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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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All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Corral Summit Pumped Storage Hydropower Hybrid Site Suitability Assessment (Rev.1)

In 2024, Idaho National Laboratory (INL) and Pacific Northwest National Laboratory (PNNL) initiated a technical-assistance project to support Cat Creek Energy, LLC, (CCE) in evaluating site suitability for the proposed Corral Summit Pumped Storage Hydropower (PSH) project in south-central Idaho near Mackay Reservoir. The Corral Summit facility incorporates battery storage and photovoltaic (PV) solar arrays in a Trybrid configuration to deliver large-volume long-duration (LVLD) storage solutions for rural electric cooperatives in eastern Idaho. The evaluation process focused on determining the most-suitable location for the upper reservoir of the PSH system, guided by a comprehensive assessment framework spanning multiple categories, including physical characteristics, environmental constraints, building infrastructure, regulatory constraints, cultural resources and sensitivity, social factors, and power market and grid integration. Each site was analyzed based on a ranking scale (0–1), which scores ranging from “severely disfavored” to “highly favored,” allowing detailed comparisons of site-specific conditions. Categories such as hydraulic head, utilities corridor, land ownership, and transmission-grid limitations emerged as key contributors to the overall assessment. Site 2 (Idaho Trust) demonstrated a slight advantage over Site 1 (Bureau of Land Management) primarily due to favorable outcomes in regulatory constraints, building infrastructure, and power-market integration. However, Site 1 outperformed Site 2 in factors related to physical characteristics and social factors. The report emphasizes the need for further evaluation of both sites before clear determination of which site is preferred, due to the limited information available on either site at the time of this report. Key areas of evaluation to clearly define the preferred site are ecological impacts, cultural-resource surveys, and economic-feasibility assessments. For successful project execution, recommended follow-up actions include seismic and geotechnical surveys, groundwater and habitat monitoring, regulatory reviews of water rights and right-of-way agreements, cultural engagement with local tribal governments, and enhanced stakeholder strategies. These efforts will ensure the Corral Summit Trybrid facility meets local energy needs while balancing environmental, social, and regulatory responsibilities.

13 - HYDRO ENERGY↗
NASA NTRS1982

Late-stage summit activity of Martian shield volcanoes

The preservation of morphologically fresh lava flows which pre-date the most recent episodes of caldera collapse at the summits of Ascraeus, Arsia and Olympus Montes indicates that explosive eruptions were not associated with this stage of Tharsis shield volcanism. The existence of resurfaced floor segments, complex wrinkle ridges, and lava terraces within the summit craters suggests that lava lakes comprised the dominant form of the intra-caldera activity. Multiple collapse episodes on Ascraeus and Olympus Montes are indicated by the nested summit craters. The most plausible cause of caldera collapse appears to be large-scale sub-terminal effusive activity, which is corroborated by the previously recognized existence of large lava flows on the flanks of these volcanoes. Due to the implied sequence of large-scale explosive (silicic) volcanism followed by effusive (basaltic) activity, it appears highly unlikely that ignimbrites or other forms of pyroclastic flows (previously proposed as possible deposits within the Olympus Mons aureole material) were ever erupted from the Tharsis Montes.

Mouginis-Mark, P. J.↗
NASA NTRS2009

Winter Camp: A Blog from the Greenland Summit, Part II

An earlier issue presents the first half of the author's experience living and working at the National Science Foundation's (NSF) Greenland Summit Camp. The author is a remote-sensing glaciologist at NASA s Goddard Space Flight Center. She took measurements that will be used to validate data collected by NASA s Aqua, Terra, and Ice, Clouds, and land Elevation Satellite (ICESat) satellites with ground-truth measurements of the Greenland Ice Sheet she made at Summit Camp from November 2008-February 2009. This article presents excerpts from the second half of her stay and work at the Greenland Summit.

Koenig, Lora↗
NASA NTRS2009

Winter Camp: A Blog from the Greenland Summit

This article presents the first half of the author's experience living and working at the National Science Foundation's (NSF) Greenland Summit Camp. The author is a remote-sensing glaciologist at NASA's Goddard Space Flight Center. She took measurements that will be used to validate data collected by NASA s Aqua, Terra, and Ice, Clouds, and land Elevation Satellite (ICESat) satellites with ground-truth measurements of the Greenland Ice Sheet she made at Summit Camp from November 2008-February 2009. This article presents excerpts from the second half of her stay and work at the Greenland Summit. The second half of the story is presented in another issue of this journal

Koenig, Lora↗
DOE OSTI2024

Summit Darshan Archival Dataset

Summit Darshan Archival Dataset contains 2021 Summit Darshan log data for 25 applications and is grouped into science domains. The dataset is processed, and all the propriety fields are anonymized. The resultant data is converted into a tabular structure and saved in parquet file format. In this notebook, we demonstrate how to access the data. Data Organization: The data is organized into two directories: Darshan total (`darshan_total`): List all the high levels generated by the `darshan-parser --total` command on `.darshan` files. There is one parquet file for each application. Note: `uid` and `exe` field are masked Darshan detail (`darshan_detail`): This data contains detailed job level log information extracted by command `darshan-parser` on the raw `.darshan` files. The data is sorted by directory hierarchy in the order of `year/month/day (2021/12/07)`. For instance, to get the data for a `job_id` 3819766 of application `App11`, which was executed on `2021-12-07`can be accessed as follows. Note:`uid` and `filename` fields are masked

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

The 2024 “Hacking Limnology” Workshop Series and Virtual Summit: Increasing Inclusion, Participation, and Representation in the Aquatic Sciences

The 4th Aquatic Ecosystem MOdeling Network—Junior (AEMON-J) Hacking Limnology Workshop and 5th Virtual Summit: Incorporating Data Science and Open Science in the Aquatic Sciences (DSOS) convened 15–19 July 2024. During the week, these joint communities engaged in activities at the intersection of big data, open science, modeling, remote sensing, and the aquatic sciences. The weeklong event, with over 100 aquatic science practitioners and enthusiasts, followed a similar structure to previous years, comprising three days of workshops followed by two days of the virtual summit.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Usage Pattern Analysis for the Summit Login Nodes

High performance computing (HPC) users interact with Summit through dedicated gateways, also known as login nodes. The performance and stability of these login nodes can have a significant impact on the user experience. In this study, the performance and stability of Summit’s five login nodes are evaluated by analyzing the log data from 2020 and 2021. The analysis focuses on the computing capability (CPU average load, users and tasks) and the storage performance, along with the associated job scheduler activity. The outcome of this study can serve as the foundation of a predictive modeling framework that enables the system admin of an HPC system to preemptively deploy countermeasures before the onset of a system failure.

Eiffert, Brett↗
DOE OSTI2022

Proteome-scale Deployment of Protein Structure Prediction Workflows on the Summit Supercomputer

Deep learning has contributed to major advances in the prediction of protein structure from sequence, a fundamental problem in structural bioinformatics. With predictions now approaching the accuracy of crystallographic experiments, and with accelerators like GPUs and TPUs making inference using large models rapid, genome-level structure prediction becomes an obvious aim. Leadership-class computing resources can be used to perform genome-scale protein structure prediction using state-of-the-art deep learning models, providing a wealth of new data for systems biology applications. Here we describe our efforts to efficiently deploy the AlphaFold v.2 program, for full-proteome structure prediction, at scale on the Oak Ridge Leadership Computing Facility's resources, including the Summit supercomputer. We performed inference to produce the predicted structures for 40,526 protein sequences, corresponding to four prokaryotic proteomes and one plant proteome, using under 4,400 total Summit node hours, equivalent to using the majority of the supercomputer for a little over one hour. We also designed an optimized structure refinement that reduced the time for the relaxation stage of the AlphaFold pipeline by over 10X for longer sequences. We demonstrate the types of analyses that can be performed on proteome-scale collections of sequences, including a search for novel quaternary structures and implications for functional annotation.

Gao, Mu↗
DOE OSTI2020

Toward Large-Scale Image Segmentation on Summit

Semantic segmentation of images is an important computer vision task that emerges in a variety of application domains such as medical imaging, robotic vision and autonomous vehicles to name a few. While these domain-specific image analysis tasks involve relatively small image sizes (~ 10 2 × 10 2 ), there are many applications that need to train machine learning models on image data with extents that are orders of magnitude larger (~10 4 × 10 4 ). Training deep neural network (DNN) models on large extent images is extremely memory-intensive and often exceeds the memory limitations of a single graphical processing unit, a hardware accelerator of choice for computer vision workloads. Here, an efficient, sample parallel approach to train U-Net models on large extent image data sets is presented. Its advantages and limitations are analyzed and near-linear strong-scaling speedup demonstrated on 256 nodes (1536 GPUs) of the Summit supercomputer. Using a single node of the Summit supercomputer, an early evaluation of a recently released model parallel framework called GPipe is demonstrated to deliver ~ 2X speedup in executing a U-Net model with an order of magnitude larger number of trainable parameters than reported before. Performance bottlenecks for pipelined training of U-Net models are identified and mitigation strategies to improve the speedups are discussed. Together, these results open up the possibility of combining both approaches into a unified scalable pipelined and data parallel algorithm to efficiently train U-Net models with very large receptive fields on data sets of ultra-large extent images.

Seal, Sudip↗
DOE OSTI2021

Unprecedented cloud resolution in a GPU-enabled full-physics atmospheric climate simulation on OLCF’s summit supercomputer

Clouds represent a key uncertainty in future climate projection. While explicit cloud resolution remains beyond our computational grasp for global climate, we can incorporate important cloud effects through a computational middle ground called the Multi-scale Modeling Framework (MMF), also known as Super Parameterization. This algorithmic approach embeds high-resolution Cloud Resolving Models (CRMs) to represent moist convective processes within each grid column in a Global Climate Model (GCM). The MMF code requires no parallel data transfers and provides a self-contained target for acceleration. This study investigates the performance of the Energy Exascale Earth System Model-MMF (E3SM-MMF) code on the OLCF Summit supercomputer at an unprecedented scale of simulation. Hundreds of kernels in the roughly 10K lines of code in the E3SM-MMF CRM were ported to GPUs with OpenACC directives. A high-resolution benchmark using 4600 nodes on Summit demonstrates the computational capability of the GPU-enabled E3SM-MMF code in a full physics climate simulation.

58 GEOSCIENCES↗
DOE OSTI2021

Future of Water Infrastructure and Innovation Summit

Energy and water systems are interdependent, and the U.S. Department of Energy (DOE) has invested in energy and water for several years, including the Energy-Water Desalination Hub (led by the National Alliance for Water Innovation, NAWI), and research and development (R&D) in resource recovery from wastewater, among other areas. The Advanced Manufacturing Office (AMO) at the U.S. Department of Energy (DOE) held The Future of Water Infrastructure and Innovation Summit to inform the understanding of future opportunities in the water space. The virtual summit was held on October 27 and 28, 2020. The organizers gathered information from a diverse group of relevant water and wastewater stakeholders representing academia, industry, government, nongovernmental organizations, and local/regional utilities. Topics from Day 1 discussions included: desalination, water and wastewater treatment/recovery, produced water, industrial management of water, and hydropower, conveyance, and water systems.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2021

Summary Report for the Virtual Summit on Decarbonizing the Agriculture Sector

A virtual summit on decarbonizing the agriculture sector was organized on September 21-22, 2021, by Oak Ridge National Laboratory (ORNL) in anticipation of federal activities surrounding a greater role for the US Department of Energy (DOE) in support of the US Department of Agriculture (USDA) in areas related to climate and carbon impacts on the US agriculture sector. A series of thought-provoking talks were presented by leaders from industry, academia, and DOE (see attachment—final agenda, Appendix A). About 50 to 70 scientists and engineers actively participated at any one time during the summit in the talks and in the topical breakouts (Appendix B).

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Washington State Cyber Incident Response Summit Event Report

On September 7 and 8, 2022, Pacific Northwest National Laboratory and Washington State Adjutant General Major General Bret Daugherty, with support from the Department of Homeland Security Cybersecurity and Infrastructure Security Agency, hosted the Washington State Cyber Incident Response Summit at Camp Murray in Washington State. The invite-only summit convened 40 key decision makers and stakeholders from across the state to discuss strategies and pilot a collaborative approach to improve cyber incident response readiness within Washington. In small working groups, participants shared incident response lessons learned, best practices, and opportunities for improvement within the water and transportation sectors. This report highlights the details of the workshop presentations and discussions.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Workflows Community Summit 2024: Future Trends and Challenges in Scientific Workflows

The 2024 Workflows Community Summit report presents the outcomes of a three-day international gathering that brought together 109 experts from 18 countries to discuss future trends and challenges in scientific workflows. The summit focused on six key areas: time-sensitive workflows, convergence of AI and HPC workflows, multi-facility workflows, heterogeneous HPC environments, user experience and interfaces, and FAIR computational workflows. Discussions highlighted emerging challenges such as integrating AI with traditional HPC, managing workflows across diverse facilities, addressing heterogeneity in computing environments, and ensuring workflows are findable, accessible, interoperable, and reusable (FAIR). The report outlines recent advances, ongoing challenges, and provides recommendations for each topic area, emphasizing the need for standardization, improved interoperability, and the development of more sophisticated tools and frameworks to support the evolving landscape of scientific workflows in the era of exascale computing and AI integration.

97 MATHEMATICS AND COMPUTING↗
NASA NTRS2003

Determination of Mercury Content in a Shallow Firn Core from Summit, Greenland by Isotope Dilution Inductively Coupled Plasma Mass Spectrometry

The total mercury Hg content was determined in 6 cm sections of a near-surface 7 m firn core and in surrounding surface snow from Summit, Greenland (elevation: 3238 m, 72.58 N, 38.53 W) in May 2001 by isotope dilution cold-vapor inductively coupled plasma mass spectrometry (ID-CV-ICP-MS). The focus of this research was to evaluate the capability of the ID-CV-ICPMS technique for measuring trace levels of Hg typical of polar snow and firn. Highly enriched Hg-201 isotopic spike is added to approximately 10 ml melted core and thoroughly mixed. The Hg(+2) in the sample is reduced on line with tin (II) chloride (SnCl2) and the elemental Hg (Hg(0)) vapor pre-concentrated on to gold gauze using a commercial amalgam system. The Hg is then thermally desorbed and introduced into a quadrupole ICP-MS. The blank corrected Hg concentrations determined for all samples ranged from 0.25 ng/L to 1.74 ng/L (ppt) (average 0.59 ng/L plus or minus 0.28 ng/L) and fall within the range of those previously determined by Boutron et al., 1998 (less than or equal to 0.05 ng/L to 2.0 ng/L) for the Summit site. The average blank value was 0.19 ng/L plus or minus 0.045 ng/L (n=6). The Hg values specifically for the firn core range from 0.25 ng/L to 0.87 ng/L (average 0.51 ng/L plus or minus 0.13 ng/L) and show both values declining with time and larger variability in concentration in the top 1.8 m.

Mann, Jacqueline L.↗
DOE OSTI2020

Performance Evaluation of Python Based Data Analytics Frameworks in Summit: Early Experiences

The explosion in the volumes of data generated from ever-larger simulation campaigns and experiments or observations necessitates competent tools for data wrangling and analysis). While the Oak Ridge Leadership Computing Facility (OLCF) provides a variety of tools to perform data wrangling and data analysis tasks, Python based tools often lack scalability, or the ability to fully exploit the computational capability of OLCF’s Summit supercomputer. NVIDIA RAPIDS and Dask offer a promising solution to accelerate and distribute data analytics workloads from personal computers to heterogeneous supercomputing systems. We discuss early performance evaluation results of RAPIDS and Dask on Summit to understand their capabilities, scalability, and limitations. Our evaluation includes a subset of RAPIDS libraries, i.e., cuDF, cuML, and cuGraph, and Chainer’s CuPy, and their multi-GPU variants when available.We also draw on the observed trends from the performance evaluation results to discuss best practices for maximizing performance.

Hernandez Arreguin, Benjamin↗
DOE OSTI2022

Pseudonymized User-Perspective Summit Login Node Data for 2020 and 2021

This dataset contains hourly snapshot data from each of the 5 login nodes of the Summit supercomputer at Oak Ridge Leadership Computing Facility (OLCF) over a period of 2 years, starting January 2020 and ending after December 2021. The snapshots include lists of currently logged-in users, CPU and memory usage, status of users' batch jobs, and disk usage statistics. Usernames, project identifiers, and file paths have been pseudonymized in order to allow studies of user behavior without divulging Personally Identifiable Information (PII).

97 MATHEMATICS AND COMPUTING↗