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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 OSTI2020

Time as a microbial resource

Microbes need resources for energy and cellular building material. They also need access to clement conditions with liquid water and a cellular damage rate that is lower than repair. When deprived of resources and clement conditions, microbes often enter some form of dormancy (e.g., by ceasing cell division, slowing metabolic rate, or forming an endospore) until they can grow again. For example, at night, phototrophs wait for the sun to return. In winter, soil microbes wait for warmer temperatures. Microbes that cause diseases like tuberculosis can stay dormant for years, waiting for the cessation of antibiotic or immune system bombardment. But what about longer timescales? Unlike multicellular life, microbes survive in an extremely broad range of conditions and can access an amazing variety of resources to maintain cellular functions in the absence of cell division. This means that they have the potential to be dormant for much longer than a few months or years. There is no theoretical reason microbes cannot survive on maintenance energy for hundreds or thousands of years, or longer, with little to no cell proliferation. Here, given this lack of theoretical constraints on the length of microbial dormancy intervals, two questions arise, (i) is there evidence for the existence of organisms experiencing very long dormancies? And (ii) what could be the advantages of such long wait times?

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Solving the sample size problem for resource selection functions

Abstract Sample size sufficiency is a critical consideration for estimating resource selection functions (RSFs) from GPS‐based animal telemetry. Cited thresholds for sufficiency include a number of captured animals and as many relocations per animal N as possible. These thresholds render many RSF‐based studies misleading if large sample sizes were truly insufficient, or unpublishable if small sample sizes were sufficient but failed to meet reviewer expectations. We provide the first comprehensive solution for RSF sample size by deriving closed‐form mathematical expressions for the number of animals M and the number of relocations per animal N required for model outputs to a given degree of precision. The sample sizes needed depend on just 3 biologically meaningful quantities: habitat selection strength, variation in individual selection and a novel measure of landscape complexity, which we define rigorously. The mathematical expressions are calculable for any environmental dataset at any spatial scale and are applicable to any study involving resource selection (including sessile organisms). We validate our analytical solutions using globally relevant empirical data including 5,678,623 GPS locations from 511 animals from 10 species (omnivores, carnivores and herbivores living in boreal, temperate and tropical forests, montane woodlands, swamps and Arctic tundra). Our analytic expressions show that the required M and N must decline with increasing selection strength and increasing landscape complexity, and this decline is insensitive to the definition of availability used in the analysis. Our results demonstrate that the most biologically relevant effects on the utilization distribution (i.e. those landscape conditions with the greatest absolute magnitude of resource selection) can often be estimated with much fewer than animals. We identify several critical steps in implementing these equations, including (a) a priori selection of expected model coefficients and (b) regular sampling of background (pseudoabsence) data within a given definition of availability. We discuss possible methods to identify a priori expectations for habitat selection coefficients, effects of scale on RSF estimation and caveats for rare species applications. We argue that these equations should be a mandatory component for all future RSF studies.

Street, Garrett M.↗
DOE OSTI2022

Sorghum Association Panel whole‐genome sequencing establishes cornerstone resource for dissecting genomic diversity

SUMMARY Association mapping panels represent foundational resources for understanding the genetic basis of phenotypic diversity and serve to advance plant breeding by exploring genetic variation across diverse accessions. We report the whole‐genome sequencing (WGS) of 400 sorghum ( Sorghum bicolor (L.) Moench) accessions from the Sorghum Association Panel (SAP) at an average coverage of 38× (25–72×), enabling the development of a high‐density genomic marker set of 43 983 694 variants including single‐nucleotide polymorphisms (approximately 38 million), insertions/deletions (indels) (approximately 5 million), and copy number variants (CNVs) (approximately 170 000). We observe slightly more deletions among indels and a much higher prevalence of deletions among CNVs compared to insertions. This new marker set enabled the identification of several novel putative genomic associations for plant height and tannin content, which were not identified when using previous lower‐density marker sets. WGS identified and scored variants in 5‐kb bins where available genotyping‐by‐sequencing (GBS) data captured no variants, with half of all bins in the genome falling into this category. The predictive ability of genomic best unbiased linear predictor (GBLUP) models was increased by an average of 30% by using WGS markers rather than GBS markers. We identified 18 selection peaks across subpopulations that formed due to evolutionary divergence during domestication, and we found six F st peaks resulting from comparisons between converted lines and breeding lines within the SAP that were distinct from the peaks associated with historic selection. This population has served and continues to serve as a significant public resource for sorghum research and demonstrates the value of improving upon existing genomic resources.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2016

WE-FG-201-02: Automated Treatment Planning for Low-Resource Settings

Many low- and middle-income countries lack the resources and services to manage cancer, from screening and diagnosis to radiation therapy planning, treatment and quality assurance. The challenges in upgrading or introducing the needed services are enormous, and include severe shortages in equipment and trained staff. In this symposium, we will describe examples of technology and scientific research that have the potential to impact all these areas. These include: (1) the development of high-quality/low-cost colposcopes for cervical cancer screening, (2) the application of automated radiotherapy treatment planning to reduce staffing shortages, (3) the development of a novel radiotherapy treatment unit, and (4) utilizing a cloud-based infrastructure to facilitate collaboration and QA. Learning Objectives: Understand some of the issues in cancer care in low- resource environments, including shortages in staff and equipment, and inadequate physical infrastructure for advanced radiotherapy. Understand the challenges in developing and deploying diagnostic and treatment devices and services for low-resource environments. Understand some of the emerging technological solutions for cancer management in LMICs. NCI; L. Court, NIH, Varian, Elekta; I. Feain, Ilana Feain is founder and CTO of Nano-X Pty Ltd.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2020

Best Practices for Successfully Writing and Publishing a Genome Announcement in Microbiology Resource Announcements

Microbiology Resource Announcements (MRA) provides peer-reviewed announcements of scientific resources for the microbial research community. We describe the best practices for writing an announcement that ensures that these publications are truly useful resources. Adhering to these best practices can lead to successful publication without the need for extensive revisions.

Dunning Hotopp, Julie C.↗
DOE OSTI2021

RAVIS: Resource Forecast and Ramp Visualization for Situational Awareness of Variable Renewable Generation

The Resource Forecast and Ramp Visualization for Situational Awareness (RAVIS) is an innovative, open-source tool for visualizing variable renewable resource forecasts and alerts for significant up- and down-ramps in renewable generation and the consequent system net load. The modular dashboard of RAVIS contains configurable panes for viewing probabilistic time-series forecasts, ramp event alerts on the look-ahead timeline, and spatially resolved resource sites, and forecasts. For comprehensive situational awareness, the tool can add additional data layers from simulation and independent system operator (ISO) market clearing data---including electric line utilization, nodal price, and available generation flexibility---in response to continuously updated renewable forecasts. This paper introduces the RAVIS technology suite employed to provide optimum visualization and flexible design characteristics. The paper also illustrates some use cases of the tool using site-specific solar power forecast data obtained from the IBM Watt-Sun forecasting platform for the California ISO and Midcontinent ISO footprints. The source code for RAVIS is public (https://github.com/ravis-nrel/ravis), and the intended users are forecasters, utility planners, ISO operators, and researchers.

41 EE - Solar Energy Technologies Office (EE-4S)↗
DOE OSTI2019

Probabilistic Resource Adequacy Suite (PRAS)

The Probabilistic Resource Adequacy Suite (PRAS) provides an open-source, research-oriented collection of tools for analysing the resource adequacy of a bulk power system. The simulation methods offered support everything from classical convolution-based analytical techniques through to high-performance sequential Monte Carlo methods supporting multi-region composite reliability assessment, including simulation of energy-limited resources such as storage.

Stephen, Gord↗
DOE OSTI2024

HIPSTER (Harmonized Impacts across Products, Scenarios, and Technologies for Environmental and Resource metrics) [SWR-24-17]

Development of a prototype, code-based life cycle assessment framework, the Harmonized Impacts across Products, Scenarios, and Technologies for Environmental and Resource metrics (HIPSTER), to coherently assess outputs of prospective NREL models across environmental and resource use metrics. Eventually, HIPSTER is aimed to also cover socioeconomic and justice metrics. At present, NREL model scenario impact assessments are performed incoherently, as they as based on varying assumptions and inputs, specifically life cycle inventories, and cover different system boundaries. HIPSTER was created to align the assumptions and boundaries with those of cradle-to-grave life cycle assessment. In FY22, HIPSTER was successfully applied using scenarios from the ReEDS™ model, NREL's flagship capacity expansion model resulting in cradle-to-grace time-series life cycle impacts across LCA midpoints and resource use metrics.

Ghosh, Tapajyoti↗
DOE OSTI2025

Flexible Resource Scheduler for FAST-DERMS (FRS-FASTDERMS) v0.9

The Flexible Resource Scheduler is a hierarchical controller that manages the distributed energy resources in a distribution substation or distribution feeder to provide a firm commitment of power flow at the substation or feeder head to be scheduled in transmission-level markets as an aggregated demand resource. It is the reference controller for the FAST-DERMS Architecture, developed in tandem with the architecture under the DOE FAST-DERMS project. It is comprised of a day-ahead stochastic optimization, which schedules substation power flow and reserves, a intra-hour MPC, which generates dispatch base points for DER, and a real-time PID controller maintaining that dispatches DER to maintain the substation power around the base points. The repository also includes a representative aggregator controller, and all of the necessary components to run a simulation using PNNL's GridAPPS-D software with the controller.

MacDonald, Jason [Lawrence Berkeley National Labor↗
DOE OSTI2021

SMC 2021 : Analyzing Resource Utilization and User Behavior on Titan Supercomputer

Resource utilization statistics of submitted jobs on a supercomputer can help us understand how users from various scientific domains use HPC platforms and better design a job scheduler. We explore to generate insight regarding workload distribution and usage pattern domains from job scheduler trace, GPU failure information, and project-specific information collected from Titan supercomputer. Furthermore, we want to know how the scheduler performance varies over time and how the users' scheduling behavior changes following a system failure. These observations have the potential to provide valuable insight, which is helpful to prepare for system failures. These practices will help us develop and apply novel machine learning algorithms in understanding system behavior, requirement, and better scheduling of HPC systems. There are two datasets, RUR and GPU. RUR: This dataset is the job scheduler traces collected from the Titan supercomputerfrom 01/01/2015 to 07/31/2019 (2015.csv - 2019.csv). These were collected usingResource Utilization Report (RUR), a Cray-developed resource-usage data collectionand reporting system. It contains the usage information of its critical resources (CPU,Memory, GPU, and I/O) of each running job on Titan during that period [2]. ProjectAreas: Every job is associated with a project ID. TheProjectAreas.csvdatasetprovides a mapping of the project ID to its domain science. GPU: There have been some hardware-related issues in the GPUs in Titan that caused some GPUs to fail, sometimes irrecoverably during some job runs. This dataset provides information regarding these failures during the execution of the submitted jobs. GPUs on Titan are uniquely identified by a serial number (SN), and they are installed in a location. A GPU can be installed in a location, then removed from that location following a failure, and then re-installed in a different location after fixing the problem. If the failure can't be recovered, the GPU might be removed entirely from Titan. There are two prominent types of failures that resulted in the removal of GPUs from Titan: Double Bit Error (DBE) and Out of the Bus (OTB). The dataset (gc_full.csv) has the following fields: 1. SN : Serial number of a GPU 2. location : The location where it is installed 3. insert : The time when it was inserted into that location 4. remove : The time when it was removed from that location 5. duration : Amount of time the GPU spent in this location 6. out : If the device was taken out entirely w/o a re-installment into a new location. 7. event : If the GPU was taken out entirely, the reason for its removal. To learn more about this dataset, please refer to the git repositoryhttps://github.com/olcf/TitanGPULifeand the related publication [1]. References [1] George Ostrouchov, Don Maxwell, Rizwan A Ashraf, Christian Engelmann, MallikarjunShankar, and James H Rogers. Gpu lifetimes on titan supercomputer: Survival analysisand reliability. InSC20: International Conference for High Performance Computing,Networking, Storage and Analysis, pages 1-14. IEEE, 2020. [2] Feiyi Wang, Sarp Oral, Satyabrata Sen, and Neena Imam. Learning from five-yearresource-utilization data of titan system. In2019 IEEE International Conference onCluster Computing (CLUSTER), pages 1-6. IEEE, 2019.

42 ENGINEERING↗
DOE OSTI2022

Habitat use by female desert tortoises suggests tradeoffs between resource use and risk avoidance

Animals may select habitat to maximize the benefits of foraging on growth and reproduction, while balancing competing factors like the risk of predation or mortality from other sources. Variation in the distribution of food resources may lead animals to forage at times or in places that carry greater predation risk, with individuals in poor quality habitats expected to take greater risks while foraging. We studied Mojave desert tortoises ( Gopherus agassizii ) in habitats with variable forage availability to determine if risk aversion in their selection of habitat relative was related to abundance of forage. As a measure of risk, we examined tortoise surface activity and mortality. We also compared tortoise body size and body condition between habitats with ample forage plants and those with less forage plants. Tortoises from low forage habitats selected areas where more annual plants were nutritious herbaceous flowering plants but did not favor areas of greater perennial shrub cover that could shelter them or their burrows. In contrast, tortoises occupying high forage habitats showed no preference for forage characteristics, but used burrows associated with more abundant and larger perennial shrubs. Tortoises in high forage habitats were larger and active above ground more often but did not have better body condition. Mortality was four times higher for females occupying low forage habitat than those in high forage habitat. Our results are consistent with the idea that tortoises may minimize mortality risk where food resources are high, but may accept some tradeoff of greater mortality risk in order to forage optimally when food resources are limiting.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2020

Shallow EGS Regional Resource Potential and Map - Snake River Plain

SMU Geothermal Lab developed a methodology to estimate shallow (1 km to 4 km) Enhanced Geothermal Systems (EGS) resource potential using an approach that utilizes recent geology and geophysical research along with new well data to improve the thermal conductivity model, mitigate impacts from groundwater flow in the thermal model, and examine radioactivity data variations. By incorporating the results of the most recent projects with the SMU shallow methodology, we developed a more accurate, updated resource estimate for the Snake River Plain (SRP). The resulting maps and resource estimates can be used by the National Renewable Energy Lab (NREL), Bureau of Land Management (BLM), and the public to determine how best to move forward with future project development.

15 GEOTHERMAL ENERGY↗
DOE OSTI2020

Analysis of Geothermal Resources in Three Texas Counties

This project updates the geothermal resources beneath our oil and gas fields, as part of the research for the Texas GEO project. This report "Analysis of Geothermal Resources in Three Texas Counties" (October 2020) improves on previous mapping of the Texas resources for the counties of Crockett (West Texas), Jackson (central Gulf Coast) and Webb (South Texas). Through additional bottom-hole temperatures (BHT), the number of well sites increased from 532 to 5,410 in total for these counties. The improved methodology to calculate formation temperatures from 3.5 km (11,500 ft) to 10 km (32,800 ft) includes thermal conductivity values more closely related to the actual county geological formations, incorporated radiogenic heat production of formations, and the related mapped depth to basement. The results show deep temperatures as hotter than previously calculated, with temperatures of 150 degrees Celcius possible for Webb County between depths of 2.6 - 5.1 km, Jackson County between depths 3.0 - 5.4 km, and Crockett County between depths of 2.7 - 8.0 km.

10 km↗
DOE OSTI2024

Ground Source Heat Pump Screening Tool and Related Resources

This archive contains both a link to Oak Ridge National Laboratory's Ground Source Heat Pump (GSHP) Screening Tool and related resources documenting its development, technical features, and applications. The web-based screening tool is a techno-economic analysis resource designed to evaluate the feasibility, costs, and benefits of implementing GSHP systems in buildings across various U.S. climate zones. It is intended for use by building owners, HVAC system designers, and installers to support decision-making in residential and commercial applications, incorporating advanced modeling capabilities such as ground heat exchanger design. The included resources detail the tool's creation and use, covering topics such as innovative ground heat exchanger design methodologies, the development of an open library of g-functions for borehole configurations, and studies on GSHP performance and economic viability in retrofitting single-family homes.

15 GEOTHERMAL ENERGY↗
DOE OSTI2021

Carbon Ore Resources Database (CORD)

The Carbon Ore Resources Database (CORD) is a working collection of 399 data files associated with carbon ore resources in the United States. The collection includes spatial/non-spatial, filtered, processed, and secondary data files with original data acquisition efforts focused on domestic coal resources. The current version 1.0 is on NETL ArcGIS Enterprise.

Carbon Ore↗
DOE OSTI2014

A High Performance Computing Scheduling and Resource Management Primer

This primer introduces the concepts of High Performance Computing, scheduling, and resource management. It focuses first on the state of the art and then introduces the issues under consideration in the design of a next generation resource manager known as Flux. The Flux design addresses not just the challenges of resource management at a much larger scale of systems, but it also enables and promotes a much broader and richer model that invites collaboration with areas whose requirements cannot be met in current systems.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2014

Solar Resource Measurements in Canyon, Texas - Equipment Only Loan: Cooperative Research and Development Final Report, CRADA Number CRD-07-233

Site-specific, long-term, continuous, and high-resolution measurements of solar irradiance are important for developing renewable resource data. These data are used for several research and development activities consistent with the NREL mission: establish a national 30-year climatological database of measured solar irradiances; provide high-quality ground-truth data for satellite remote sensing validation; support development of radiative transfer models for estimating solar irradiance from available meteorological observations; and provide solar resource information needed for technology deployment and operations. Data acquired under this agreement will be available to the public through NREL's Measurement & Instrumentation Data Center (MIDC) or the Renewable Resource Data Center (RReDC). The MIDC offers a variety of standard data display, access, and analysis tools designed to address the needs of a wide user audience (e.g., industry, academia, and government interests).

14 SOLAR ENERGY↗
DOE OSTI2015

Expanding Geothermal Resource Utilization through Directed Research, Education, and Public Outreach

The Great Basin Center for Geothermal Energy (GBCGE or the Center) was established at the University of Nevada, Reno (UNR) in May 2000 to promote research and utilization of geothermal resources. The Center received funding through this grant to promote increased geothermal development in the Great Basin, with most of the funding used for peer-reviewed research. Funding to the Center and work under the contract were initiated in March 2002, with supplemental funding in subsequent years. The Center monitored the research projects that were competitively awarded in a series of proposal calls between 2002 and 2007. Peer-reviewed research promoted identification and utilization of geothermal resources in Nevada. Projects used geology, geochemistry, geophysics, remote sensing, and the synthesis of multi-disciplinary information to produce new models of geothermal systems in the Western U.S. and worldwide. Funds were also used to support graduate student research and training. Part of the grant was used to support public outreach activities, including webpages, online maps and data resources, and informational workshops for stakeholders.

15 GEOTHERMAL ENERGY↗