The gas in galaxy clusters is heated by shock compression through accretion (outer shocks) and mergers (inner shocks). These processes additionally produce turbulence. To analyse the relation between the thermal and turbulent energies of the gas under the influence of non-adiabatic processes, we performed numerical simulations of cosmic structure formation in a box of 152 Mpc comoving size with radiative cooling, UV background, and a subgrid scale model for numerically unresolved turbulence. By smoothing the gas velocities with an adaptive Kalman filter, we are able to estimate bulk flows toward cluster cores. This enables us to infer the velocity dispersion associated with the turbulent fluctuation relative to the bulk flow. For halos with masses above $10^{13}\,M_\odot$, we find that the turbulent velocity dispersions averaged over the warm-hot intergalactic medium (WHIM) and the intracluster medium (ICM) are approximately given by powers of the mean gas temperatures with exponents around 0.5, corresponding to a roughly linear relation between turbulent and thermal energies and transonic Mach numbers. However, turbulence is only weakly correlated with the halo mass. Since the power-law relation is stiffer for the WHIM, the turbulent Mach number tends to increase with the mean temperature of the WHIM. This can be attributed to enhanced turbulence production relative to dissipation in particularly hot and turbulent clusters.
Major mergers are considered to be a significant source of turbulence in clusters. We performed a numerical simulation of a major merger event using nested-grid initial conditions, adaptive mesh refinement, radiative cooling of primordial gas, and a homogeneous ultraviolet background. By calculating the microscopic viscosity on the basis of various theoretical assumptions and estimating the Kolmogorov length from the turbulent dissipation rate computed with a subgrid-scale model, we are able to demonstrate that most of the warm-hot intergalactic medium can sustain a fully turbulent state only if the magnetic suppression of the viscosity is considerable. Accepting this as premise, it turns out that ratios of turbulent and thermal quantities change only little in the course of the merger. This confirms the tight correlations between the mean thermal and non-thermal energy content for large samples of clusters in earlier studies, which can be interpreted as second self-similarity on top of the self-similarity for different halo masses. Another long-standing question is how and to which extent turbulence contributes to the support of the gas against gravity. From a global perspective, the ratio of turbulent and thermal pressures is significant for the clusters in our simulation. On the other hand, a local measure is provided by the compression rate, i.e. the growth rate of the divergence of the flow. Particularly for the intracluster medium, we find that the dominant contribution against gravity comes from thermal pressure, while compressible turbulence effectively counteracts the support. For this reason it appears to be too simplistic to consider turbulence merely as an effective enhancement of thermal energy.
Abstract. Classical numerical models for the global atmosphere, as used for numerical weather forecasting or climate research, have been developed for conventional central processing unit (CPU) architectures. This hinders the employment of such models on current top-performing supercomputers, which achieve their computing power with hybrid architectures, mostly using graphics processing units (GPUs). Thus also scientific applications of such models are restricted to the lesser computer power of CPUs. Here we present the development of a GPU-enabled version of the ICON atmosphere model (ICON-A), motivated by a research project on the quasi-biennial oscillation (QBO), a global-scale wind oscillation in the equatorial stratosphere that depends on a broad spectrum of atmospheric waves, which originates from tropical deep convection. Resolving the relevant scales, from a few kilometers to the size of the globe, is a formidable computational problem, which can only be realized now on top-performing supercomputers. This motivated porting ICON-A, in the specific configuration needed for the research project, in a first step to the GPU architecture of the Piz Daint computer at the Swiss National Supercomputing Centre and in a second step to the JUWELS Booster computer at the Forschungszentrum Jülich. On Piz Daint, the ported code achieves a single-node GPU vs. CPU speedup factor of 6.4 and allows for global experiments at a horizontal resolution of 5 km on 1024 computing nodes with 1 GPU per node with a turnover of 48 simulated days per day. On JUWELS Booster, the more modern hardware in combination with an upgraded code base allows for simulations at the same resolution on 128 computing nodes with 4 GPUs per node and a turnover of 133 simulated days per day. Additionally, the code still remains functional on CPUs, as is demonstrated by additional experiments on the Levante compute system at the German Climate Computing Center. While the application shows good weak scaling over the tested 16-fold increase in grid size and node count, making also higher resolved global simulations possible, the strong scaling on GPUs is relatively poor, which limits the options to increase turnover with more nodes. Initial experiments demonstrate that the ICON-A model can simulate downward-propagating QBO jets, which are driven by wave–mean flow interaction.
One of the major disadvantages of common Fluorescent Immunoassays is the Fluorophore per Protein ratio. In the normal settings several fluorophores such as FITC are covalently linked to one specific antibody. The problem which occurs with an increased number of fluorescent molecules per binding event is an appearing quenching effect and with this a decrease of fluorescence and signal. To overcome this weakness of Fluorescent Immunoassays, different types of organic fluorescent nanoparticles were fabricated with an easy one step top down method, using cavitation forces for breaking down larger fluorophore powder. After the fabrication and well characterization, two of the chosen fluorophores were aggregation induce emissive: TPEDH and novel scaffold TPEA. The other non-direct fluorophore...[ Read more ]
Abstract. The Next Generation of Earth Modeling Systems (nextGEMS) project aimed to produce multi-decadal climate simulations, for the first time, with resolved kilometer-scale (km-scale) processes in the ocean, land, and atmosphere. In only three years, nextGEMS achieved this milestone with the two km-scale Earth system models, ICOsahedral Non-hydrostatic model (ICON) and Integrated Forecasting System coupled to the Finite-volumE Sea ice-Ocean Model (IFS-FESOM). nextGEMS was based on three cornerstones: 1) developing km-scale Earth system models with small errors in the energy and water balance, 2) performing km-scale climate simulations with a throughput greater than one simulated year per day, and 3) facilitating new workflows for an efficient analysis of the large simulations with common data structures and output variables. These cornerstones shaped the timeline of nextGEMS, divided into four cycles. Each cycle marked the release of a new configuration of ICON and IFS-FESOM, which were evaluated at hackathons. The participants in hackathons included experts from climate science, software engineering, and high-performance computing, as well as users from the energy and agricultural sectors. The continuous efforts over the four cycles allowed us to produce 30-year simulations of ICON and IFS-FESOM, spanning the period 2020–2049 under the SSP3-7.0 scenario. The throughput was about 500 simulated days per day on the Levante supercomputer of the German Climate Computing Center (DKRZ). The simulations employed a horizontal grid of about 5 km resolution in the ocean and 10 km resolution in the atmosphere and land. Aside from this technical achievement, the simulations allowed us to gain new insights into the realism of ICON and IFS-FESOM. Beyond its timeframe, nextGEMS builds the foundation of the Climate Change Adaptation Digital Twin developed in the Destination Earth initiative and paves the way for future European research on climate change.
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