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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 541 records · Page 30
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

HTESP (High-throughput electronic structure package): A package for high-throughput ab initio calculations

High-throughput ab initio calculations are the indispensable parts of data-driven discovery of new materials with desirable properties, as reflected in the establishment of several online material databases. The accumulation of extensive theoretical data through computations enables data-driven discovery by constructing machine learning and artificial intelligence models to predict novel compounds and forecast their properties. Efficient usage and extraction of data from these existing online material databases can accelerate the next stage materials discovery that targets different and more advanced properties, such as electron–phonon coupling for phonon-mediated superconductivity. However, extracting data from these databases, generating tailored input files for different ab initio calculations, performing such calculations, and analyzing new results can be demanding tasks. Here, in this work, we introduce a software package named “HTESP” (High-Throughput Electronic Structure Package) written in Python and Bash languages, which automates the entire workflow including data extraction, input file generation, calculation submission, result collection and plotting. Our HTESP will help speed up future computational materials discovery processes.

36 MATERIALS SCIENCE↗
DOE OSTI2015

Nexus: a modular workflow management system for quantum simulation codes

The management of simulation workflows is a significant task for the individual computational researcher. Automation of the required tasks involved in simulation work can decrease the overall time to solution and reduce sources of human error. A new simulation workflow management system, Nexus, is presented to address these issues. Nexus is capable of automated job management on workstations and resources at several major supercomputing centers. Its modular design allows many quantum simulation codes to be supported within the same framework. Current support includes quantum Monte Carlo calculations with QMCPACK, density functional theory calculations with Quantum Espresso or VASP, and quantum chemical calculations with GAMESS. Users can compose workflows through a transparent, text-based interface, resembling the input file of a typical simulation code. A usage example is provided to illustrate the process.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Association Kinetics for Perfluorinated n -Alkyl Radicals

Radical-radical reaction channels are important in the pyrolysis and oxidation chemistry of perfluoroalkyl substances (PFAS). In particular, unimolecular dissociation reactions within unbranched n-perfluoroalkyl chains, and their corresponding reverse barrierless association reactions, are expected to be significant contributors to the gas-phase thermal decomposition of families of species such as perfluorinated carboxylic acids and perfluorinated sulfonic acids. Unfortunately, experimental data for these reactions are scarce and uncertain. Furthermore, obtaining reliable theoretical predictions for such reactions is a laborious and computationally intensive task. Here, in this work, the chemical kinetics of the various association/decomposition reactions producing/decomposing the C 2 -C 4 series of unbranched n-perfluoroalkanes (C 2 F 6 , C 3 F 8 , and C 4 F 10 ) are examined using state-of-the-art ab initio transition-state-theory-based master-equation calculations. The variable-reaction-coordinate transition-state theory (VRC-TST) formalism is employed in computing the microcanonical and canonical rates for the association reactions. Reaction thermochemistry is obtained via composite quantum chemistry calculations and the laddering of error-canceling reaction schemes via a connectivity-based hierarchy approach employing ANL1/ANL0-style reference energies. Lennard-Jones collision model parameters for the considered systems were estimated by a direct dynamics approach, and collisional energy transfer parameters were obtained from analogies to systems of similar size and heavy-atom connectivity. A one-dimensional master equation approach was used to convert the microcanonical rate coefficients from the VRC-TST analysis into temperature- and pressure-dependent rate constants for the association reactions and the reverse dissociation reactions. The data are reported in standardized formats for usage in comprehensive chemical kinetic models for PFAS thermal destruction.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2026

Search for active and inactive ion insertion sites in organic crystalline materials

The position of mobile active and inactive ions, specifically ion insertion sites, within organic crystals, significantly affects the properties of organic materials used for energy storage and ionic transport. Identifying the positions of these atomic (and ionic) sites in an organic crystal is challenging, especially when the element has low X-ray scattering power, such as lithium (Li) and hydrogen, which are difficult to detect by powder X-ray diffraction. First-principles calculations, exemplified by density functional theory (DFT), are very practical for confirming the relative stability of ion positions in materials. However, the lack of effective strategies to identify ion sites in these organic crystalline frameworks renders this task extremely challenging. This work presents two algorithms: the (i) efficient location of ion insertion sites from extrema in electrostatic local potential and charge density (ELIISE), and the (ii) ElectRostatic InsertioN (ERIN), which leverage charge density and electrostatic potential fields accessed from first-principles calculations, combined with the Simultaneous Ion Insertion and Evaluation (SIIE) workflow. SIIE inserts all ions simultaneously—to determine ion positions in organic crystals. We demonstrate that these methods accurately reproduce known ion positions in 16 organic materials and identify previously overlooked low-energy sites in tetralithium 2,6-naphthalenedicarboxylate (Li4NDC), an organic electrode material, highlighting the importance of inserting all ions simultaneously, as in the SIIE workflow. These algorithms are also integrated with off-the-shelf machine learning potentials, yielding promising results comparable to first-principles findings.

Gopidi, Harshan Reddy [Argonne National Laboratory↗
DOE OSTI2017

Sensor for Measuring Hydrogen Partial Pressure in Parabolic Trough Power Plant Expansion Tanks

The National Renewable Energy Laboratory and Acciona Energy North America are working together to design and implement a process system that provides a permanent solution to the issue of hydrogen buildup at parabolic trough power plants. We are pursuing a method that selectively removes hydrogen from the expansion tanks that serve as reservoirs for the heat transfer fluid (HTF) that circulates in the collector field and power block components. Our modeling shows that removing hydrogen from the expansion tanks at a design rate reduces and maintains dissolved hydrogen in the circulating HTF to a selected target level. Our collaborative work consists of several tasks that are needed to advance this process concept to a development stage, where it is ready for implementation at a commercial power plant. Our main effort is to design and evaluate likely process-unit operations that remove hydrogen from the expansion tanks at a specified rate. Additionally, we designed and demonstrated a method and instrumentation to measure hydrogen partial pressure and concentration in the expansion-tank headspace gas. We measured hydrogen partial pressure in the headspace gas mixture using a palladium-alloy membrane, which is permeable exclusively to hydrogen. The membrane establishes a pure hydrogen gas phase that is in equilibrium with the hydrogen in the gas mixture. We designed and fabricated instrumentation, and demonstrated its effectiveness in measuring hydrogen partial pressures over a range of three orders of magnitude. Our goal is to install this instrument at the Nevada Solar One power plant and to demonstrate its effectiveness in measuring hydrogen levels in the expansion tanks under normal plant operating conditions.

14 SOLAR ENERGY↗
DOE OSTI2019

Context-Enriched Identification of Particles with a Convolutional Network for Neutrino Events

Particle detectors record the interactions of subatomic particles and their passage through matter. The identification of these particles is necessary for in-depth physics analysis. While particles can be identified by their individual behavior as they travel through matter, the full context of the interaction in which they are produced can aid the classification task substantially. We have developed the first convolutional neural network for particle identification which uses context information. This is also the first implementation of a four-tower siamese-type architecture both for separation of independent inputs and inclusion of context information. The network classifies clusters of energy deposits from the NOvA neutrino detectors as electrons, muons, photons, pions, and protons with an overall efficiency and purity of 83.3% and 83.5%, respectively. We show that providing the network with context information improves performance by comparing our results with a network trained without context information.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2025

Leveraging differentiable programming in the inverse problem of neutron stars

Neutron stars (NSs) probe the high-density regime of the nuclear equation of state (EOS). However, inferring the EOS from observations of NSs is a computationally challenging task. Here, in this work, we efficiently solve this inverse problem by leveraging differential programming in two ways. First, we enable full Bayesian inference in under one hour of wall time on a GPU by using gradient-based samplers, without requiring pretrained machine learning emulators. Moreover, we demonstrate efficient scaling to high-dimensional parameter spaces. Second, we introduce a novel gradient-based optimization scheme that recovers the EOS of a given NS mass-radius curve. We demonstrate how our framework can reveal consistencies or tensions between nuclear physics and astrophysics. First, we show how the breakdown density of a metamodel description of the EOS can be determined from NS observations. Second, we demonstrate how degeneracies in EOS modeling using nuclear empirical parameters can influence the inverse problem during gradient-based optimization. Looking ahead, our approach opens up new theoretical studies of the relation between NS properties and the EOS, while effectively tackling the data analysis challenges brought by future detectors.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2025

Uncertainty Visualization of Critical Points of 2D Scalar Fields for Parametric and Nonparametric Probabilistic Models

This paper presents a novel end-to-end framework for closed-form computation and visualization of critical point uncertainty in 2D uncertain scalar fields. Critical points are fundamental topological descriptors used in the visualization and analysis of scalar fields. The uncertainty inherent in data (e.g., observational and experimental data, approximations in simulations, and compression), however, creates uncertainty regarding critical point positions. Uncertainty in critical point positions, therefore, cannot be ignored, given their impact on downstream data analysis tasks. Here, in this work, we study uncertainty in critical points as a function of uncertainty in data modeled with probability distributions. Although Monte Carlo (MC) sampling techniques have been used in prior studies to quantify critical point uncertainty, they are often expensive and are infrequently used in production-quality visualization software. We, therefore, propose a new end-to-end framework to address these challenges that comprises a threefold contribution. First, we derive the critical point uncertainty in closed form, which is more accurate and efficient than the conventional MC sampling methods. Specifically, we provide the closed-form and semianalytical (a mix of closed-form and MC methods) solutions for parametric (e.g., uniform, Epanechnikov) and nonparametric models (e.g., histograms) with finite support. Second, we accelerate critical point probability computations using a parallel implementation with the VTK-m library, which is platform portable. Finally, we demonstrate the integration of our implementation with the ParaView software system to demonstrate near-real-time results for real datasets.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2013

Examination of an optical transmittance test for photovoltaic encapsulation materials

The optical transmittance of encapsulation materials is a key characteristic for their use in photovoltaic (PV) modules. Changes in transmittance with time in the field affect module performance, which may impact product warranties. Transmittance is important in product development, module manufacturing, and field power production (both immediate and long-term). Therefore, an international standard (IEC 62788-1-4) has recently been proposed by the Encapsulation Task-Group within the Working Group 2 (WG2) of the International Electrotechnical Commission (IEC) Technical Committee 82 (TC82) for the quantification of the optical performance of PV encapsulation materials. Existing standards, such as ASTM E903, are general and more appropriately applied to concentrated solar power than to PV. Starting from the optical transmittance measurement, the solar-weighted transmittance of photon irradiance, yellowness index (which may be used in aging studies to assess durability), and ultraviolet (UV) cut-off wavelength may all be determined using the proposed standard. The details of the proposed test are described. The results of a round-robin experiment (for five materials) conducted at seven laboratories to validate the test procedure using representative materials are also presented. For example, the Encapsulation Group actively explored the measurement requirements (wavelength range and resolution), the requirements for the spectrophotometer (including the integrating sphere and instrument accessories, such as a depolarizer), specimen requirements (choice of glass-superstrate and -substrate), and data analysis (relative to the light that may be used in the PV application). The round-robin experiment identified both intra- and inter-laboratory instrument precision and bias for five encapsulation materials (encompassing a range of transmittance and haze-formation characteristics).

14 SOLAR ENERGY↗
DOE OSTI2017

Predicting Coculture Substrate Depletion (PCSD) v1

(based on Erbilgin et al., BMC Bioinformatics, 2017, 18:57). Mixed cultures of microbial species are increasingly used to carry out a specific task in lieu of engineering a single microbe to do the same task. However, knowing how different species' metabolisms will integrate to reach a desired outcome is a difficult problem that has been studied in great detail--but only using steady-state models. Mixed cultures used for biotechnology processes, as well as microbial communities that exist in nature and crop agriculture, are metabolically dynamic systems. Studying the temporal substrate utilization by individuals is individuals in mixed cultures is an important first step in developing approaches to better model these biochemical processes and microbial communities found in nature. The 2017-004 software is comprised of four custom iPython notebooks that were used for all computational tasks in the experimental work described in "Dynamic substrate preferences predict metabolic properties of a simple microbial consortium" (Erbilgin et al., BMC Bioinformatics, 2017). The results indicate that a significant portion of a model community's overall metabolism can be predicted based on the metabolism of the individuals. The results further suggest that, when the mixed culture's substrate depletion significantly deviates from such a prediction, this may highlight potential metabolic pathways affected by species-species microbial interactions, leading to hypotheses about how a particular microbial community's structure and function may be modulated to perform a particular biotechnological goal.

Bowen, Benjamin↗
DOE OSTI2017

Measuring, Predicting and Visualizing Short-Term Change in Word Representation and Usage in VKontakte Network

Language in social media is extremely dynamic: new concepts emerge, trend and disappear, while the meaning of existing concepts can fluctuate over time. This work addresses a novel task of measuring and predicting meaning shift – context or semantic representation change from surface level dynamics e.g., word frequencies observed in social media streams. We study the relationship between language dynamics and short-term context change on a corpus of VKontakte posts collected during the Russia-Ukraine crisis in 2014 – 2015. We train a neural network model to predict short-term shifts in meaning from previous meaning as well as word dynamics. We demonstrate that short-term representation shift can be accurately predicted up to several weeks in advance. Moreover, we visualize this short-term context shift for the example words and demonstrate the practical side of our approach to discover and track meaning of newly emerging terms in social media during crises.

distributional semantics, word embeddings, social ↗
DOE OSTI2013

Fujitsu and BEA Collaboration on Cyber Enabled Smart Grid Performance (CRADA Final report)

In conclusion of this CRADA, this letter documents the actions taken in regard to this collaboration. While the statement of work overviews several joint tasks associated with this effort, the availability of resources on the FLA side prevented any collaboratory development of shared Intellectual Property. Therefore, no patents, copyrights or publications resulted from this effort. However, several meetings at the INL and FLA offices maintained a good relationship and sharing of other potential areas of collaboration in the area of building/community energy management and cyber security.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2016

Liquid Organic Battery Development: Cooperative Research and Development Final Report, CRADA Number CRD-14-540

Battery electric vehicles (BEV) have the potential to significantly reduce consumption of gasoline and emission of greenhouse gases. However, the commercial success of mass-market, long-range BEVs requires battery technology with a challenging combination of technical metrics -- specific energy, safety, fast recharge capability, cycle life, and cost. The NREL team proposes a robust, liquid-phase battery design utilizing a high-energy organic redox couple capable of decoupling these metrics via electrode exchange to provide the necessary combination of performance characteristics. The overall objective of this project is to demonstrate a functioning prototype and determine its ability to meet RANGE performance targets in large-scale production. Three main tasks described below will work towards this goal with the individual objectives of (1) identifying a robust, high-performance redox couple-solvent-additive combination, (2) designing and demonstrating a functional cell, and (3) analyzing the concept's potential performance and cost in future mass-production scenarios.

25 ENERGY STORAGE↗
DOE OSTI2017

Analysis Results for Sub-Samples of SRS Tanks 30, 32, and 39 to Support Evaluations of the I-129 Inventory

In order to appropriately model and predict the chemical integrity and performance of cementitious materials used for waste immobilization at the Savannah River Site (SRS), it is critical to understand the I-129 solubility and distribution within the tank farm. Iodine in radioactive waste and in environmental media is typically highly mobile and long lived. Iodine is ubiquitous in SRS tank waste and waste forms. The iodine is assumed to be soluble and present at low levels in Performance Assessments (PAs) for SRS Tank Farms, and is one of the dose drivers in the PAs for both the SRS Salt Disposal Facility (SDF) and the H-Area Tank Farm (HTF). Analysis of tank waste samples is critical to understanding the Tank Farm iodine inventory and reducing disposal uncertainty. Higher than expected iodine levels have recently been observed in residual solids isolated from some SRS tanks prior to closure, indicating uncertainty regarding the chemical species involved. If the iodine inventory uncertainty is larger than anticipated, future work may be necessary to reduce the uncertainty. This memorandum satisfies a portion of the work scope identified in Task Plan SRNL-RP-2016-00651. A separate memorandum issued previously, reported historical unpublished I-129 data, a significant portion of which was below detectable analytical limits. This memorandum includes iodine and general chemical analysis results for six archived SRNL samples which were previously reported to have I-129 concentrations below detectable limits. Lower sample dilution factors were used for the current analyses in order to obtain concentrations above detection. The samples analyzed included surface and depth samples from SRS tanks 30, 32, and 39.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2018

Evaluation of Simple Chemical Interactions in the Defense Waste Processing Facility (DWPF) Chemical Process Cell (CPC) Under the Glycolic Acid Flowsheet

This report describes results from testing developed for the increased understanding of simple chemical interactions relevant to the processing and vitrification of radioactive waste at the Savannah River Site (SRS) in Aiken, SC. This work is outlined in task 3 of the Technical Task Request X-TTR-S-00024, Rev. 0 and task 4 of the Task Technical & Quality Assurance Plan SRNL- RP-2014-01183 Rev. 0. The testing was conducted from May 2016 through February 2017 at the Aiken County Technology Laboratory (ACTL) and performed by Savannah River National Laboratory (SRNL) personnel. This testing involved the investigation of simple reactions and interactions of glycolic or nitric acids with combinations of sodium nitrite, manganese dioxide, mercuric oxide, and noble metals (silver, palladium, rhodium, and ruthenium). The aim of this testing was to better understand the reactions and chemical phenomena previously observed in the Sludge Receipt and Adjustment Tank (SRAT) cycle of the Chemical Process Cell (CPC) in the Defense Waste Processing Facility (DWPF).

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2020

Predictive Modeling and Diagnostic Monitoring of Extreme Science Workflows (Final Report)

This proposal addresses a critical issue of performance prediction identified in the report from the ASCR \Computational Modeling of Big Networks (COMBINE)" workshop: "end-to-end performance is not predictable due to a variety of factors. Even when some performance forecasts or predictions can be made, they often cannot explain the reasons why some predictions fail." We will develop new analytical models to predict the end-to-end performance of scientific workflows on DOE computing infrastructures, and use simulations and experimentation to validate and refine these models, as well as to pinpoint the sources of model inaccuracy. We will also use these models to help diagnose application and infrastructure problems, and to adapt the system based on this diagnosis. This section provides background in the areas relevant to the proposed work. RPI’s specific tasks within the Panorama project are as follows: (1) Develop Aspen-Simulation interface for Workflow Model Driven Simulation. (2) Validate manual performance models of two target workflow scenarios with empirical measurement and simulation. (3) Extend ROSS-Aspen API to simulate workflow descriptions when required. (4) Validate Aspen performance models of two target workflow scenarios with automatic performance model empirical measurement and simulation. (5) Design and implement final system to automatically generate Aspen performance models from workflow descriptions (including methods to compensate for limitations of Aspen analytical models). (6) Validate improved Aspen performance models with target workflow on production infrastructure. To date, all the project milestones assigned to us where reached within the best of our abilities over the course of the project performance period. Below describes the key outcome from our collaborative research in a system named, Durango .

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2020

International Collaborations Activities on Disposal in Argillite R&D: Bentonite Barrier Characterization Studies and Modeling Investigations

This interim report is an update of the report Jove Colon et al. (2019; M4SF-19SN010301091) describing international collaboration activities pertaining to FEBEX-DP and DECOVALEX19 Task C projects. Although work on these two international repository science activities is no longer continuing by the international partners, investigations on the collected data and samples is still ongoing. Descriptions of these underground research laboratory (URL) R&D activities are given in Jové Colón et al. (2018; 2019) but will repeated here for completeness. The 2019 status of work conducted at Sandia National Laboratories (SNL) on these two activities is summarized along with other international collaboration activities in Birkholzer et al. (2019).

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗