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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 721 records · Page 40
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Transforming Energy Through Computational Excellence: Bringing Low Mach Number Reactive Flow Simulations at the Exascale

PeleLMeX's unique capabilities are allowing for reactive flow modeling at unprecedented scales and a reasonable time and cost. The code is currently being extended to tackle more practical, design-oriented simulations by implementing Large Eddy Simulation and data-driven chemical models, providing a fast but accurate tool for engineers considering the emergence of GPU-accelerated platforms. These extensions are critical for enabling the physical insight required to design the next generation of combustion devices as a key component of a renewable energy future.

MATHEMATICS AND COMPUTING↗
DOE OSTI2023

Biomass Feedstock Flow Modeling at Exascale

Variable biomass feedstock handling in hoppers and screw conveyors can cause upstream failures and disrupt downstream conversion operations. Understanding how particle size, shape, and moisture content affect feedstock flowability is important for efficient biorefinery operation. We present the use of our open-source discrete element method (DEM) solver for predicting flows in large scale hoppers and assess its performance on state-of-the-art high performance computing platforms that use heterogenous computing architectures.

bioenergy↗
DOE OSTI2024

Distributed Multi-GPU Community Detection on Exascale Computing Platforms

Community detection is a fundamental operation in graph mining, and by uncovering hidden structures and patterns within complex systems it helps solve fundamental problems pertaining to social networks, such as information diffusion, epidemics, and recommender systems. Scaling graph algorithms for massive networks becomes challenging on modern distributed-memory multi-GPU (Graphics Processing Unit) systems due to limitations such as irregular memory access patterns, load imbalances, higher communication-computation ratios, and cross-platform support. We present a novel algorithm HiPDPL-GPU (Distributed Parallel Louvain) to address these challenges. We conduct experiments involving different partitioning techniques to achieve an optimized performance of HiPDPL-GPU on the two largest supercomputers: Frontier and Summit. Remarkably, HiPDPL-GPU processes a graph with 4.2 billion edges in less than 3 minutes using 1024 GPUs. Qualitatively, the performance of HiPDPL-GPU is similar or better compared to other state-of-the-art CPU- and GPU-based implementations. While prior GPU implementations have predominantly employed CUDA, our first-of-its-kind implementation for community detection is cross-platform, accommodating both AMD and NVIDIA GPUs.

Sattar, Naw Safrin↗
DOE OSTI2024

Bridging the time scale in exascale computing of chemical systems (Final Technical Report)

This report summarizes the work carried out with support of the United States Department of Energy under Award DE-SC0019441. The theme of this project was to develop and apply methods that allowed for the acceleration of atomistic calculations, particularly in challenging areas such as multiphase systems, electrified interfaces, uncertainty estimation, and applications requiring chemical accuracy, which tend to be applications where simulation time is severely bottlenecked by the computational time requirements. Much of the focus was on the application of emerging machine-learning methodologies, although a wide range of methodologies were employed. This report has two major sections. The first focuses on the methodological advances themselves. Within this part, we report a number of major advances, a few examples of which are described here. We report the first machine-learning scheme for the acceleration of electronically grand-canonical calculations (that is, those applicable to electrochemistry). We report new methods of performing transfer learning, in which physics-based priors can be used to provide predictions, often with uncertainty estimates, of images well outside of training sets; we also offer ways to fine-tune these transfer-learning models. We provide a new systematic means to generate and apply minimal training data sets to very large (10,000’s of atoms) systems, with only small training sets appropriate for electronic structure. We developed new methodologies to integrate surface vibrations into surface adsorption calculations. We made advances to the applicability of diffusion Monte Carlo methods to allow (learned) force prediction, finite-size error correction, and force-free means of searching for transition states. We integrated machine-learned atomistic predictions into mechanism generation codes. Additionally, we released new software including AmpTorch, a modernized version of our original atomistic machine-learning code Amp. The second part of this report focuses on the scientific applications that accompanied, and were often enabled by, the methodological advances described earlier. A few examples follow, but full details are in the individual chapters of the report. For example, we developed a general theory of phonon-induced friction on molecular adsorbates. We showed fundamentally how solvent influences the adsorption and desorption process and how it differs from the processes typically involved at the solid–gas interface, making aqueous-phase and electrocatalysis different from traditional thermocatalysis. We examined how metal–insulator and magnetic transitions can be probed, and accelerated exciton dynamics via Frenkel Hamiltonian parameters. We showed that the nearsighted force-training approach, developed within this project, can predict both the stability and reactivity of large nanoparticles, and can also lead to insights on catalyst coverage on binding energies and entropies. These applied studies, which generally integrated with our method development, allowed us to push forward the theoretical understanding of several reaction classes.

08 HYDROGEN↗
DOE OSTI2024

Performance Improvements of Poincaré Analysis for Exascale Fusion Simulations

Understanding the time-varying magnetic field in a fusion device is critical for the successful design and construction of clean-burning fusion power plants. Poincaré analysis provides a powerful method for the visualization of magnetic fields in fusion devices. However, Poincaré plots can be very computationally expensive making it impractical, for example, to generate these plots in situ during a simulation. In this short paper, we describe a collaboration among computer science and physics researchers to develop a new Poincaré tool that provides a significant reduction in the time to generate analysis results.

Pugmire, Dave↗
DOE OSTI2024

Thermo-Fluid Modeling Framework for Supercomputer Digital Twins: Part 1, Demonstration at Exascale

A thermo-fluid modeling framework is being developed for ExaDigiT---an open-source framework for developing comprehensive digital twins of liquid-cooled supercomputers. The work is being conducted in two parts, and discussion is divided into two companion papers. The work documented in this paper focuses on the development of a cooling system library in Dymola for the Frontier supercomputer at Oak Ridge National Laboratory. The second part, outlined in a companion paper, focuses on a templating structure called Auto-CSM for easily creating model-agnostic, physics-based thermo-fluid cooling system models for liquid-cooled supercomputers using a text-based schema. The cooling model is being developed using primarily the open-source Transient Simulation Framework of Reconfigurable Models (TRANSFORM) library. The library follows the templating architecture developed within the TRANSFORM library for modeling subsystems. A full-system validation was performed to validate a very simple model that is integrated with the system controls, and the results are presented herein.

Kumar, Vineet↗