This team utilizes large-scale computational tools to help understand how adequately reduced-order formulations can capture the strong coupling between the fluid mechanics of the gas flow and the transport properties of the high-temperature gas.

The approach for this INCITE project is to characterize the effects of pressure gradient and wall cooling on boundary-layer turbulence and perform a thorough evaluation of the existing turbulence models as well as the models that are currently under development.

Coupling machine learning and physics-based methods, with this work researchers aim to accelerate the slow process of drug discovery, which typically lasts many years and costs billions of dollars—a major weakness in public health emergencies.

With this new INCITE project, this team will conduct not only a full suite of 3D simulations for the spectrum of progenitor stars, but plan to double this long-term effort because of code speed-ups and improvements.

With this INCITE project, the team involved has enhanced the particle-in-cell code OSIRIS to launch lasers with arbitrary spatial-temporal profiles.

To advance the design of low enriched uranium fuel elements for future nuclear reactors, researchers are performing high-fidelity simulations of turbulent flows to provide improved engineering predictions and thus more accurate thermal hydraulic safety analysis.

An understanding of the effects of climate change on extreme weather and atmospheric hazards is essential to ascertain future socioeconomic and infrastructural impacts from these events. The awarded effort from this project will produce the world’s first high-resolution “medium” ensemble from a single global modeling system, using regional refinement in the Department of Energy’s recently released Energy Exascale Earth System Model (E3SM) version 2.

In this project, a team of researchers at Argonne National Laboratory plan to use high-fidelity computational fluid dynamics (CFD) and multiphysics simulations to investigate fundamental flow phenomena in next-generation nuclear power reactors.

This team's research will pursue a multi-faceted strategy aimed at modeling short-and-long-range dynamics of nuclei providing reliable estimates of the associated theoretical uncertainty.

This team from Cornell University utilized ALCF supercomputing resources to research innovations that will enable advancements to U.S. wind systems, reduce the cost of electricity, and accelerate the deployment of wind power.

As part of the US Department of Energy (DOE) National Nuclear Security Administration’s (NNSA) initiative to reduce the enrichment of research and test reactors, this research project sets out to investigate the conversion of HFIR from a high enriched uranium (HEU) core to a low enriched uranium (LEU) core.

TAE Technologies combines accelerator physics and plasma physics to solve the challenge of fusion. As part of an ongoing investigation, this team will utilize ALCF HPC resources to conduct first principles particle-in-cell (PIC) simulations to develop an understanding of this newly identified and highly impactful regime

Despite large increases in the uptake of screening in the past two decades, colorectal cancer (CRC) is still the second leading cause of cancer death in the US. This points to inadequate screening and treatment, and gaps in care that need to be addressed. This project will use leadership-class computing resources to run comparative probabilistic sensitivity analyses (PSAs) of screening strategies with three state-of-the-art CRC models.

The extreme-scale MD simulations from this project will deliver key information on physical properties of amorphous carbon including phase diagram, shock Hugoniot and the equation of state, the critical elements for the design of successful ICF ignition.

To interpret DESI scheduled for the summer of 2023, this team will produce a set of high-resolution hydrodynamical simulations which are needed to accurately compute the large-scale clustering of the intergalactic medium within the cosmic web while capturing small-scale effects due to pressure broadening of gas or a finite mass of dark matter particles leading to free-streaming, at sufficient precision.

The main objective of this research project is to develop a data-driven operator level surrogate model (DeepONet) to take the place of atomic scale modeling in a time-dependent multiscale system. As noted by DeepMind researcher Irina Higgins, “Once DeepONet is trained, it can be applied to new input functions, thus producing new results substantially faster than numerical solvers.”

There is an increasing worldwide demand for high energy density batteries. This research is aimed at using large-scale, high performance computing to assist the discovery of novel battery electrolytes.

In this project, supercomputers at Oak Ridge Leadership Computing Facility, Argonne Leadership Computing Facility, and the National Energy Research Scientific Computing Center will be used to refine the theoretical prediction based on a technique known as lattice Quantum Chromodynamics (QCD).

In this project, the team aims to develop and leverage cutting-edge ab initio computational methods to investigate the temperature-dependent optical and transport properties of compound semiconductors, perovskites, and plasmonic ceramics for uses in power devices, solar cells, and light-emitting diodes.

Astrophysical collisionless shocks are among the most powerful particle accelerators in the Universe. The objectives of this research are to perform large-scale first-principles simulations of magnetized collisionless shocks to address important longstanding questions associated with energy partition and particle acceleration.