Related papers: The world's largest turbulence simulations
FEARLESS (Fluid mEchanics with Adaptively Refined Large Eddy SimulationS) is a new numerical scheme arising from the combined use of subgrid scale (SGS) model for turbulence at the unresolved length scales and adaptive mesh refinement (AMR)…
Three-dimensional turbulence is usually studied experimentally by using a spatially localized forcing at large scales (e.g. via rotating blades or oscillating grids), often in a deterministic way. Here, we report an original technique where…
A novel unitary quantum lattice gas algorithm is used to simulate quantum turbulence of a BEC described by the Gross-Pitaevskii equation on grids up to 5760^3. For the first time, an accurate power law scaling for the quantum Kelvin wave…
Cluster media are dynamical, not static; observational evidence suggests they are turbulent. High-resolution simulations of the intracluster media (ICMs) and of idealized, similar media help us understand the complex physics and…
During the last two years the RealityGrid project has allowed us to be one of the few scientific groups involved in the development of computational grids. Since smoothly working production grids are not yet available, we have been able to…
We have designed a simple multi-scale method that identifies turbulent motions in hydrodynamical grid simulations. The method does not assmume ant a-priori coherence scale to distinguish laminar and turbulent flows. Instead, the local mean…
Turbulence is indispensable to redistribute nutrients for all life forms larger than microbial, on land and in the ocean. Yet, the development of deep-sea turbulence was not studied in three dimensions to date. As a disproportionate…
Horizon Run 5 (HR5) is a cosmological hydrodynamical simulation which captures the properties of the Universe on a Gpc scale while achieving a resolution of 1kpc. Inside the simulation box we zoom-in on a high-resolution cuboid region with…
(Abridged) We present a series of decaying turbulence simulations that represent a cluster-forming clump within a molecular cloud, investigating the role of magnetic fields on the formation of potential star-forming cores. We present an…
Turbulent fluid flows are among the most computationally demanding problems in science, requiring enormous computational resources that become prohibitive at high flow speeds. Physics-informed neural networks (PINNs) represent a radically…
Deep learning provides a versatile suite of methods for extracting structured information from complex datasets, enabling deeper understanding of underlying fluid dynamic phenomena. The field of turbulence modeling, in particular, benefits…
The development of turbulence closure models, parametrizing the influence of small non-resolved scales on the dynamics of large resolved ones, is an outstanding theoretical challenge with vast applicative relevance. We present a closure,…
We explore the suitability of deep learning to capture the physics of subgrid-scale ideal magnetohydrodynamics turbulence of 2-D simulations of the magnetized Kelvin-Helmholtz instability. We produce simulations at different resolutions to…
We present results from our cosmological N-body simulation which consisted of 2048x2048x2048 particles and ran distributed across three supercomputers throughout Europe. The run, which was performed as the concluding phase of the…
Material properties are fundamentally dictated by multiscale phenomena, which often reach mesoscale in size. The {\mu}m mesoscale is also the size which can be observed directly under an optical microscope, bridging the atomistic…
Hydrodynamic simulations have become irreplaceable in modern cosmology for exploring complex systems and making predictions to steer future observations. In Chapter 1, we begin with a philosophical discussion on the role of simulations in…
We present an optimized implementation of the recently proposed information geometric regularization (IGR) for unprecedented scale simulation of compressible fluid flows applied to multi-engine spacecraft boosters. We improve upon…
Turbulence is a key process in many fields of astrophysics. Advances in numerical simulations of fluids over the last several decades have revolutionized our understanding of turbulence and related processes such as star formation and…
Modern computing clusters offer specialized hardware for reduced-precision arithmetic that can speed up the time to solution significantly. This is possible due to a decrease in data movement, as well as the ability to perform arithmetic…
Turbulence has strong and seemingly random fluctuations. Assessing its repeatability is key to predicting flows in technology and nature, much of which decay as viscosity dissipates energy. Much has been done to this end since the work of…