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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 703 records · Page 39
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Artificial Intelligence/Deep Learning FRNN Software for Prediction & Real-Time Control of DIII-D Plasma Control System (PCS)

This collaborative project integrated an improved version of the Artificial Intelligence/Deep Learning FRNN prediction and control software into the real-time DIII-D PCS (plasma control system). A key AI/DL software challenge is to build a modern high-performance computing (HPC) enabled “synthetic plasma simulator” capable of carrying out HPC-driven real-time plasma control applications. This involves development of a deep learning framework to train the surrogate model for a first-principles-based instability analysis simulator (“SGTC”) derived from the global gyrokinetic code GTC. The role of SGTC is to provide accurate and detailed plasma instability information from a real-time AI-based simulator capability to complement the deep learning prediction and control from experimentally-measured signals, such as ECE Imaging, supplemented by synthetic SGTC-ECEI.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2018

Affordable and accurate large-scale hybrid-functional calculations on GPU-accelerated supercomputers

Performing high accuracy hybrid functional calculations for condensed matter systems containing a large number of atoms is at present computationally very demanding or even out of reach if high quality basis sets are used. We present a highly optimized multiple graphics processing unit implementation of the exact exchange operator which allows one to perform fast hybrid functional density-functional theory (DFT) calculations with systematic basis sets without additional approximations for up to a thousand atoms. With this method hybrid DFT calculations of high quality become accessible on state-of-the-art supercomputers within a time-to-solution that is of the same order of magnitude as traditional semilocal-GGA functionals. The method is implemented in a portable open-source library.

density functional theory↗
DOE OSTI2025

University Data Management Pilot Utilizing the Nuclear Research Data System

Background In 2022, the Office of Science and Technology Policy (OSTP) issued a memo that significantly reshaped the landscape of access to federally funded research. The memo mandated that all taxpayer-funded research be made available to the public without delay upon publication, without an embargo period, superseding the 2013 OSTP public access policy. This public access policy promotes transparency and the democratization of knowledge, ensuring that the fruits of scientific endeavors funded by federal agencies could be immediately accessed and built upon by scientists, educators, students, and the public at large. To implement the requirements of the OSTP guidance and DOE Public Access Plan, the Office of Nuclear Energy (NE) has implemented public access plan guidance and has identified several areas where better data management practices would further expand public access to important nuclear energy related scientific data, reports, and other technical products. Significant NE supported efforts are already underway for data management and public access to important nuclear energy related data.1 2 To address gaps in data management practices, and improve retention and accessibility of data, NE is actively exploring enhanced data management options utilizing its high-performance computing resources administered by its Nuclear Scientific User Facility Program. A newly piloted system, the Nuclear Research Data System (NRDS) acts as a portal for data collection and dissemination. Nuclear Energy University Program Research and Development Portfolio According to Web of Science, NEUP has produced 2,345 journal publication that have been cited more than 61,000 times3 and countless conference proceedings. These publications are publicly available through OSTI.gov and in the open literature. Additional scientific and technical products including project milestones that are not publications and NEUP project final reports are vetted through OSTI.gov and released once reviewed and approved by DOE. Since 2009, NEUP has awarded close to 1,000 different R&D projects in technical areas across the NE research programs. As of June 2023, 512 NEUP reports are publicly available on OSTI. The underlying data for projects is still held at universities, and data transfer, co-location, and dissemination has not occurred in a systematic way. NEUP data is currently accessible through myriad university-based data repositories, or through direct requests to PIs. The program identified this patchwork of repositories, or often lack of publicly available data, as a significant barrier to an organized, accessible, and comprehensive solution to sharing data with the larger nuclear energy community. Approach The goal of this pilot project is to establish a pathway to a consolidated long-term repository for NEUP project data. To accomplish this goal, the pilot strives to accomplish the following objectives: Establish data collection standards, including a standard set of required supplementary information to contextualize and support raw data files. Work with the HPC group collect and upload information and to modify the NRDS system, as needed, to support a standardized approach. Resolve potential barriers to successful roll out of an expanded data collection strategy, including modifying data management plan guidelines and establishing a document and data release process that accounts for potential intellectual property and/or export control concerns. Results Overall, the pilot was successful in collecting 8,982 raw and processes data files, 220 reports, 56 calibration files, and 5,931 other supplementary documents. Supplementary documents included experimental plans, methods, journal publications and conference proceedings, milestone reports, and final reports. Figure 2 shows the number of data sets and supplementary project information provided by each project. Projects has significantly different input, depending on experimental data produced and completeness of the datasets provided.

Data collection↗
DOE OSTI2020

Robust, Accurate, and Efficient: Quantum Embedding Using the Huzinaga Level-Shift Projection Operator for Complex Systems

Using wave function (WF) in density functional theory (DFT) embedding methods provides a framework for performing localized, high-accuracy WF calculations on a system, while not incurring the full computational cost of the WF calculation on the full system. To effectively partition a system into localized WF and DFT subsystems, we utilize the Huzinaga level-shift projection operator within an absolutely localized basis. In this work, we study the ability of the absolutely localized Huzinaga level-shift projection operator method to study complex WF and DFT partitions, including partitions between multiple covalent bonds, a double bond, and transition-metal–ligand bonds. We find that our methodology can accurately describe all of these complex partitions. Additionally, we study the robustness of this method with respect to the WF method, specifically where the embedded systems were described using a multiconfigurational WF method. We found that the method is systematically improvable with respect to both the number of atoms in the WF region and the size of the basis set used, with energy errors less than 1 kcal/mol. Additionally, we calculated the adsorption energy of H2 to a model of an iron metal–organic framework (Fe-MOF-74) to within 1 kcal/mol compared to CASPT2 calculations performed on the full model while incurring only a small fraction of the full computational cost. In conclusion, this work demonstrates that the absolutely localized Huzinaga level-shift projection operator method is applicable to very complex systems with difficult electronic structures.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2016

VTK-m: Accelerating the Visualization Toolkit for Massively Threaded Architectures

Execution on massively threaded processors is one of the most critical challenges for high-performance computing (HPC) scientific visualization. Of the many fundamental changes we are seeing in HPC systems, one of the most profound is a reliance on new processor types optimized for execution bandwidth over latency hiding. Moreover, our current production scientific visualization software is not designed for these new types of architectures. In order to address this issue, the VTK-m framework serves as a container for algorithms, provides flexible data representation, and simplifies the design of visualization algorithms on new and future computer architecture.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

Frontier (HPE Cray EX) Exascale Supercomputer at the Oak Ridge Leadership Computing Facility

Frontier is the HPE Cray EX exascale supercomputer deployed and operated by the Oak Ridge Leadership Computing Facility (OLCF) at Oak Ridge National Laboratory (ORNL). Frontier is designed for large-scale modeling, simulation, and AI workloads and is built from HPE Cray EX system architecture with AMD CPUs and AMD Instinct GPU accelerators connected by the HPE Slingshot interconnect. System composition (representative production configuration): Frontier is composed of approximately 74 cabinets with 128 compute nodes per cabinet (~9,400 compute nodes total). Each compute node contains one 64-core AMD EPYC CPU and four AMD Instinct MI250X GPUs. Nodes are connected using HPE Slingshot (Slingshot-200 class) networking with multiple NIC ports per node providing high injection bandwidth. Frontier is connected to the Orion parallel file system (multi-tier Lustre) providing a large, center-wide high-performance storage namespace. Operational context: Frontier entered public prominence as the first system to reach No. 1 on the TOP500 list in May 2022 (HPL benchmark), establishing the first widely recognized exascale-era performance milestone. The system supports DOE Office of Science mission workloads and enables leadership-class computational science and AI for open science users.

AMD EPYC↗
DOE OSTI2017

Processing-in-Memory Enabled Graphics Processors for 3D Rendering

The performance of 3D rendering of Graphics Processing Unit that convents 3D vector stream into 2D frame with 3D image effects significantly impact users’ gaming experience on modern computer systems. Due to the high texture throughput in 3D rendering, main memory bandwidth becomes a critical obstacle for improving the overall rendering performance. 3D stacked memory systems such as Hybrid Memory Cube (HMC) provide opportunities to significantly overcome the memory wall by directly connecting logic controllers to DRAM dies. Based on the observation that texel fetches significantly impact off-chip memory traffic, we propose two architectural designs to enable Processing-In-Memory based GPU for efficient 3D rendering.

Innovative hardware/software co-design↗
DOE OSTI2024

Evaluating integration and performance of containerized climate applications on a Hewlett Packard Enterprise Cray system

Containers have taken over large swaths of cloud computing as the most convenient way of packaging and deploying applications. The features that containers offer for packaging and deploying applications translate to high performance computing (HPC) as well. At The National Oceanic and Atmospheric Administration, containers provide an easy way to build and distribute complex HPC applications, allowing faster collaboration, portability, and experiment computer environment reproducibility amongst the scientific community. The challenge arises when applications rely on message passing interface (MPI). This necessitates investigation into how to properly run these applications with their own unique requirements and produce performance on par with native runs. We investigate the MPI performance for benchmarks and containerized climate models for various containers covering selection of compiler and MPI library combinations from the Cray provided programming environments on the Cray XC supercomputer GAEA. Performance from the benchmarks and the climate models shows that for the most part containerized applications perform on par with the natively built applications when the system optimized Cray MPICH libraries are bound into the container, and the hybrid model containers have poor performance in comparison. We also describe several challenges and our solutions in running these containers, particularly challenges with heterogeneous jobs for the containerized model runs.

Abraham, Subil↗
DOE OSTI2017

Numerical Uncertainties vs. Model Uncertainties in Two-phase Flow Simulations

In recent years, verification, validation and uncertainty quantification (VVUQ) have become a common practice in thermal-hydraulics analysis. In general, these activities deal with propagations of uncertainties in computer code simulations, e.g., through system analysis codes. However, most existing such activities in thermal-hydraulics analysis have been primarily focused on model uncertainties, while numerical uncertainties were largely overlooked. Numerical uncertainties (or numerical errors) can appear in many different forms, e.g., round-off error, statistical sampling error, and discretization error. As pointed out by Oberkampf and Roy, discretization error is usually the largest however the most difficult type of error to estimate reliably. In thermal-hydraulics analysis, especially two-phase flow simulations commonly encountered in reactor safety analysis, the lack of consideration on discretization error is mainly due to the difficulty in estimating them. Accurate estimations on discretization error require continuous mesh refinement and/or implementation of high-order numerical schemes in system analysis code, both of which are unfortunately difficult to achieve for existing codes. At first, it should be emphasized that mesh refinement (or mesh sensitivity study) is a well-understood concept in the thermal-hydraulics field. However, it is not commonly practiced for many reasons, e.g., lack of understanding on numerical errors by code users, consideration on computational cost, deteriorated numerical stability performance with refined meshes, and etc. Secondly, high-order (e.g., second-order) numerical schemes are simply unavailable in almost any existing system analysis code. Implementation of high-order numerical schemes in these codes is difficult, if not impossible, given the complexity of these codes. On the other hand, people tend to argue that numerical uncertainties (or discretization error as we have emphasized) are not as important as the commonly large uncertainties in two-phase flow models, e.g., closure correlations. As a result, nodalization is sometimes played unintentionally, or maybe intentionally, to match experimental data, which is referred as 'user effects' or 'compensating errors' in thermal-hydraulics community. Such a practice is highly criticized by Levy. In this work, we will at first build a computer code that incorporates both first-order and second-order numerical methods to solve the two-phase flow problems. The first-order method resembles the one used in many existing system analysis codes; and the second-order method works as the reference to estimate numerical errors. Numerical verifications on spatial discretization schemes will be presented in the form of mesh convergence study. It will also be demonstrated that, in practical scenarios, discretization errors can be as large as, or even larger than, model uncertainties via case studies.

42 ENGINEERING↗
DOE OSTI2025

High Performance, High Fidelity: A GPU‐Accelerated Doubly‐Periodic Configuration of the Simple Cloud‐Resolving E3SM Atmosphere Model Version 1 (DP‐SCREAMv1)

The development of the Simplified Cloud Resolving Energy Exascale Earth System Atmosphere Model (SCREAMv1) enables global storm-resolving simulations on modern GPU-based supercomputers. However, the high computational cost of SCREAMv1 limits its routine use for process-level studies, creating a need for efficient proxy configurations. This study addresses this gap by introducing DP-SCREAMv1, a doubly periodic cloud-resolving model designed to be fully consistent with SCREAMv1 while enabling high-resolution, long-duration simulations at significantly reduced computational expense by simulating a limited doubly periodic domain rather than the entire globe. Built on a C++/Kokkos architecture, DP-SCREAMv1 achieves exceptional performance scalability on GPU systems and includes a rich library of cases for validation and scientific exploration. In this work, we demonstrate short wall-clock times at SCREAMv1's default resolution and show that DP-SCREAMv1 supports routine execution of large-domain, high-resolution experiments that were previously challenging in practice. Furthermore, we show that DP-SCREAMv1 enables routine execution of “Giga-LES” style simulations and facilitates large-domain, high-resolution simulations that were recently considered burdensome to perform. These results document an efficient, fully consistent process-level configuration for SCREAMv1 (DP-SCREAMv1) and illustrate its use for long-duration and large-domain experiments at cloud-resolving to eddy-permitting resolution.

Environmental sciences↗
DOE OSTI2018

COOLR: A New System for Dynamic Thermal-Aware Computing

Extreme-scale systems need to walk a fine line between the amount of cooling they receive and the thermal-induced performance and reliability degradation they can sustain. System managers are extremely motivated to battle cooling-energy cost: the largest line item in their total operating cost. On the other hand, pushing the nodes to peak performance in tightened cooling regimes places the burden on the dynamic thermal management (DTM) to protect the hardware. DTM schemes throttle performance to relieve heat accumulation within nodes when cooling cannot mitigate the problem. These interventions prevent fatal failures but introduce inevitable performance degradations and variations. Such variations can have drastic consequences on the performance of extreme-scale systems. Furthermore, distribution of heat across different system nodes depends not only on the amount of workload. Even if a strictly equal share of computational load is assigned to all nodes, there are physical attributes and topological features of each system that inherently cause uneven accumulation of heat. These can cause one subcomponent to trigger DTM prematurely and penalize the overall system. Our goal in this project is to create a holistic thermal-aware view of the system, capturing both inherent physical attributes and dynamic system state. We developed COOLR, a dynamic system with the ability to evaluate the interplay between management of computation, data, power dissipation, and thermal state. Thereby, COOLR will achieve higher overall performance at the same energy and cooling cost. First, we performed a systematic power and thermal modeling and of high performance computing architectures. Analytical and empirical techniques were blended to generate a light-weight thermal model of target systems. Second, we developed a thermal-aware OS and runtime system. Policies for managing resources and schedules for processes has been designed to co-optimize the thermal state and performance of the system. The main outcome of our proposed thermal-aware dynamic computing system are scheduling and allocation of processes and memory accesses leading to an appropriately “skewed” distribution of activity across the system. The ultimate result of this new arrangement resulted in reduced occurrences of hotspots and reduced episodes of DTM intervention, leading to improvements in performance. The overall thermal characterization and modeling methodology (thermal instrumentation techniques, model building, and systematic model reduction) resulting from this project can be applied to systems beyond our immediate scope and will be applicable to future scaling. The thermal-aware dynamic computing paradigm will impact the efficiency of extreme-scale systems. The gained “thermal slack” can be given back to further tighten the cooling budget, benefiting the management cost of next generation extreme-scale systems.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

Uncovering I/O demands on HPC platforms: Peeking under the hood of Santos Dumont

High-Performance Computing (HPC) platforms are required to solve the most diverse large-scale scientific problems in various research areas, such as biology, chemistry, physics, and health sciences. Researchers use a multitude of scientific softwares, which have different requirements. These include input and output operations, which directly impact performance due to the existing difference in processing and data access speeds. Thus, supercomputers must efficiently handle mixed workload when storing data from the applications. Understanding the set of applications and their performance running in a supercomputer is paramount to understanding the storage system's usage, pinpointing possible bottlenecks, and guiding optimization techniques. This research proposes a methodology and visualization tool to evaluate a supercomputer's data storage infrastructure's performance, taking into account the diverse workload and demands of the system over a long period of operation. As a study case, we focus on the Santos Dumont supercomputer, identifying inefficient usage, problematic performance factors, and providing guidelines on how to tackle those issues.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2016

Towards a Scalable and Adaptive Application Support Platform for Large-Scale Distributed E-Sciences in High-Performance Network Environments

The advent of large-scale collaborative scientific applications has demonstrated the potential for broad scientific communities to pool globally distributed resources to produce unprecedented data acquisition, movement, and analysis. System resources including supercomputers, data repositories, computing facilities, network infrastructures, storage systems, and display devices have been increasingly deployed at national laboratories and academic institutes. These resources are typically shared by large communities of users over Internet or dedicated networks and hence exhibit an inherent dynamic nature in their availability, accessibility, capacity, and stability. Scientific applications using either experimental facilities or computation-based simulations with various physical, chemical, climatic, and biological models feature diverse scientific workflows as simple as linear pipelines or as complex as a directed acyclic graphs, which must be executed and supported over wide-area networks with massively distributed resources. Application users oftentimes need to manually configure their computing tasks over networks in an ad hoc manner, hence significantly limiting the productivity of scientists and constraining the utilization of resources. The success of these large-scale distributed applications requires a highly adaptive and massively scalable workflow platform that provides automated and optimized computing and networking services. This project is to design and develop a generic Scientific Workflow Automation and Management Platform (SWAMP), which contains a web-based user interface specially tailored for a target application, a set of user libraries, and several easy-to-use computing and networking toolkits for application scientists to conveniently assemble, execute, monitor, and control complex computing workflows in heterogeneous high-performance network environments. SWAMP will enable the automation and management of the entire process of scientific workflows with the convenience of a few mouse clicks while hiding the implementation and technical details from end users. Particularly, we will consider two types of applications with distinct performance requirements: data-centric and service-centric applications. For data-centric applications, the main workflow task involves large-volume data generation, catalog, storage, and movement typically from supercomputers or experimental facilities to a team of geographically distributed users; while for service-centric applications, the main focus of workflow is on data archiving, preprocessing, filtering, synthesis, visualization, and other application-specific analysis. We will conduct a comprehensive comparison of existing workflow systems and choose the best suited one with open-source code, a flexible system structure, and a large user base as the starting point for our development. Based on the chosen system, we will develop and integrate new components including a black box design of computing modules, performance monitoring and prediction, and workflow optimization and reconfiguration, which are missing from existing workflow systems. A modular design for separating specification, execution, and monitoring aspects will be adopted to establish a common generic infrastructure suited for a wide spectrum of science applications. We will further design and develop efficient workflow mapping and scheduling algorithms to optimize the workflow performance in terms of minimum end-to-end delay, maximum frame rate, and highest reliability. We will develop and demonstrate the SWAMP system in a local environment, the grid network, and the 100Gpbs Advanced Network Initiative (ANI) testbed. The demonstration will target scientific applications in climate modeling and high energy physics and the functions to be demonstrated include workflow deployment, execution, steering, and reconfiguration. Throughout the project period, we will work closely with the science communities in the fields of climate modeling and high energy physics including Spallation Neutron Source (SNS) and Large Hadron Collider (LHC) projects to mature the system for production use.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

A Perspective on Scalable AI on High-Performance Computing and Leadership Class Supercomputing Facilities [Industrial and Governmental Activities]

Many scientific applications that support the mission of the US Department of Energy (US-DoE) require modeling complex engineering and/or physical systems. Here, examples of such complex systems arise from: (a) materials science to develop new compounds with exceptional mechanical and thermodynamical properties (e.g., resistance to mechanical stresses and high temperatures), (b) structural and nuclear engineering to model the temporal evolution of the structural damage of concrete shields exposed to continuous neutron and gamma radiations emitted by the nuclear reactor core, (c) urban sciences (e.g., transportation and smart buildings), and (d) power grid systems.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2017

Thermosyphon Cooler Hybrid System for Water Savings in an Energy-Efficient HPC Data Center: Modeling and Installation: Preprint

The Thermosyphon Cooler Hybrid System (TCHS) integrates the control of a dry heat rejection device, the thermosyphon cooler (TSC), with an open cooling tower. A combination of equipment and controls, this new heat rejection system embraces the 'smart use of water,' using evaporative cooling when it is most advantageous and then saving water and modulating toward increased dry sensible cooling as system operations and ambient weather conditions permit. Innovative fan control strategies ensure the most economical balance between water savings and parasitic fan energy. The unique low-pressure-drop design of the TSC allows water to be cooled directly by the TSC evaporator without risk of bursting tubes in subfreezing ambient conditions. Johnson Controls partnered with the National Renewable Energy Laboratory (NREL) and Sandia National Laboratories to deploy the TSC as a test bed at NREL's high-performance computing (HPC) data center in the first half of 2016. Located in NREL's Energy Systems Integration Facility (ESIF), this HPC data center has achieved an annualized average power usage effectiveness rating of 1.06 or better since 2012. Warm-water liquid cooling is used to capture heat generated by computer systems direct to water; that waste heat is either reused as the primary heat source in the ESIF building or rejected using evaporative cooling. This data center is the single largest source of water and power demand on the NREL campus, using about 7,600 m3 (2.0 million gal) of water during the past year with an hourly average IT load of nearly 1 MW (3.4 million Btu/h) -- so dramatically reducing water use while continuing efficient data center operations is of significant interest. Because Sandia's climate is similar to NREL's, this new heat rejection system being deployed at NREL has gained interest at Sandia. Sandia's data centers utilize an hourly average of 8.5 MW (29 million Btu/h) and are also one of the largest consumers of water on Sandia's site. In addition to describing the installation of the TSC and its integration into the ESIF, this paper focuses on the full heat rejection system simulation program used for hourly analysis of the energy and water consumption of the complete system under varying operating scenarios. A follow-up paper will detail the test results. The evaluation of the TSC's performance at NREL will also determine a path forward at Sandia for possible deployment in a large-scale system not only for data center use but also possibly site wide.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

A parallel, distributed memory implementation of the adaptive sampling configuration interaction method

The many-body simulation of quantum systems is an active field of research that involves several different methods targeting various computing platforms. Many methods commonly employed, particularly coupled cluster methods, have been adapted to leverage the latest advances in modern high-performance computing. Selected configuration interaction (sCI) methods have seen extensive usage and development in recent years. However, the development of sCI methods targeting massively parallel resources has been explored only in a few research works. Here, we present a parallel, distributed memory implementation of the adaptive sampling configuration interaction approach (ASCI) for sCI. In particular, we will address the key concerns pertaining to the parallelization of the determinant search and selection, Hamiltonian formation, and the variational eigenvalue calculation for the ASCI method. Load balancing in the search step is achieved through the application of memory-efficient determinant constraints originally developed for the ASCI-PT2 method. The presented benchmarks demonstrate near optimal speedup for ASCI calculations of Cr 2 (24e, 30o) with 10 6 , 10 7 , and 3 × 10 8 variational determinants on up to 16 384 CPUs. Importantly, to the best of the authors’ knowledge, this is the largest variational ASCI calculation to date.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗