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Small shallow cumulus clouds (<1 km) over the tropical oceans appear to possess the ability to self‐organize into mesoscale (10–100 km) patterns. To better understand the processes leading to such self‐organized convection, we present Cloud Botany, an ensemble of 103 large‐eddy simulations on domains of 150 km, produced by the Dutch Atmospheric Large Eddy Simulation model on supercomputer Fugaku. Each simulation is run in an idealized, fixed, larger‐scale environment, controlled by six free parameters. We vary these over characteristic ranges for the winter trades, including parameter combinations observed during the EUREC4A (Elucidating the role of clouds–circulation coupling in climate) field campaign. In contrast to simulation setups striving for maximum realism, Cloud Botany provides a platform for studying idealized, and therefore more clearly interpretable causal relationships between conditions in the larger‐scale environment and patterns in mesoscale, self‐organized shallow convection. We find that any simulation that supports cumulus clouds eventually develops mesoscale patterns in their cloud fields. We also find a rich variety in these patterns as our control parameters change, including cold pools lined by cloudy arcs, bands of cross‐wind clouds and aggregated patches, sometimes topped by thin anvils. Many of these features are similar to cloud patterns found in nature. The published data set consists of raw simulation output on full 3D grids and 2D cross‐sections, as well as post‐processed quantities aggregated over the vertical (2D), horizontal (1D) and all spatial dimensions (time‐series). The data set is directly accessible from Python through the use of the EUREC4A intake catalog. Plain Language Summary The organization of shallow cumulus clouds over the tropical ocean has recently received a lot of attention. This type of organization is potentially important for how the clouds are affected by a changing climate and also for how they modulate further warming. We present a collection of 103 detailed simulations of shallow cumulus clouds in idealized atmospheric environments. These environments are described by six parameters, and our collection is formed by systematically simulating different parameter combinations. This way an ensemble is created that spans up a multidimensional phase space of environmental conditions typical for the wintertime subtropical Atlantic Ocean. This approach allows us to form a picture of how the environmental conditions relate to the cloud organization that develops in the simulations. At a glance, most simulations evolve similarly: They quickly form small cumulus clouds, which then grow in size and organize into patterns. Often this leads to rainfall, which then causes further heterogeneity and pattern formation. The data is openly available online, and will serve future studies of cumulus clouds, their organization, and how they interact with the climate. Key Points We present Cloud Botany, an ensemble of idealized large‐eddy simulations of the winter trade wind regions, controlled by six varied parameters The parameter ranges are chosen to match the climatology of the trade wind region The simulations show a variety of cloud organization patterns: small cumulus, stripes, cold pools, cloud arcs, and anvils

In-silico cellular models of blood are invaluable to gain understanding about the many interesting properties that blood exhibits. However, numerical investigations that focus on the effects of cytoplasmic viscosity in these models are not very prevalent. We present a parallelised method to implement cytoplasmic viscosity for HemoCell, an open-source cellular model based on immersed boundary lattice Boltzmann methods, using an efficient ray-casting algorithm. The effects of the implementation are investigated with single-cell simulations focusing on the deformation in shear flow, the migration due to wall induced lift forces, the characteristic response time in periodic stretching and pair collisions between red blood cells and platelets. Collective transport phenomena are also investigated in many-cell simulations in a pressure driven channel flow. The simulations indicate that the addition of a viscosity contrast between internal and external fluids significantly affects the deformability of a red blood cell, which is most pronounced during very short time-scale events. Therefore, modelling the cytoplasmic viscosity contrast is important in scenarios with high velocity deformation, typically high shear rate flows.

Projecting the anthropogenic mass loss of the Greenland and Antarctic ice sheets requires models that can accurately describe the physics of flowing ice and its interactions with the atmosphere, the ocean, and the solid Earth. As the uncertainty in many of these processes can only be explored by running large numbers of simulations to sample the phase space of possible physical parameters, the computational efficiency and user-friendliness of such a model are just as relevant to its applicability as is its physical accuracy. Here, we present and verify version 2.0 of the Utrecht Finite Volume Ice-Sheet Model (UFEMISM). UFEMISM is a state-of-the-art finite-volume model that applies an adaptive grid in both space and time. Since the first version published 2 years ago, v2.0 has added more accurate approximations to the Stokes flow, more sliding laws, different schemes for calculating the ice thickness rates of change, a more numerically stable time-stepping scheme, more flexible and powerful mesh generation code, and a more generally applicable discretisation scheme. The parallelisation scheme has changed from a shared-memory architecture to distributed memory, enabling the user to utilise more computational resources. The version control system (git) includes automated unit tests and benchmark experiments to aid with model development, as well as automated installation of the required libraries, improving both user comfort and reproducibility of results. The input/output (I/O) now follows the NetCDF-4 standard, including automated remapping between regular grids and irregular meshes, reducing user workload for pre- and post-processing. These additions and improvements make UFEMISM v2.0 a powerful, flexible ice-sheet model that can be used for long palaeoglaciological applications, as well as large ensemble simulations for future projections of ice-sheet retreat, and that is ready to be used for coupling within Earth system models.

One of the routine clinical treatments to eliminate ischemic stroke thrombi is injecting a biochemical product into the patient’s bloodstream, which breaks down the thrombi’s fibrin fibers: intravenous or intravascular thrombolysis. However, this procedure is not without risk for the patient; the worst circumstances can cause a brain hemorrhage or embolism that can be fatal. Improvement in patient management drastically reduced these risks, and patients who benefited from thrombolysis soon after the onset of the stroke have a significantly better 3-month prognosis, but treatment success is highly variable. The causes of this variability remain unclear, and it is likely that some fundamental aspects still require thorough investigations. For that reason, we conducted in vitro flow-driven fibrinolysis experiments to study pure fibrin thrombi breakdown in controlled conditions and observed that the lysis front evolved non-linearly in time. To understand these results, we developed an analytical 1D lysis model in which the thrombus is considered a porous medium. The lytic cascade is reduced to a second-order reaction involving fibrin and a surrogate pro-fibrinolytic agent. The model was able to reproduce the observed lysis evolution under the assumptions of constant fluid velocity and lysis occurring only at the front. For adding complexity, such as clot heterogeneity or complex flow conditions, we propose a 3-dimensional mesoscopic numerical model of blood flow and fibrinolysis, which validates the analytical model’s results. Such a numerical model could help us better understand the spatial evolution of the thrombi breakdown, extract the most relevant physiological parameters to lysis efficiency, and possibly explain the failure of the clinical treatment. These findings suggest that even though real-world fibrinolysis is a complex biological process, a simplified model can recover the main features of lysis evolution.

There is a discrepancy between successful recanalization and good clinical outcome after endovascular treatment (EVT) in acute ischemic stroke patients. During removal of a thrombus, a shower of microemboli may release and lodge to the distal circulation. The objective of this study was to determine the extent of damage on brain tissue caused by microemboli. In a rat model of microembolization, a mixture of microsphere (MS) sizes (15, 25 and 50 µm diameter) was injected via the left internal carotid artery. A 3D image of the left hemisphere was reconstructed and a point-pattern spatial analysis was applied based on G- and K-functions to unravel the spatial correlation between MS and the induced hypoxia or infarction. We show a spatial correlation between MS and hypoxia or infarction spreading up to a distance of 1000–1500 µm. These results imply that microemboli, which individually may not always be harmful, can interact and result in local areas of hypoxia or even infarction when lodged in large numbers.

Large scale validation and uncertainty quantification are essential in the experimental design, control, and operations of fusion reactors. Reduced models and increasing computational power means that it is possible to run many simulations, yet setting up simulation runs remaining a time-consuming and error-prone process that involves many manual steps. duqtools is an open-source workflow tool written in Python for that addresses this bottleneck by automating the set up of new simulations. This enables uncertainty quantification and large scale validation of fusion energy modelling simulations. In this work, we demonstrate how duqtools can be used to set up and launch 2000 different simulations of plasma experiments to validate aspects of the JINTRAC modelling suite. With this large-scale validation we identified issues in preserving data consistency in model initialization of the current (\\(I(p)\\)) distribution. Furthermore, we used duqtools for sensitivity analysis on the QLKNN-jetexp-15D surrogate model to verify its correctness in multiple regimes.

Large scale validation and uncertainty quantification are essential in the experimental design, control, and operations of fusion reactors. Reduced models and increasing computational power means that it is possible to run many simulations, yet setting up simulation runs remaining a time-consuming and error-prone process that involves many manual steps. duqtools is an open-source workflow tool written in Python for that addresses this bottleneck by automating the set up of new simulations. This enables uncertainty quantification and large scale validation of fusion energy modelling simulations. In this work, we demonstrate how duqtools can be used to set up and launch 2000 different simulations of plasma experiments to validate aspects of the JINTRAC modelling suite. With this large-scale validation we identified issues in preserving data consistency in model initialization of the current (\\(I(p)\\)) distribution. Furthermore, we used duqtools for sensitivity analysis on the QLKNN-jetexp-15D surrogate model to verify its correctness in multiple regimes.