Exchange processes across a porous-media free-flow interface occur in a wide range of environmental, technical and bio-mechanical systems. In the course of these processes, flow dynamics in the porous domain and in the free-flow domain exhibit strong coupling, often controlled by mechanisms at the common interfaces. Therefore, understanding the underlying processes is decisive. The primary objective of this research is to analyse and improve theories of non-isothermal, multiphase, multi-component flow processes at a porous-media free-flow interface and their influence on flow in the porous media system. The goal is to transfer the knowledge gained through the analysis of experimental and simulation data into an efficient multi-scale modelling approach using a dispersion concept where turbulence-related processes are accounted for in ample detail while still retaining a degree of efficiency, allowing for the simulation of real-world applications. To this end, it is crucial to first identify and understand the system-governing phenomena and processes observable from detailed simulations and experimental data. With the help of machine learning algorithms, these findings shall then be incorporated into advanced parametrisations or used in order to extend existing balance equations.
|Project Number||PN 1-5|
|Project Name||Dispersion concept for interface closure in the context of coupling free-flow and porous media multiphase flow on the REV scale|
|Project Duration||January 2019 - June 2023|
|Project Leader||Rainer Helmig
|Project Members||Ingo Steinwart, collaborative applicant
Edward Coltman, PhD Researcher
|Project Partners||The development of a coupling concept for multiphase flow processes of a laminar and turbulent free flow and a porous-media flow domain (in cooperation with SFB 1313) will get data-rich information from small scale measurements from PN1-3 and PN1-4 and detailed simulations results for multiphase flow from PN1-2. The results will be compared to REV-based wind tunnel experiments from our international partners. To develop the new parameter-dependent dispersion concept we need new machine learning methods to improve the complex model descriptions. This will be done in strong cooperation with PN-6 and especially with the group of PN6-3. For experimental data, we have international external partners at Utrecht University, the University of Colorado Boulder, and the University of Texas at Arlington.|