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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 1,765 records · Page 98
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

EchemFEM: A Firedrake-based Python package for electrochemical transport

The transition from fossil fuels to renewable energy has brought about a rapid increase in the availability of clean electricity. However, electricity generated from sources such as wind and solar are limited to intermittent operation due to daily and seasonal variation. One solution is to utilize electrochemical devices in energy storage and electrochemical manufacturing applications, where they can harness surplus energy and decarbonize chemical industries traditionally reliant on petrochemical feedstocks. Managing the growing prevalence of renewable energy underscores the importance of developing and scaling up these technologies, which can in turn facilitate the achievement of carbon emission reduction commitments of companies and developed economies. Likewise, the electrification of transport creates an increasing need for energy-dense electrochemical energy storage devices such as batteries and supercapacitors. Naturally, simulation tools are required to assist in the design of efficient and industrial-scale electrochemical devices.

30 DIRECT ENERGY CONVERSION↗
DOE OSTI2024

naturf: a package for generating urban parameters for numerical weather modeling

The Neighborhood Adaptive Tissues for Urban Resilience Futures tool (NATURF) is a Python workflow that generates files readable by the Weather Research and Forecasting (WRF) model. NATURF uses geopandas and hamilton to calculate 132 building parameters from shapefiles with building footprint and height information. These parameters can be collected and used in many formats, and the primary output is a binary file configured for input to WRF. This workflow is a flexible adaptation of the National/World Urban Database and Access Portal Tool (NUDAPT/WUDAPT) that can be used with any study area at any spatial resolution. The climate modeling community and urban planners can identify the effects of building/neighborhood morphology on the microclimate using the urban parameters and WRF-readable files produced by NATURF. More information on the urban parameters calculated can be found in the documentation.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

DeepBench: A simulation package for physical benchmarking data

We introduce **DeepBench**, a python library that generates simple simulated image data from first principles, such as basic geometric shapes and astronomical objects. These data are highly valuable for developing (calibration, testing, and benchmarking) statistical and machine learning models because they make it possible to connect the final data product to physically interpretable inputs. This software includes tools to curate and store the datasets to maximize reproducibility.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2024

wmpy-power: A Python package for process-based regional hydropower simulation

Hydropower is an important source of renewable energy in many parts of the world. The generation potential for a hydropower facility can vary greatly due to fluctuations in precipitation and snowmelt patterns impacting streamflow and reservoir storage. Human activities such as irrigation, manufacturing, and hydration can also influence water availability at nearby and downstream facilities. wmpy-power--the hydropower model described in this work--is process-based, leveraging explicit reservoir storage and release data to address impacts on hydropower from climate change and human adaptive behaviors to inform long-term planning and resource-adequacy considerations.

13 HYDRO ENERGY↗
DOE OSTI2024

CurvilinearGrids.jl: A Julia package for curvilinear coordinate transformations

Finite-difference discretizations of partial differential equations are widespread throughout the scientific community. Oftentimes finite-differences are used to compute spatial gradients of fields on a discrete grid, which is typically a uniform or rectilinear Cartesian mesh. Arbitrary multidimensional geometry is difficult to discretize directly with finite differences, however, due to non-uniform grid spacing and non-orthogonality. Curvilinear coordinate transformations can be used as a strategy to enable arbitrary geometry. While these curvilinear transformations are straightforward, the governing PDEs require additional terms (metrics) and must adhere to strict conservation laws; these criteria complicate the application of the transformation and require careful implementation.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

DeepHyper: A Python Package for Massively Parallel Hyperparameter Optimization in Machine Learning

Machine learning models are increasingly applied across scientific disciplines, yet their effectiveness often hinges on heuristic decisions—such as data transformations, training strategies, and model architectures—that are not learned by the models themselves. Automating the selection of these heuristics and analyzing their sensitivity is crucial for building robust and efficient learning workflows. DeepHyper addresses this challenge by democratizing hyperparameter optimization, providing accessible tools to streamline and enhance machine learning workflows from a laptop to the largest supercomputer in the world. Building on top of hyperparameter optimization, it unlocks new capabilities around ensembles of models for improved accuracy and uncertainty quantification. All of these organized around efficient parallel computing.

ensemble↗
DOE OSTI2025

hashin_shtrikman_mp: a package for the optimal design and discovery of multi-phase composite materials

hashin_shtrikman_mp is a tool for composites designers who have desired composite properties in mind, but who do not yet have an underlying formulation. The library utilizes the tightest theoretical bounds on the effective properties of composite materials with unspecified microstructure – the Hashin-Shtrikman bounds – to identify candidate theoretical materials, find real materials that are close to the candidates, and determine the optimal volume fractions for each of the constituents in the resulting composite. Its features include (i) leveraging of materials in the Materials Project database, (ii) integration with the Materials Project API, (iii) use of genetic machine-learning, (iv) agnosticism to underlying microstructure, and (v) ultimate engineering application, make it a tool with much broader applications than its predecessors.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2015

Report on Review of Waste Package Reliability Estimates for Geologic Disposal

Disposal overpacks are proposed as an element of the engineered barrier system for direct disposal of spent nuclear fuel in dual-purpose canisters (DPCs). DPCs are currently licensed for storage and transport, but not disposal. In the DPC disposal system, overpacks would provide long-term containment, and conversely, they would keep groundwater from flooding DPCs. Without flooding, DPCs can never achieve nuclear criticality because they are under-moderated.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2015

Verification and Validation Propensity Improvements via Solution Verification Automation: Presentation Package (Part 1 of 2) (FY15 ASC L2 milestone #5320)

A new tool, called Mesh Scaling, for producing series of hexahedral meshes suitable for solution verification was enhanced and hardened by this milestone. In addition, solution verification using the meshes produced from Mesh Scaling was performed and documented. We conclude that Mesh Scaling now produces meshes suitable for solution verification, while offering a substantial decrease in the computational cost of solution verification.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2015

Completion of Level 4 Milestone M4AT-15OR2301039 for the Johnson Noise Thermometry for Drift-free Temperature Measurements Work Package AT-15OR230103

This memorandum constitutes our September 2015 level 4 milestone for the project entitled “Johnson Noise Thermometry for Drift-free Temperature Measurements” and satisfies the Milestone/Activity (Conclude HFIR field demonstration of JNT prototype). The progress summary describes the work performed to complete the subject milestone.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2016

Data Validation Package May 2015, Groundwater Sampling at the Shoal, Nevada, Site

The U.S. Department of Energy Office of Legacy Management conducted annual sampling at the Shoal, Nevada, Site (Shoal) in May 2015. Groundwater samples were collected from wells MV-1, MV-2, MV-3, MV-4, MV-5, H-3, HC-1, HC-2d, HC-3, HC-4, HC-5, HC-6, HC-7, HC-8, and HS-1. Sampling was conducted as specified in the Sampling and Analysis Plan for US. Department of Energy Office of Legacy Management Sites (LMS/PRO/S04351, continually updated, http://energy.gov/lm/downloads/sampling-and-analysis-plan-us-department-energy­ office-legacy-management-sites). Monitoring wells MV-1, MV-2, MV-3, MV-4, MV-5, HC-2d, HC-4, HC-5, HC-7, HC-8, and HS-1 were purged prior to sampling using dedicated submersible pumps. At least one well casing volume was removed, and field parameters (temperature, pH, and specific conductance) were allowed to stabilize before samples were collected. Samples were collected from wells H-3, HC-1, HC-3, and HC-6 using a depth-specific bailer because these wells are not completed with dedicated submersible pumps. Samples were submitted under Requisition Index Number (RIN) 15057042 to ALS Laboratory Group in Fort Collins, Colorado, for the determination of bromide, gross alpha, gross beta, tritium, uranium isotopes, and total uranium (by mass); and under RIN 15057043 to the University of Arizona for the determination of carbon-14 and iodine-129. A duplicate sample from location MV-2 was included with RIN 15057042. The laboratory results from the 2015 sampling event are consistent with those of previous years with the exception of sample results from well HC-4. This well continues to be the only well with tritium concentrations above the laboratory’s minimum detectable concentration which is attributed to the wells proximity to the nuclear detonation. The tritium concentration (731 picocuries per liter [pCi/L]) is consistent with past results and is below the U.S. Environmental Protection Agency's (EPA) maximum contaminant level (MCL) of 20,000 pCi/L. However, concentrations of gross alpha, uranium, and carbon-14 all increased in the sample from well HC-4 during this sampling event. Concentrations of gross alpha and uranium have been above the EPA MCLs in this well since 2012 and the highest concentrations of gross alpha (60.6 pCi/L) and uranium (110 micrograms per liter) were detected during this sampling event. Refer to the time-concentration plots included with this report. Also see the 2015 Groundwater Monitoring Report Project Shoal Area: Subsurface Corrective Action Unit 447 for additional information on the 2015 sampling results.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2015

AGC-2 Specimen Post Irradiation Data Package Report

This report documents results of the post-irradiation examination material property testing of the creep, control, and piggyback specimens from the irradiation creep capsule Advanced Graphite Creep (AGC)-2 are reported. This is the second of a series of six irradiation test trains planned as part of the AGC experiment to fully characterize the neutron irradiation effects and radiation creep behavior of current nuclear graphite grades. The AGC-2 capsule was irradiated in the Idaho National Laboratory Advanced Test Reactor at a nominal temperature of 600°C and to a peak dose of 5 dpa (displacements per atom). One-half of the creep specimens were subjected to mechanical stresses (an applied stress of either 13.8, 17.2, or 20.7 MPa) to induce irradiation creep. All post-irradiation testing and measurement results are reported with the exception of the irradiation mechanical strength testing, which is the last destructive testing stage of the irradiation testing program. Material property tests were conducted on specimens from 15 nuclear graphite grades using a similar loading configuration as the first AGC capsule (AGC-1) to provide easy comparison between the two capsules. However, AGC-2 contained an increased number of specimens (i.e., 487 total specimens irradiated) and replaced specimens of the minor grade 2020 with the newer grade 2114. The data reported include specimen dimensions for both stressed and unstressed specimens to establish the irradiation creep rates, mass and volume data necessary to derive density, elastic constants (Young’s modulus, shear modulus, and Poisson’s ratio) from ultrasonic time-of-flight velocity measurements, Young’s modulus from the fundamental frequency of vibration, electrical resistivity, and thermal diffusivity and thermal expansion data from 100–500°C. No data outliers were determined after all measurements were completed. A brief statistical analysis was performed on the irradiated data and a limited comparison between pre- and post-irradiation properties is presented. A more complete evaluation of trends in the material property changes, as well as irradiation-induced creep due to irradiation, temperature, and applied load on specimens will be discussed in later AGC-2 post-irradiation examination analysis reports.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2016

Waste Form Release Data Package for the 2005 Integrated Disposal Facility Performance Assessment. Erratum

Please insert erratum page in report PNNL-14805. This erratum serves to correct the K g (Glass apparent equilibrium constant based on the SiO 2 (aq) activity) values affected by a calculation error for glasses LAWA44, LAWB45 and LAWC22. The error was first identified by Papathanassiu et al. (2011). The K g values were corrected by Freedman et al. (2015) by multiplying the erroneous K g values by approx. 3.4. As indicated in Freedman et al. (2015), this 3.4 factor originates from the erroneous use of the molecular weight of H 4 SiO 4 (96.114 g·mol –1 ) instead of the molecular weight for Si (28.086 g·mol –1 ).

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2016

Data Validation Package, December 2015, Groundwater Sampling at the Bluewater, New Mexico, Disposal Site, September 2016

Groundwater samples were collected from monitoring wells at the Bluewater, New Mexico, Disposal Site to monitor groundwater contaminants as specified in the 1997 Long-Term Surveillance Plan for the DOE Bluewater (UMTRCA Title II) Disposal Site Near Grants, New Mexico (LTSP). Sampling and analyses were conducted as specified in the Sampling and Analysis Plan for U.S. Department of Energy Office of Legacy Management Sites (LMS/PRO/S04351, continually updated). A duplicate sample was collected from location HMC-951. Alluvium wells are completed in the alluvial sediments in the former channel of the Rio San Jose, which was covered by basalt lava flows known as the El Malpais, and are identified by the suffix (M). Bedrock wells are completed in the San Andres Limestone/Glorieta Sandstone hydrologic unit (San Andres aquifer) and are identified by the suffix (SG). Wells HMC-951 and OBS-3 are also completed in the San Andres aquifer. The LTSP requires monitoring for molybdenum, selenium, uranium, and polychlorinated biphenyls (PCBs); PCB monitoring occurs only during November sampling events. This event included sampling for an expanded list of analytes to characterize the site aquifers and to support a regional groundwater investigation being conducted by the New Mexico Environment Department.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2016

Data Validation Package, June 2016 Groundwater Sampling at the Hallam, Nebraska, Decommissioned Reactor Site, August 2016

The 2008 Long-Term Surveillance Plan [LTSP] for the Decommissioned Hallam Nuclear Power Facility, Hallam, Nebraska (http://www.lm.doe.gov/Hallam/Documents.aspx) requires groundwater monitoring once every 2 years. Seventeen monitoring wells at the Hallam site were sampled during this event as specified in the plan. Planned monitoring locations are shown in Attachment 1, Sampling and Analysis Work Order. Water levels were measured at all sampled wells and at two additional wells (6A and 6B) prior to the start of sampling. Additionally, water levels of each sampled well were measured at the beginning of sampling. See Attachment 2, Trip Report, for additional details. Sampling and analysis were conducted as specified in Sampling and Analysis Plan for U.S. Department of Energy Office of Legacy Management Sites (LMS/PRO/S04351, continually updated, http://energy.gov/lm/downloads/sampling-and-analysis-plan-us-department- energy-office-legacy-management-sites). Gross alpha and gross beta are the only parameters that were detected at statistically significant concentrations. Time/concentration graphs of the gross alpha and gross beta data are included in Attachment 3, Data Presentation. The gross alpha and gross beta activity concentrations observed are consistent with values previously observed and are attributed to naturally occurring radionuclides (e.g., uranium and uranium decay chain products) in the groundwater.

2016 Groundwater Sampling↗