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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 1,639 records · Page 91
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Robust second-order approximation of the compressible Euler equations with an arbitrary equation of state

Here, this paper is concerned with the approximation of the compressible Euler equations supplemented with an arbitrary or tabulated equation of state. The proposed approximation technique is robust, formally second-order accurate in space, invariant-domain preserving, and works for every equation of state, tabulated or analytic, provided the pressure is nonnegative. An entropy surrogate functional that grows across shocks is proposed. The numerical method is verified with novel analytical solutions and then validated with several computational benchmarks seen in the literature including problems with composite waves.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2020

Application of deep neural networks to improve diagnostic accuracy of rheumatoid arthritis using diffuse optical tomography

A set of deep neural network models for rheumatoid arthritis (RA) classification using a highway network, a convolutional neural network and a residual network is proposed based on the data of diffuse optical tomography (DOT) utilising near-infrared light, which ensures early diagnosis of pathophysiological changes resulting from inflammation. A numerical model of the finger is used to generate images to overcome the inherent problem of insufficient clinical DOT images available. The proposed deep neural network models are applied to automatically classify simulated DOT images of inflamed and non-inflamed joints and transfer learning is also used to improve the performance of the classification. The results demonstrate that all three deep neural network methods improve the diagnostic accuracy as compared to the widely applied support vector machine (SVM), especially for high inter-subject variability databases. In cases of distinct modelled severity of disease, residual network achieved the highest accuracy (> 99 %), and both of highway and convolutional neural networks reached 99 %, respectively. However, as the severity of the modelled disease is reduced, this accuracy is reduced to 75.2 % for residual networks. The results indicate that transfer learning can improve the performance of deep neural network methods on RA classification from DOT data and highlight their potential as a computer aided tool in DOT diagnostic systems. (paper)

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2024

APACE: AlphaFold2 and advanced computing as a service for accelerated discovery in biophysics

The prediction of protein 3D structure from amino acid sequence is a computational grand challenge in biophysics and plays a key role in robust protein structure prediction algorithms, from drug discovery to genome interpretation. The advent of AI models, such as AlphaFold, is revolutionizing applications that depend on robust protein structure prediction algorithms. To maximize the impact, and ease the usability, of these AI tools we introduce APACE, AlphaFold2 and advanced computing as a service, a computational framework that effectively handles this AI model and its TB-size database to conduct accelerated protein structure prediction analyses in modern supercomputing environments. We deployed APACE in the Delta and Polaris supercomputers and quantified its performance for accurate protein structure predictions using four exemplar proteins: 6AWO, 6OAN, 7MEZ, and 6D6U. Using up to 300 ensembles, distributed across 200 NVIDIA A100 GPUs, we found that APACE is up to two orders of magnitude faster than off-the-self AlphaFold2 implementations, reducing time-to-solution from weeks to minutes. This computational approach may be readily linked with robotics laboratories to automate and accelerate scientific discovery.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

Examination of Semi-Analytical Solution Methods in the Coarse Operator of Parareal Algorithm for Power System Simulation

With continuing advances in high-performance parallel computing platforms, parallel algorithms have become powerful tools for development of faster than real-time power system dynamic simulations. In particular, it has been demonstrated in recent years that parallel-in-time (Parareal) algorithms have the potential to achieve such an ambitious goal. Here, the selection of a fast and reasonably accurate coarse operator of the Parareal algorithm is crucial for its effective utilization and performance. This paper examines semi-analytical solution (SAS) methods as the coarse operators of the Parareal algorithm and explores performance of the SAS methods to the standard numerical time integration methods. Two promising time-power series-based SAS methods were considered; Adomian decomposition method and Homotopy analysis method with a windowing approach for improving the convergence. Numerical performance case studies on 10-generator 39-bus system and 327-generator 2383-bus system were performed for these coarse operators over different disturbances, evaluating the number of Parareal iterations, computational time, and stability of convergence. All the coarse operators tested with different scenarios have converged to the same corresponding true solution (if they are convergent) and the SAS methods provide comparable computational speed, while having more stable convergence to the true solution in many cases.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

A Stochastic Reduced-Order Model for Statistical Microstructure Descriptors Evolution

Integrated computational materials engineering (ICME) models have been a crucial building block for modern materials development, relieving heavy reliance on experiments and significantly accelerating the materials design process. However, ICME models are also computationally expensive, particularly with respect to time integration for dynamics, which hinders the ability to study statistical ensembles and thermodynamic properties of large systems for long time scales. To alleviate the computational bottleneck, we propose to model the evolution of statistical microstructure descriptors as a continuous-time stochastic process using a non-linear Langevin equation, where the probability density function (PDF) of the statistical microstructure descriptors, which are also the quantities of interests (QoIs), is modeled by the Fokker–Planck equation. In this work, we discuss how to calibrate the drift and diffusion terms of the Fokker–Planck equation from the theoretical and computational perspectives. The calibrated Fokker–Planck equation can be used as a stochastic reduced-order model to simulate the microstructure evolution of statistical microstructure descriptors PDF. Considering statistical microstructure descriptors in the microstructure evolution as QoIs, we demonstrate our proposed methodology in three integrated computational materials engineering (ICME) models: kinetic Monte Carlo, phase field, and molecular dynamics simulations.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2020

GentenMPI: Distributed Memory Sparse Tensor Decomposition

GentenMPl is a toolkit of sparse canonical polyadic (CP) tensor decomposition algorithms that is designed to run effectively on distributed-memory high-performance computers. Its use of distributed-memory parallelism enables it to efficiently decompose tensors that are too large for a single compute node's memory. GentenMPl leverages Sandia's decades-long investment in the Trilinos solver framework for much of its parallel-computation capability. Trilinos contains numerical algorithms and linear algebra classes that have been optimized for parallel simulation of complex physical phenomena. This work applies these tools to the data science problem of sparse tensor decomposition. In this report, we describe the use of Trilinos in GentenMPl, extensions needed for sparse tensor decomposition, and implementations of the CP-ALS (CP via alternating least squares) and GCP-SGD (generalized CP via stochastic gradient descent) sparse tensor decomposition algorithms. We show that GentenMPl can decompose sparse tensors of extreme size, e.g., a 12.6-terabyte tensor on 8192 computer cores. We demonstrate that the Trilinos backbone provides good strong and weak scaling of the tensor decomposition algorithms.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2020

Machine Learning for Synchrophasor Analysis

The report presents results from the development of a cloud-based, Big Data analysis framework for power systems. The computational pipeline uses the Apache Spark framework running in an OpenStack cloud infrastructure. A real-world phasor measurement unit (PMU) dataset has been used to carry out the analysis. Several Machine Learning (ML) methods have been developed and implemented for event and anomaly detection and classification. Actual examples of power system events detection and analysis using synchrophasor data are presented. It has been shown that applications of the cloud-based computing environment and the Apache Spark framework enable a significant increase in the computational efficiency of large-scale PMU data analysis.

20 FOSSIL-FUELED POWER PLANTS↗
DOE OSTI2020

Raytracing

The raytracing (spectrally specific radiative transfer that does not couple back to the hydrodynamic evolution) code was developed because it helps improve our understanding of the physics of explosive events, validates computational physics codes, and estimates optical signals that are cheaper and safer than experiments involving high explosives. The raytracing code can also produce simulations that are useful to analyze what we can understand with sensors, such as determining what we can discriminate with sensors, like the presence of certain metals or the amount of soot. In addition, simulations can provide extra validations to computational physics to help us better understand discrepancies.

45 MILITARY TECHNOLOGY, WEAPONRY, AND NATIONAL DEF↗
DOE OSTI2021

Exploring OpenSNAPI Use Cases and Evolving Requirements [Slides]

Emerging system architectures are rapidly transforming in order to meet shifting requirements. Motivated by expanding data volumes, energy efficiency concerns, and the omnipresent need to improve performance, architectures are increasingly adopting a data-centric approach. At the core of this concept is the goal of minimizing data motion and instead processing data in-situ to the greatest degree possible. Therefore, data-centric designs, in contrast to conventional CPU-centric models, typically distribute compute capabilities throughout the architecture. As part of this paradigm shift, a novel class of devices known as data processing units (DPUs), alongside CPUs and GPUs, are quickly forming a third pillar of data-centric systems. These devices, which include smart network adapters and switches, seek to offload computation on data at the network edge as well as in-flight within the network fabric. The Open Smart Network API (OpenSNAPI) project seeks to develop a unified API for DPU devices. In our previous talks, we introduced the OpenSNAPI project and detailed our investigations regarding the viability of offloading compute intensive kernels to BlueField DPUs. In contrast, in this talk we detail our efforts to offload application-level file I/O to the DPU. We also discuss plans and early efforts to explore in-network compute capabilities. Finally, we describe our observations with respect to the evolving design of OpenSNAPI.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

User Manual - HydraGNN: Distributed PyTorch Implementation of Multi-Headed Graph Convolutional Neural Networks

This document serves as user manual for HydraGNN, a scalable graph neural network (GNN) architecture that allows for a simultaneous prediction of multiple target properties using multi-task learning (MTL). The HydraGNN architecture is constructed by successive superposition of three different sets of layers. The first set is made of message-passing layers to exchange information across nodes in the graph and use this to update the nodal features. The second set is made of global pooling layers that aggregate information from all the nodes in the graph and map it into a scalar, and is needed only for global target properties that are related to the entire graph. The third set of layers is dedicated to the implementation of MTL, which is enabled by forking of the architecture into separate heads, each one of them dedicated to the predictive task of one specific target property. Through an object-oriented programming paradigm, HydraGNN is templated over different message-passing policies, which allows for a user-friendly hyperparameter study to assess the sensitivity of the predictive performance of the HydraGNN architecture on a specific dataset with respect to the choice of the message-passing policy. The object-oriented paradigm used by HydraGNN also allows for a user-friendly inclusion of newly developed message passing policies within the existing framework. HydraGNN supports distributed computing capabilities for scalable data reading and scalable training on leadership-class supercomputers.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Light Output Fitting Software

Light output response of scintillators is crucial to the utilization of organic scintillators as effective tools in radiation detection and measurement. While the response is a continuous distribution, the light output corresponding to the maximum energy deposition is crucial in effectively understanding and simulating a detector. There are a variety of fits derived in literature that will vary for every detector material. The Light Output Response Fitter, or LORF Program is a python script designed to easily and quickly compute and plot fits for a variety of scintillator light output models. It includes a stopping power library constructed from SRIM including Organic Glass, EJ309, EJ301, Stilbene, EJ276, and their deuterated counterparts by default, with the ability for the user to add custom stopping power libraries. The user is also capable of importing the python package and utilizing its in-built functions as appropriate. Uses for this capability include plotting and computing a model with known parameters.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2025

Parallel Programming in MCNP6

Monte Carlo N-Particle (MCNP)1 is a general-purpose Monte Carlo particle transport code developed by Los Alamos National Laboratory (LANL). To efficiently handle long simulations, MCNP version 6 (MCNP6) supports parallel execution using two primary programming models: • Shared-memory task-based threading using OpenMP (Open Multi-Processing), and • Distributed-memory calculations using MPI (Message Passing Interface). The OpenMP and MPI programming models enable MCNP6 to scale from desktop systems to high-performance computing (HPC) clusters, allowing users to run MCNP in one of three parallel modes: • OpenMP-only, • MPI-only, and • Hybrid (MPI + OpenMP). The choice of parallelization mode depends on the underlying computer architecture and the characteristics of the simulation problem.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

High-Fidelity Simulations of Supercritical CO2 Based Oxy-Combustion with Non-Ideal Equation of State

The supercritical carbon dioxide (sCO2) Allam-Fetvedt cycle employs high-temperature oxy-combustion using sCO2 as the working fluid, leading to a unique and challenging environment for materials. In this cycle, pressurized gaseous fuel is combusted in the presence of a hot oxidant flow containing a mixture of CO2 and oxygen and a hot CO2 diluent recycle stream at approximately 300 bar under lean combustion conditions. We present high-fidelity reacting simulations of this oxy-combustor environment and compare the results when performing simulations with ideal and non-ideal equations of state. By doing so, we identify if the combustor behavior at the regime of interest is sensitive enough to the equation of state to justify the added computational cost incurred with a non-ideal equation of state.

equation of state↗
DOE OSTI2023

Verification and Performance Impact of the New Parallel MCNP6.3 Particle Track Output Capability for Subcritical Multiplication Simulations

The MCNP6® code, version 6.3, has several new features that are intended to ultimately replace legacy features that are now marked for deprecation. One of these features is the new particle track output (PTRAC) format and capability, where the legacy PTRAC capability still exists alongside the modern PTRAC capability in MCNP6.3. While the MCNP6.3 code has been extensively verified and validated for many applications, the PTRAC feature is not exercised in any of the typical verification and validation (V&V) applications studied during the course of a typical MCNP code release. The primary goal of this paper is to verify that the legacy and modern PTRAC feature produces equivalent results for subcritical multiplication benchmarks previously studied. In the process of verifying that the simulated benchmark results are equivalent, the computational performance is compared between the legacy and modern PTRAC uses. In addition to verification of the update, which is important to the community as a whole, this effort also supports advances in the simulation of recent subcritical neutron noise measurements that require higher computational effort per second of real-time measurement than that of systems typically measured.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Coupled Neutronics - Thermal-Hydraulics - Thermomechanics modeling of Molten Chloride Reactor

Molten Salt Reactors (MSRs) are innovative Generation-IV nuclear reactors known for their enhanced safety features, fuel economy, and fuel cycle advancements. However, designing and studying these reactors present unique safety and modeling challenges. The Department of Energy (DOE)'s Nuclear Energy Advanced Modeling and Simulation program addresses these challenges through the development of flexible multi-fidelity, multiphysics simulation tools. This study exemplifies NEAMS tools' capabilities by modeling a chloride-based micro-MSR: Griffin for neutronics simulations, Pronghorn for thermal-hydraulics simulations, and BISON for thermomechanics simulations. These tools are integrated into a comprehensive 3D multiphysics model, successfully capturing steady-state and transient reactor operations. A loss of flow accident is examined as an illustrative example of transient behavior. Additionally, this paper contributes to demonstrating the theoretical feasibility of micro-MSR concepts, which have received less attention compared to other designs. The subsequent sections provide a brief description of the MSR concept studied, an overview of the computational tools used, and present results for both steady-state operation and a loss of flow accident.

42 - ENGINEERING↗