Timo Koch

Release 3.9.0 of DuMux, DUNE for Multi-{Phase, Component, Scale, Physics, ...} flow and transport in porous media. DuMux is a free and open-source simulator for flow and transport processes in and around porous media. It is based on the Distributed and Unified Numerics Environment DUNE.

Inspired by recent experimental observations of a harmonically excited elastic foil hovering near a wall while supporting substantial weight, we develop a theoretical framework that describes the underlying physical effects. Using elastohydrodynamic lubrication theory, we quantify how the dynamic deformation of the soft foil couples to the viscous fluid flow in the intervening gap. Our analysis shows that the soft foil rectifies the reversible forcing, breaking time-reversal symmetry; the spatial distribution of the forcing determines whether the sheet is attracted to or repelled from the wall. A simple scaling law predicts the time-averaged equilibrium hovering height and the maximum weight the sheet can sustain before detaching. Numerical simulations of the governing equation corroborate our theoretical predictions, are in qualitative agreement with experiments, and might explain the behavior of organisms while providing design principles for soft robotics.

We present the Gonzo dataset: Brain MRI and derivative data from one healthy male human volunteer (“Gonzo”) before and during the 72 hours after intrathecal injection of the contrast agent gadobutrol into the cerebrospinal fluid (CSF) of the spinal canal. The MRI data records include images highlighting the temporal and spatial evolution of the contrast agent in CSF, brain, and adjacent structures. In addition to raw MRI, we provide derivatives that enable numerical simulations of the transport process under study. Derivatives include T1 maps, tracer concentration maps, diffusion tensor maps, and unstructured triangulated volume meshes of the brain geometry. We also provide brain region markers obtained by image segmentation. A regional statistical analysis of the concentration data complements the image data. The presented data can be used to study the transport behavior and the underlying processes of a tracer in the brain. It is intended to contribute to and inspire new studies on the understanding of tracer transport, method development for image analysis, and simulation of brain fluid transport processes.

Version 2.10 of the Distributed and Unified Numerics Environment DUNE introduces a range of enhancements across its core and extension modules, with a continued emphasis on modern C++ integration and improved usability. This release extends support for C++20 features, particularly concepts, through comprehensive refinements in dune-common and dune-grid, enabling safer and more expressive generic programming paradigms. A notable advancement is the improved support for curved geometries, including new geometry implementations and a more flexible interface. Data structures have been modernized through native support for std::mdspan and std::mdarray, performance improvements in sparse matrices, and tools for visualization of matrix patterns. The build system has been restructured towards a modern CMake workflow, emphasizing target-based configuration and improved automation. Furthermore, new local finite elements have been introduced to broaden numerical capabilities. The release also brings updates across DUNE extensions, as well as improvements to infrastructure and module-level components.

We present a modelling framework for describing bulk fluid flow in brain tissue. Within this framework, using computational simulation, we estimate bulk flow velocities in the grey matter parenchyma due to static or slowly varying water potential gradients—hydrostatic pressure gradients and osmotic pressure gradients. Working with the situation that experimental evidence and some model parameter estimates, as we point out, are presently insufficient to estimate velocities precisely, we explore feasible parameter ranges resulting in a range of estimates. We consider the effect of realistic microvascular architecture (extracted from mouse cortical grey matter). Although the estimated velocities are small in magnitude (e.g. in comparison to blood flow velocities), the passive transport of solutes with the bulk fluid can be a relevant process when considering larger molecules transported over larger distances. We compare velocity magnitudes resulting from filtration and pulsations. Filtration can lead to continuous directed fluid flow in the parenchyma, while pulsation-driven flow is (at least partly) reversible. For the first time, we consider the effect of the vascular architecture on the velocity distribution in a tissue sample of ca 1 mm ³ cortical grey matter tissue. We conclude that both filtration and pulsations are potentially potent drivers for fluid flow.

We present a modelling framework for describing bulk fluid flow in brain tissue. Within this framework, using computational simulation, we estimate bulk flow velocities in the gray matter parenchyma due to static or slowly varying water potential gradients—hydrostatic pressure gradients and osmotic pressure gradients. Working with the situation that experimental evidence and some model parameter estimates, as we point out, are presently insufficient to estimate velocities precisely, we explore feasible parameter ranges resulting in a range of estimates. We consider the effect of realistic microvascular architecture (extracted from mouse cortical gray matter). Although the estimated velocities are small in magnitude (e.g. in comparison to blood flow velocities), the passive transport of solutes with the bulk fluid can be a relevant process when considering larger molecules transported over larger distances. We compare velocity magnitudes resulting from filtration and pulsations. Filtration can lead to continuous directed fluid flow in the parenchyma, while pulsation-driven flow is (at least partly) reversible. For the first time, we consider the effect of the vascular architecture on the velocity distribution in a tissue sample of ca. 1 mm ³ cortical gray matter tissue. We conclude that both filtration and pulsations are potentially potent drivers for fluid flow. Summary (lay abstract): Fluid transport through brain tissue is dynamic, but the basic properties of this flow and its variability are poorly characterised. Disturbed fluid transport was linked to Alzheimer’s disease. Moreover, fluid flow may be exploited to administer drugs to the brain. Using computer simulations, we estimate bulk flow velocities in the gray matter functional brain tissue due to changes in blood pressure and solute concentrations in unseen detail. We show that microvessel pulsations as filtration across the blood capillaries are potentially potent drivers of fluid flow in brain tissue.

Numerical simulations play a crucial role in various research domains including mathematics, physics, and engineering. Such simulations typically involve solving a set of model equations that describe the physical system under investigation. To find an approximate solution to these equations on a given domain geometry and with specific boundary conditions, the domain is usually discretized into a grid composed of points and cells, on which discretization schemes such as finite differences, finite volumes, or finite elements are then employed. This process yields a discrete solution defined at specific grid positions, which, depending on the scheme, can be interpolated over the entire domain using its basis functions. Due to the high computational demand of such simulations, developers often implement simulation codes in performant C++ and leverage distributed-memory parallelism through MPI, the Message Passing Interface (Clarke et al., 1994; MPI Forum, 2023), to run them on large high-performance computing systems.