Electrokinetic characterization of conducting two-phase flows in model porous media

PN 3-3

Project description

We would like to construct a numerical two-phase model of an oil-water system containing salt in various concentrations in a model porous environment based on the electrokinetic lattice-Boltzmann approach. We want to characterize the flow and emergent structures that develop in various (liquid) morphologies in porous geometries. Possible changes are the interfacial tension between the two fluids as well as the wetting properties of the solid interfaces through charges or surface modifications.  Electrokinetic and flow data is obtained from experiments of heterogeneous/complex porous structures data and will be provided by Holger Steeb’s group. The physical and image-based data will be used for parameterization and cross-validation of our model. For comparisons H. Steeb can also provide SPH data on selected geometries and different characteristic Ca/Re numbers. We will investigate if the flow characteristics can be classified by using morphological measures, so-called Minkowski functionals or we will use feature extractions from machine-learning algorithms. For the long run we intend to investigate if machine-leaning based algorithms can help to speed up the LB algorithm.

Project information

Project title Electrokinetic characterization of conducting two-phase flows in model porous media
Project leaders Christian Holm (Holger Steeb)
Project partners Holger Claas
Project duration January 2019 - December 2023
Project number PN 3-3

Publications PN 3-3 and PN 3-3 (II)

  1. 2023

    1. C. Lohrmann and C. Holm, “Optimal motility strategies for self-propelled agents to explore porous media,” Physical Review B, vol. 108, no. 5, Art. no. 5, Nov. 2023, doi: 10.1103/PhysRevE.108.054401.
    2. C. Lohrmann and C. Holm, “A novel model for biofilm initiation in porous media flow,” Soft Matter, vol. 19, no. 36, Art. no. 36, 2023, doi: 10.1039/D3SM00575E.

  2. 2022

    1. I. Tischler, F. Weik, R. Kaufmann, M. Kuron, R. Weeber, and C. Holm, “A thermalized electrokinetics model including stochastic reactions suitable for multiscale simulations of reaction-advection-diffusion systems,” Journal of Computational Science, vol. 63, p. 101770, 2022, doi: 10.1016/j.jocs.2022.101770.

  3. 2021

    1. J. Zeman, S. Kondrat, and C. Holm, “Ionic screening in bulk and under confinement,” The Journal of Chemical Physics, vol. 155, no. 20, Art. no. 20, 2021, doi: 10.1063/5.0069340.
    2. A. Wagner et al., “Permeability Estimation of Regular Porous Structures: A Benchmark for Comparison of Methods,” Transport in Porous Media, vol. 138, no. 1, Art. no. 1, 2021, doi: 10.1007/s11242-021-01586-2.
    3. K. Szuttor, P. Kreissl, and C. Holm, “A numerical investigation of analyte size effects in nanopore sensing systems,” The Journal of Chemical Physics, vol. 155, no. 13, Art. no. 13, 2021, doi: 10.1063/5.0065085.
    4. K. Szuttor, F. Weik, J.-N. Grad, and C. Holm, “Modeling the current modulation of bundled DNA structures in nanopores,” The Journal of Chemical Physics, vol. 154, no. 5, Art. no. 5, 2021, doi: 10.1063/5.0038530.
    5. J. M. Riede, C. Holm, S. Schmitt, and D. F. B. Haeufle, “The control effort to steer self-propelled microswimmers depends on their morphology: comparing symmetric spherical versus asymmetric              L              -shaped particles,” Royal Society Open Science, vol. 8, no. 9, Art. no. 9, Sep. 2021, doi: 10.1098/rsos.201839.
    6. M. Kuron, C. Stewart, J. de Graaf, and C. Holm, “An extensible lattice Boltzmann method for viscoelastic flows: complex and moving boundaries in Oldroyd-B fluids,” The European Physical Journal E, vol. 44, no. 1, Art. no. 1, 2021, doi: 10.1140/epje/s10189-020-00005-6.

  4. 2020

    1. J. Zeman, S. Kondrat, and C. Holm, “Bulk ionic screening lengths from extremely large-scale molecular dynamics simulations,” Chemical Communications, vol. 56, no. 100, Art. no. 100, 2020, doi: 10.1039/D0CC05023G.
    2. S. Tovey et al., “DFT accurate interatomic potential for molten NaCl from machine learning,” The Journal of Physical Chemistry C, vol. 124, no. 47, Art. no. 47, 2020, doi: 10.1021/acs.jpcc.0c08870.
    3. G. Sivaraman et al., “Machine-learned interatomic potentials by active learning: amorphous and liquid hafnium dioxide,” npj Computational Materials, vol. 6, no. 1, Art. no. 1, Jul. 2020, doi: 10.1038/s41524-020-00367-7.
    4. K. Breitsprecher et al., “How to speed up ion transport in nanopores,” Nature Communications, vol. 11, no. 1, Art. no. 1, Nov. 2020, doi: 10.1038/s41467-020-19903-6.

  5. 2019

    1. J. Zeman, C. Holm, and J. Smiatek, “The Effect of Small Organic Cosolutes on Water Structure and Dynamics,” Journal of Chemical & Engineering Data, vol. 65, no. 3, Art. no. 3, Aug. 2019, doi: 10.1021/acs.jced.9b00577.
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