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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 127 records · Page 7
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

ZFP Hardware Implementation

As core counts increase in new HPC systems with comparatively little increase in memory bandwidth, the trend is an effective decrease in memory bandwidth per core. Other bandwidth limitations in HPC systems exist between CPU and GPU memory, between system nodes, and between node memory and storage. Compression of floating-point data has the potential to reduce data movement and pressure across these communication channels. Furthermore, it has the potential to reduce the footprint of floating-point arrays stored in memory. ZFP, implemented in software, is gaining traction as an effective method in floating-point compression; however, performance gains are limited to the spare compute cycles available before reaching the bandwidth limitations of the communication channel. A hardware implementation of ZFP has the potential to raise the bar on performance. From the inception of ZFP, it was designed to accommodate a hardware implementation.

42 ENGINEERING↗
DOE OSTI2012

An Integrated Geovisual Analytics Framework for Analysis of Energy Consumption Data and Renewable Energy Potentials

We present an integrated geovisual analytics framework for utility consumers to interactively analyze and benchmark their energy consumption. The framework uses energy and property data already available with the utility companies and county governments respectively. The motivation for the developed framework is the need for citizens to go beyond the conventional utility bills in understanding the patterns in their energy consumption. There is also a need for citizens to go beyond one-time improvements that are often not monitored and measured over time. Some of the features of the framework include the ability for citizens to visualize their historical energy consumption data along with weather data in their location. The quantity of historical energy data available is significantly more than what is available from utility bills. An overlay of the weather data provides users with a visual correlation between weather patterns and their energy consumption patterns. Another feature of the framework is the ability for citizens to compare their consumption on an aggregated basis to that of their peers other citizens living in houses of similar size and age and within the same or different geographical boundaries, such as subdivision, zip code, or county. The users could also compare their consumption to others based on the size of their family and other attributes. This feature could help citizens determine if they are among the best in class . The framework can also be used by the utility companies to better understand their customers and to plan their services. To make the framework easily accessible, it is developed to be compatible with mobile consumer electronics devices.

energy apps↗
DOE OSTI2013

Carbon/Ternary Alloy/Carbon Optical Stack on Mylar as an Optical Data Storage Medium to Potentially Replace Magnetic Tape

A novel write-once-read-many (WORM) optical stack on Mylar tape is proposed as a replacement for magnetic tape for archival data storage. This optical tape contains a cosputtered bismuth–tellurium–selenium (BTS) alloy as the write layer sandwiched between thin, protective films of reactively sputtered carbon. The composition and thickness of the BTS layer were confirmed by Rutherford Backscattering (RBS) and atomic force microscopy (AFM), respectively. The C/BTS/C stack on Mylar was written to/marked by 532 nm laser pulses. Under the same conditions, control Mylar films without the optical stack were unaffected. Marks, which showed craters/movement of the write material, were characterized by optical microscopy and AFM. The threshold laser powers for making marks on C/BTS/C stacks with different thicknesses were explored. Higher quality marks were made with a 60× objective compared to a 40× objective in our marking apparatus. Finally, the laser writing process was simulated with COMSOL.

Environmental Molecular Sciences Laboratory↗
DOE OSTI2024

Data Generation for Machine Learning Interatomic Potentials and Beyond

The field of data-driven chemistry is undergoing an evolution, driven by innovations in machine learning models for predicting molecular properties and behavior. Recent strides in ML-based interatomic potentials have paved the way for accurate modeling of diverse chemical and structural properties at the atomic level. The key determinant defining MLIP reliability remains the quality of the training data. A paramount challenge lies in constructing training sets that capture specific domains in the vast chemical and structural space. This Review navigates the intricate landscape of essential components and integrity of training data that ensure the extensibility and transferability of the resulting models. We delve into the details of active learning, discussing its various facets and implementations. We outline different types of uncertainty quantification applied to atomistic data acquisition and the correlations between estimated uncertainty and true error. The role of atomistic data samplers in generating diverse and informative structures is highlighted. Furthermore, we discuss data acquisition via modified and surrogate potential energy surfaces as an innovative approach to diversify training data. The Review also provides a list of publicly available data sets that cover essential domains of chemical space.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2013

Quality-Aware Data Management for Large Scale Scientific Applications

Increasingly larger scale simulations are generating an unprecedented amount of output data, causing researchers to explore new `data staging' methods that buffer, use, and/or reduce such data online rather than simply pushing it to disk. Leveraging the capabilities of data staging, this study explores the potential for data reduction via online data compression, first using general compression techniques and then proposing use-specific methods that permit users to define simple data queries that cause only the data identified by those queries to be emitted. Using online methods for code generation and deployment, with such dynamic data queries, end users can precisely identify the quality of information (QoI) of their output data, by explicitly determining what data may be lost vs. retained, in contrast to general-purpose lossy compression methods that do not provide such levels of control. The paper also describes the key elements of a quality-aware data management system (QADMS) for high-end machines enabled by this approach. Initial experimental results demonstrate that QADMS can effectively reduce data movement cost and improve the QoS while meeting the QoI constraint stated by users.

Computer Science↗
DOE OSTI2025

Dataset, Code, and Models for Training Deep Learning Potentials for Low Temperature Plasma-Surface Interactions

This repository contains datasets, training scripts, and finished models, and test simulations used in the development of DeepREBO— a machine-learned interatomic potential trained to emulate the REBO2 empirical potential. The data was generated to study deep potential development for simulations of plasma-surface interactions. It uses an active learning framework, starting from a minimal dataset and iteratively expanding it. Included are those generated datasets, the trained models, and simulations used to evaluate the performance of the training process. This resource supports reproducibility and provides a reference framework for training deep potentials in plasma-surface interaction studies.

active learning↗
DOE OSTI2012

Quality-Aware Data Management for Large Scale Scientific Applications

Increasingly larger scale simulations are generating an unprecedented amount of output data, causing researchers to explore new data staging methods that buffer, use, and/or reduce such data online rather than simply pushing it to disk. Leveraging the capabilities of data staging, this study explores the potential for data reduction via online data compression, first using general compression techniques and then proposing usespecific methods that permit users to define simple data queries that cause only the data identified by those queries to be emitted. Using online methods for code generation and deployment, with such dynamic data queries, end users can precisely identify the quality of information (QoI) of their output data, by explicitly determining what data may be lost vs. retained, in contrast to general-purpose lossy compression methods that do not provide such levels of control. The paper also describes the key elements of a quality-aware data management system (QADMS) for highend machines enabled by this approach. Initial experimental results demonstrate that QADMS can effectively reduce data movement cost and improve the QoS while meeting the QoI constraint stated by users.

Zou, Hongbo↗
DOE OSTI2025

MS25: Materials Science-Focused Benchmark Data Set for Machine Learning Interatomic Potentials

Here, we present MS25, a benchmark data set for evaluating machine learning interatomic potentials (MLIPs) across diverse materials-relevant systems including MgO surfaces, liquid water, zeolites, a catalytic Pt surface reaction, high-entropy alloys (HEAs), and disordered Zr-oxides. Five MLIP architectures (MACE, NequIP, Allegro, MTP, and Torch-ANI) are trained and tested, focusing not only on traditional metrics (energies, forces, and stresses) but also explicitly validating derived physical observables such as lattice constants, volumes, and reaction barriers. We find that most models reach comparable accuracy on standard error metrics across the simple systems, although equivariant MLIPs offer 1.5–2× improvements over nonequivariant MLIPs in energy and force error for structurally complex or compositionally disordered environments such as HEAs and Zr–O systems. Our analysis highlights that low errors in energy and force predictions do not guarantee reliable observables, emphasizing the necessity of explicit validation. We demonstrate limitations in cross-framework transferability, as models trained on one zeolite framework (CHA) fail to reliably generalize to predictions of structurally distinct frameworks (e.g., MFI). Size-extensive tests show some dependence on system size for MgO, resulting from forced periodicity. The HEA and Zr–O data sets are identified as challenging tests for future benchmarks and MLIP model architecture developments as they show significant differentiation in error between MLIP architectures and are still relatively difficult at 1000 training images. Moving forward, we recommend that benchmarking efforts shift their focus from marginal accuracy improvements in energy and force errors toward identifying and understanding model failure modes, rigorously assessing transferability, and evaluating how their errors affect observable predictions. For researchers looking to choose an MLIP architecture, we suggest selecting equivariant MLIP architectures if the complexity of the system is a challenge. For simple materials problems, auxiliary features such as integration with molecular dynamics engines, trade-offs between computational data set generation cost vs MLIP inference speed, and framework integration may play a more important decision factor than small differences in error metrics that are unlikely to matter for production-level research.

chemical structure↗
DOE OSTI2025

Data for "A 13-year record indicates differences in the duration and depth of soil carbon accrual among potential bioenergy crops"

Data sets for material included in "A 13-year record indicates differences in the duration and depth of soil carbon accrual among potential bioenergy crops" by Kantola et al., 2025, in Global Change Biology Bioenergy. Data include soil organic carbon (SOC), carbon stable isotope ratios, annual belowground biomass, and annual post-harvest litter for four crops, maize/soybean, miscanthus, switchgrass, and prairie, between 2008 and 2021.

bioenergy crops↗
DOE OSTI2014

Analysis of shockless dynamic compression data on solids to multi-megabar pressures: Application to tantalum

Magnetically-driven, planar shockless-compression experiments to multi-megabar pressures were performed on tantalum samples using a stripline target geometry. Free-surface velocity waveforms were measured in 15 cases; nine of these in a dual-sample configuration with two samples of different thicknesses on opposing electrodes, and six in a single-sample configuration with a bare electrode opposite the sample. Details are given on the application of inverse Lagrangian analysis (ILA) to these data, including potential sources of error. The most significant source of systematic error, particularly for single-sample experiments, was found to arise from the pulse-shape dependent free-surface reflected wave interactions with the deviatoric-stress response of tantalum. This could cause local, possibly temporary, unloading of material from a ramp compressed state, and thus multi-value response in wave speed that invalidates the free-surface to in-material velocity mapping step of ILA. By averaging all 15 data sets, a final result for the principal quasi-isentrope of tantalum in stress-strain was obtained to a peak longitudinal stress of 330 GPa with conservative uncertainty bounds of ±4.5% in stress. The result agrees well with a tabular equation of state developed at Los Alamos National Laboratory.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2024

Data for Harnessing the Potential of Oilcane Waste Mud for Recovering Biobased Waxes

Oilcane is an engineered sugarcane with the ability to hyper-accumulate vegetative lipids. It is processed to obtain juice and bagasse as a potential substrate for the production of biofuels and biochemicals. The juice comprises solid particles that are separated as waste mud before the fermentation of the juice. In this study, the oilcane waste mud (OWM) generated from 1000 liters of oilcane juice was quantified and evaluated as a potential resource for recovering biobased waxes. Hexane and ethyl acetate were evaluated as two different solvents for extracting waxes from OWM followed by its purification using acetone. The extracted biobased wax samples were characterized for their chemical and thermal profiles which were then compared with commercial natural waxes. Detailed mass balance shows that 53.6 ± 2.6 kg (dry basis) of solid OWM gets generated upon processing 1000 L (~1068 kg) of oilcane juice. Hexane and ethyl acetate led to a crude wax yield of 25.6 ± 0.2% and 16.6 ± 0.4% (wt/wt, dry basis) respectively from OWM at the end of 8 h. The relative purification of the wax samples was reported in the range of 58%–65% (wt/wt). The purified OWM wax has a melting point of 74.7°C. The waste mud was valorized as a source of biobased waxes with characteristic chemical and thermal profiles comparable to commercial natural waxes (carnauba and beeswax). Considering the decline in the supply of petroleum wax in the future coupled with the switch to “greener” alternative products by consumers, OWM could be a valuable source of natural wax in the industrial sector reducing the dependence on petroleum waxes. Eventually, recovering biobased wax as a co-product from OWM would bring in an additional stream of revenue leading to the development of a zero-waste biorefinery based on bioenergy crops.

Biomass Analytics↗
DOE OSTI2019

SSL R&D Data Mining - Screening for Potential Field Validation Candidates

In 2019, Pacific Northwest National Laboratory conducted a review for the U.S. Department of Energy of the projects that have been awarded research and development (R&D) funding for the advancement of solid state lighting (SSL) technologies. The projects were reviewed with the intent of finding research that is now utilized in commercially available products and to identify potential field validation project candidates. This paper discusses the research approach as well as what we learned regarding the commercial availability of the technologies that were advanced with DOE R&D funding. PNNL reviewed 82 projects from the DOE-supported SSL R&D portfolio and identified 11 projects to evaluate for potential laboratory and field validations in commercial buildings. For these 11 projects, reports were reviewed and interviews were scheduled with principal investigators to discuss commercialization status. Six projects were identified as holding promise as field validation candidates; four are being commercialized, while the remaining two have products or systems using some of the advancements that were developed during the funding period.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2023

A Review of Candidates for a Validation Data Set for High-Assay Low-Enrichment Uranium Fuels

Many advanced reactor concept designs rely on high-assay low-enriched uranium (HALEU) fuel, enriched up to approximately 19.75% 235 U by weight. Efforts are underway by the US government to increase HALEU production in the United States to meet anticipated needs. However, very few data exist for validation of computational models that include HALEU, beyond a few fresh fuel benchmark specifications in the International Reactor Physics Experiment Evaluation Project. Nevertheless, there are other data with potential value available for developing into quality benchmarks for use in data- and software-validation efforts. This paper reviews the available evaluated HALEU fuel benchmarks and some of the potentially relevant benchmarks for fresh highly enriched uranium. It then introduces experimental data for HALEU fuel irradiated at Idaho National Laboratory, from relatively recent irradiation programs at the Advanced Test Reactor. Such data should be evaluated and, if valuable, collected into detailed benchmark specifications to meet the needs of HALEU-based reactor designers.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2016

SU-C-204-07: Radiation Therapy as a Potential Treatment for Obesity: Initial Data from a Preclinical Investigation

Purpose: To evaluate the feasibility of Yttrium-90 (90Y) radionuclide therapy as a potential treatment for obesity in a porcine model. As the only appetite-stimulating hormone, localized targeting of ghrelin-producing X/A cells in the fundus of the stomach using 90Y may reduce serum ghrelin levels and decrease hunger. Methods: Under approval of the University of Tennessee IACUC, 8 young female pigs aged 12–13 weeks and weighing 21.8–28.1 Kg were included in this study. Six animals underwent transfemoral angiography as part of a two-day procedure involving: (1) infusion of 99mTc-MAA, followed by nuclear scintigraphy and contrast-enhanced CT for treatment-planning and (2) administration of resin 90Y microspheres into the stomach fundus. Calibrated 90Y activities were infused into the main left gastric and the gastric artery arising from the splenic to yield predetermined fundal absorbed doses. Control animals underwent a sham procedure with saline and contrast. Weekly animal weight and serum ghrelin were measured along with post-euthanasia histologic analyses of mucosal integrity and ghrelin immunoreactive cell-density. Results: 90Y radioembolization was administered to six pigs in dosages from 46.3 to 105.1 MBq resulting in average fundal absorbed doses between 35.5 and 91.9 Gy. No animal showed any signs of pain or GI complication through the duration of the study. Ghrelin immunoreactive cell-density was significantly lower in treated vs. control animals in both the stomach fundus (13.5 vs 34.8, P < 0.05) and body (11.2 vs 19.8, P < 0.05). A trend towards decreased weight gain in treated animals as well as a decrease in explanted stomach volume was also noted. Conclusion: The safety and technical feasibility of radiation therapy using 90Y radioembolization as a potential treatment for obesity has been demonstrated at fundal absorbed doses over 90 Gy. Preliminary data is suggestive of short-term safety and potential efficacy, however, further animal studies are required. Project was funded by SIRTex medical ltd.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2022

Reviews and syntheses: The promise of big diverse soil data, moving current practices towards future potential

Abstract. In the age of big data, soil data are more available and richer than ever, but – outside of a few large soil survey resources – they remain largely unusable for informing soil management and understanding Earth system processes beyond the original study. Data science has promised a fully reusable research pipeline where data from past studies are used to contextualize new findings and reanalyzed for new insight. Yet synthesis projects encounter challenges at all steps of the data reuse pipeline, including unavailable data, labor-intensive transcription of datasets, incomplete metadata, and a lack of communication between collaborators. Here, using insights from a diversity of soil, data, and climate scientists, we summarize current practices in soil data synthesis across all stages of database creation: availability, input, harmonization, curation, and publication. We then suggest new soil-focused semantic tools to improve existing data pipelines, such as ontologies, vocabulary lists, and community practices. Our goal is to provide the soil data community with an overview of current practices in soil data and where we need to go to fully leverage big data to solve soil problems in the next century.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2014

TU-C-12A-02: Development of a Multiparametric Statistical Response Map for Quantitative Imaging

Purpose: Quantitative imaging biomarkers (QIB) are becoming increasingly utilized in early phase clinical trials as a means of non-invasively assessing treatment response and associated response heterogeneity. The aim of this study was to develop a flexible multiparametric statistical framework to predict voxel-by-voxel response of several potential MRI QIBs. Methods: Patients with histologically proven glioblastomas (n=11) were treated with chemoradiation (with/without bevacizumab) and underwent one baseline and two mid-treatment (3–4wks) MRIs. Dynamic contrast-enhanced (3D FSPGR, 6.3sec/phase, 0.1 mmol/kg Gd-DTPA), dynamic susceptibility contrast (2D GRE-EPI, 1.5sec/phase, 0.2mmol/kg Gd-DTPA), and diffusion tensor (2D DW-EPI, b=0, 1200 s/mm{sup 2}, 27 directions) imaging acquisitions were obtained during each study. Mid-treatment and pre-treatment images were rigidly aligned, and regions of partial response (PR), stable disease (SD), and progressive disease (PD) were contoured in consensus by two experienced radiation oncologists. Voxels in these categories were used to train ordinal (PR<SD<PD) and logistic (PR/SD<PD) models incorporating the apparent diffusion coefficient, fractional anisotropy, Ktrans, ve, vp, and relative cerebral blood volume imaging biomarkers, as well as treatment type. Leave-one-out cross-validation was performed at the patient level to assess model prediction accuracy. Results: Ordinal regression resulted in model prediction accuracies of 60% (PR), 0% (SD), 81% (PD), and 69% (overall), with coefficients of variation (COV) of 9.4%, 9.6%, and 23.6%, respectively. Logistic regression resulted in accuracies of 82.0% (PR/SD), 46.2% (PD), and 76.2% (overall) with COVs of 22.4%, 45.7%, and 23.8%, respectively. Conclusion: Despite limited patient numbers, this feasibility pilot study demonstrates that ordinal and logistic regression models potentially provide a flexible statistical framework for incorporating longitudinal multiparametric quantitative imaging data and, for these particular data, can potentially distinguish PR from PD. Additional training data exhibiting a full range of treatment responses is expected to allow prediction of all three treatment outcomes. Moreover, these models are extensible to multi-modality, multiparametric analyses.

60 APPLIED LIFE SCIENCES↗