Pretty Porous Science Lecture #19 "Efficiency and Accuracy of Micro-Macro Models for Two-Mineral Reactive Systems" by Stephan Gärttner

May 5, 2022 /

The SFB 1313 "Pretty Porous Science Lecture" #19 will be given by Stephan Gärttner from the FAU Erlangen-Nürnberg (Germany) | 5 May 2022 | 4:00 pm CET

We are pleased to announce that Stephan Gärttner, doctoral researcher at the FAU Erlangen-Nürnberg, will give the SFB 1313 "Pretty Porous Science Lecture" #19. His talk will be on "Efficiency and Accuracy of Micro-Macro Models for Two-Mineral Reactive Systems".

Date: Thursday, 5 May 2022
Time: 4:00 pm CET
Speaker: M. Sc. Stephan Gärttner, Department of applied Mathematics, FAU Erlangen-Nürnberg (Germany)
Lecture title: "Efficiency and Accuracy of Micro-Macro Models for Two-Mineral Reactive Systems" (Authors: Stephan Gärttner, Peter Frolkovič,, Andreas Meier, Florian Frank, Peter Knabner, Nadja Ray)
Place: The lecture will be a hybrid lecture. A small audience is possible in Pfaffenwaldring 61 MML, additionally the lecture is offered online. After registration, you will receive the meeting information.
Registration: If you are interested in participating in the lecture, please contact katharina.heck@iws.uni-stuttgart.de

Abstract

In this talk, we present an effective micro-macro model for reactive flow and transport in evolving porous media exhibiting two competing mineral phases. As such, our approach comprises flow and transport equations on the macroscopic scale including effective hydrodynamic parameters calculated from representative unit cells. Conversely, the macroscopic solutes’ concentrations alter the unit cells' geometrical structure by triggering dissolution or precipitation processes on the distinct mineral surfaces. Gradually, such processes result in complex and hardly predictable geometries.  Accordingly, associate effective parameters cannot be covered accurately by simple heuristic laws. Hence, we derive hydrological parameters directly from the full geometry represented by level-set methods.
The numerical realization of such micro-macro models poses several challenges, especially in terms of computational complexity. Costly computations of effective parameters directly from the representative geometry often constitute a bottleneck in the simulation of heterogeneous porous media. In this talk, the significant performance enhancements arising from machine learning techniques are evaluated. As such, we train convolutional neural networks on permeability estimation and investigate the predictive power and reduction in computation time arising from their application within micro-macro simulations.

 

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