder deals with the spatial extent of the flow data, while a recurrent neural network is used to capture its temporal correlation. Third, parallel-in-time integration allows to expand a coupled simulation to multiple temporal scales. An implementation based on a variant of the Parareal algorithm is presented here, where a Lattice Boltzmann solver is used as hydrodynamic predictor to supervise the microscopic system in time.

The capabilities of the novel data-driven enhancements are demonstrated in molecular-continuum flow scenarios including a three-dimensional Couette flow and a vortex street scenario. Large-scale MaMiCo scalability experiments on supercomputing platforms validate the benefits of the presented new methods.