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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 595 records · Page 33
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

Correlating dynamic microstructure to observed color in electrophoretic displays via in situ small-angle x-ray scattering

Electrophoretic deposition (EPD) is an industrially relevant and scalable technique used to form particle deposits from colloidal suspensions. Highly concentrated particle suspensions generally prevent real-time in situ microscopy observations which limit the characterization of EPD films to ex situ, or postprocessed, laboratory techniques. For dynamic systems, such as tunable amorphous photonic crystals (APCs), only reversible deposits are formed during the EPD process. Since reversible deposits cannot be characterized with standard ex situ methods, the particle-particle and particle-field interactions that govern the displayed color and crystallinity of these systems are not well understood. In this paper, we present in situ small-angle x-ray scattering and UV-Vis techniques for measuring both the structural and optical response of an APC under applied electric fields. Furthermore, we also develop a computational model based on colloidal interactions to explain the observed change in the interparticle spacing of APCs due to the applied electric field which correlates to displayed color. Ultimately, this work provides a new in situ characterization method that could be expanded for other dynamic, tunable colloidal systems.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Vulnerability Assessments for Power-Electronics-Based Smart Grids

Here, in this paper, a novel method is proposed to evaluate the cyber security of the power-electronics-based smart grids (PESG). The proposed method considers the performance and stability of both the individual inverter and the grid. To our knowledge, this is a first attempt to evaluate the performance and stability of PESG due to cyber attacks. We first develop impedance-based modeling and cyber-attack modeling for PESG. Then we propose innovative two security criteria to evaluate the security of PESG, including stability-based and metrics-based. For metrics-based criteria, we propose to use both total harmonic distortion (THD) and space phasor model (SPM) to evaluate the inverter performance. The simulation results with a two-inverter-based power grid verify the validity and accuracy of the proposed security evaluation method. Results have shown that the performance and stability of PESG are significantly affected by cyber attacks, and thus there is indeed a need to further study cyber security issues of PESG.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2013

Assaying Used Nuclear Fuel Assemblies Using Lead Slowing-Down Spectroscopy and Singular Value Decomposition

This study investigates the use of a Lead Slowing-Down Spectrometer (LSDS) for the direct and independent measurement of fissile isotopes in light-water nuclear reactor fuel assemblies. The current study applies MCNPX, a Monte Carlo radiation transport code, to simulate the measurement of the assay of the used nuclear fuel assemblies in the LSDS. An empirical model has been developed based on the calibration of the LSDS to responses generated from the simulated assay of six well-characterized fuel assemblies. The effects of self-shielding are taken into account by using empirical basis vectors calculated from the singular value decomposition (SVD) of a matrix containing the self-shielding functions from the assay of assemblies in the calibration set. The performance of the empirical algorithm was tested on version 1 of the Next-Generation Safeguards Initiative (NGSI) used fuel library consisting of 64 assemblies, as well as a set of 27 diversion assemblies, both of which were developed by Los Alamos National Laboratory. The potential for direct and independent assay of the sum of the masses of Pu-239 and Pu-241 to within 2%, on average, has been demonstrated.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2022

Genome-wide DNA methylation patterns harbour signatures of hatchling sex and past incubation temperature in a species with environmental sex determination

Conservation of thermally sensitive species depends on monitoring organismal and population-level responses to environmental change in real time. Epigenetic processes are increasingly recognized as key integrators of environmental conditions into developmentally plastic responses, and attendant epigenomic data sets hold potential for revealing cryptic phenotypes relevant to conservation efforts. Here, we demonstrate the utility of genome-wide DNA methylation (DNAm) patterns in the face of climate change for a group of especially vulnerable species, those with temperature-dependent sex determination (TSD). Due to their reliance on thermal cues during development to determine sexual fate, contemporary shifts in temperature are predicted to skew offspring sex ratios and ultimately destabilize sensitive populations. Using reduced-representation bisulphite sequencing, we profiled the DNA methylome in blood cells of hatchling American alligators (Alligator mississippiensis), a TSD species lacking reliable markers of sexual dimorphism in early life stages. We identified 120 sex-associated differentially methylated cytosines (DMCs; FDR < 0.1) in hatchlings incubated under a range of temperatures, as well as 707 unique temperature-associated DMCs. We further developed DNAm-based models capable of predicting hatchling sex with 100% accuracy (in 20 training samples and four test samples) and past incubation temperature with a mean absolute error of 1.2°C (in four test samples) based on the methylation status of 20 and 24 loci, respectively. Though largely independent of epigenomic patterning occurring in the embryonic gonad during TSD, DNAm patterns in blood cells may serve as nonlethal markers of hatchling sex and past incubation conditions in conservation applications. These findings also raise intriguing questions regarding tissue-specific epigenomic patterning in the context of developmental plasticity.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2026

Energy Impact of Radiative Cooling Paints in Warehouses Under Various United States Climates

Although radiative cooling research is widely found in the literature, no comprehensive study has yet been conducted on the impact of novel radiant cooling (>0.91 reflectance) on the energy efficiency of warehouses. Here, in this work, we develop three building models based on a Department of Energy prototype warehouse model using trnsys, representing a typical warehouse with a black roof, a typical warehouse with a white roof, and a warehouse with novel radiative cooling (RC) paint on its roof. These models are run for 15 different cities, each representative of a different ASHRAE climate zone, to better understand the impact of RC in many different climates. It was found that an RC-coated roof in a warehouse could reduce the building's annual heating, ventilation, and air conditioning (HVAC) loads by up to 14.11 kWh/m 2 of the roof area compared to a black roof, resulting in a maximum reduction in energy costs of 0.55 $\$$/m 2 or $\$$2646/year for a large 4835 m 2 warehouse. Similarly, replacing the typical white roof coating with an RC coating could reduce the warehouse's energy consumption by up to 8.17 kWh/ m 2 of roof area, thus reducing energy costs by as much as 0.29 $\$$/m 2 or $\$$1386/year for a 4835 m 2 warehouse. In addition, applying RC paint to an unconditioned warehouse could reduce the building's ASHRAE Standard 55 indoor temperature exceedance by up to 1330 h/year compared to a black roof and up to 532 h/year compared to a white roof.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2015

Modeling precursor diffusion and reaction of atomic layer deposition in porous structures

Atomic layer deposition (ALD) is a technique for depositing thin films of materials with a precise thickness control and uniformity using the self-limitation of the underlying reactions. Usually, it is difficult to predict the result of the ALD process for given external parameters, e.g., the precursor exposure time or the size of the precursor molecules. Therefore, a deeper insight into ALD by modeling the process is needed to improve process control and to achieve more economical coatings. In this paper, a detailed, microscopic approach based on the model developed by Yanguas-Gil and Elam is presented and additionally compared with the experiment. Precursor diffusion and second-order reaction kinetics are combined to identify the influence of the porous substrate's microstructural parameters and the influence of precursor properties on the coating. The thickness of the deposited film is calculated for different depths inside the porous structure in relation to the precursor exposure time, the precursor vapor pressure, and other parameters. Good agreement with experimental results was obtained for ALD zirconiumdioxide (ZrO{sub 2}) films using the precursors tetrakis(ethylmethylamido)zirconium and O{sub 2}. The derivation can be adjusted to describe other features of ALD processes, e.g., precursor and reactive site losses, different growth modes, pore size reduction, and surface diffusion.

36 MATERIALS SCIENCE↗
DOE OSTI2015

Critical points of metal vapors

A new method is proposed for calculating the parameters of critical points and binodals for the vapor–liquid (insulator–metal) phase transition in vapors of metals with multielectron valence shells. The method is based on a model developed earlier for the vapors of alkali metals, atomic hydrogen, and exciton gas, proceeding from the assumption that the cohesion determining the basic characteristics of metals under normal conditions is also responsible for their properties in the vicinity of the critical point. It is proposed to calculate the cohesion of multielectron atoms using well-known scaling relations for the binding energy, which are constructed for most metals in the periodic table by processing the results of many numerical calculations. The adopted model allows the parameters of critical points and binodals for the vapor–liquid phase transition in metal vapors to be calculated using published data on the properties of metals under normal conditions. The parameters of critical points have been calculated for a large number of metals and show satisfactory agreement with experimental data for alkali metals and with available estimates for all other metals. Binodals of metals have been calculated for the first time.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2024

Combining lattice QCD and phenomenological inputs on generalised parton distributions at moderate skewness

Abstract We present a systematic study demonstrating the impact of lattice QCD data on the extraction of generalised parton distributions (GPDs). For this purpose, we use a previously developed modelling of GPDs based on machine learning techniques fulfilling the theoretical requirements of polynomiality, a form of positivity constraint and known reduction limits. A special care is given to estimate the uncertainty stemming from the ill-posed character of the connection between GPDs and the experimental processes usually considered to constrain them, like deeply virtual Compton scattering (DVCS). Moke lattice QCD data inputs are included in a Bayesian framework to a prior model based on an Artificial Neural Network. This prior model is fitted to reproduce the most experimentally accessible information of a phenomenological extraction by Goloskokov and Kroll. We highlight the impact of the precision, correlation and kinematic coverage of lattice data on GPD extraction at moderate $$\xi $$ ξ which has only been brushed in the literature so far, paving the way for a joint extraction of GPDs.

Physics↗
DOE OSTI2024

REDOTHERM (Redox Countercurrent Thermodynamic Limits Model) [SWR-24-88]

REDOTHERM is an open-source, MATLAB-based thermodynamic modeling framework developed to evaluate the performance of redox-active materials for water (H2O) and carbon dioxide (CO2) splitting. It includes models of all major unit operations and supports comparative analysis of different redox-active material candidates. The model is tailored for systems of moving oxide under a parallel/cocurrent flow (PF) and countercurrent flow (CF) configurations. Unvalidated mixed flow reactor (MFR, also known as CSTR) model is also included as an optional addition.

Lidor, Alon [National Renewable Energy Laboratory ↗
DOE OSTI2025

PRIME: Protein Representation Inference for Mutation Evaluation

Protein language machine learning models built upon existing ESM-2 model developed by Evolutionary Scale (evolutionaryscale.ai) and an in-house protein language model based on the BERT model developed by Google. The code also includes model training scripts and saved checkpoints from our own training using publicly available SARS-CoV-2 protein sequences.

Gibson, Kaetlyn [Los Alamos National Lab]↗
DOE OSTI2022

Macro Analysis to Estimate Electric Vehicles Fast-Charging Infrastructure Requirements in Small Urban Areas

Electric vehicles (EVs) are known to reduce emissions and fossil fuel dependency. However, the limited range, long charging time, and inadequate charging infrastructure have hampered the adoption of EVs. The current EV charging infrastructure planning studies and tools require detailed information, extensive resources, and skills that can be a significant barrier to urban areas for finding the required charging infrastructure to support a targeted EV market share. This study generates regression models to estimate the number of direct current fast charging stations and the chargers to support the EV charging demand for urban areas. These models provide macro-level estimates of the required infrastructure investment in urban areas, which can be easily implemented by policy-makers and city planners. This study incorporates data obtained from applying a disaggregate optimization-based charger placement model, developed recently by the same authors, for multiple case studies to generate the required data to calibrate the macro-level models, in the state of Michigan. This simulated data set includes the number of charging stations and chargers for each market share, technology advancement scenario, and the transportation network topology. The results show that the number of charging stations reduces with battery size and charging power and increases with EV market share and the road network lane length. The number of chargers reduces with charging power, whereas it increases with battery size, EV market share, and vehicle miles traveled in the system. The model developed here can be applied to any state having urban characteristics and weather conditions similar to Michigan.

Engineering↗
DOE OSTI2018

Penn State Lab Testing Fluid-Rock Interaction in Geothermal Reservoirs

This project focused on assessment and discovery of fluid-rock interaction in geothermal reservoirs. We accomplished work in four main areas: 1) fracture formation and the relationship between fluid flow and shear failure, 2) assessment of fracture geometry and fluid permeability using novel acoustic measurements, 3) an improved understanding of how drilling, injection and geothermal production influence local seismicity, and 4) development of process based models for using induced seismicity to assess the critical stress-state in Earth's crust. This submission contains relevant true triaxial granite and shale experiments and associated run sheets accompanied with links to final technical report and presentation project slides.

15 GEOTHERMAL ENERGY↗
DOE OSTI2022

Machine Learning-Assisted High-Temperature Reservoir Thermal Energy Storage Optimization: Numerical Modeling and Machine Learning Input and Output Files

This data set includes the numerical modeling input files and output files used to synthesize data, and the reduced-order machine learning models trained from the synthesized data for reservoir thermal energy storage site identification. In this study, a machine-learning-assisted computational framework is presented to identify High-Temperature Reservoir Thermal Energy Storage (HT-RTES) site with optimal performance metrics by combining physics-based simulation with stochastic hydrogeologic formation and thermal energy storage operation parameters, artificial neural network regression of the simulation data, and genetic algorithm-enabled multi-objective optimization. A doublet well configuration with a layered (aquitard-aquifer-aquitard) generic reservoir is simulated for cases of continuous operation and seasonal-cycle operation scenarios. Neural network-based surrogate models are developed for the two scenarios and applied to generate the Pareto fronts of the HT-RTES performance for four potential HT-RTES sites. The developed Pareto optimal solutions indicate the performance of HT-RTES is operation-scenario (i.e., fluid cycle) and reservoir-site dependent, and the performance metrics have competing effects for a given site and a given fluid cycle. The developed neural network models can be applied to identify suitable sites for HT-RTES, and the proposed framework sheds light on the design of resilient HT-RTES systems. All the simulations and the neural network model were done by Idaho National Laboratory. A detailed description of the work was reported in publication linked below.

15 GEOTHERMAL ENERGY↗
DOE OSTI2018

Competitive docking model for prediction of the human nicotinic acetylcholine receptor α7 binding of tobacco constituents

The detrimental health effects associated with tobacco use constitute a major public health concern. The addiction associated with nicotine found in tobacco products has led to difficulty in quitting among users. Nicotinic acetylcholine receptors (nAChRs) are the targets of nicotine and are responsible for addiction to tobacco products. However, it is unknown if the other >8000 tobacco constituents are addictive. Since it is time-consuming and costly to experimentally assess addictive potential of such larger number of chemicals, computationally predicting human nAChRs binding is important for in silico evaluation of addiction potential of tobacco constituents and needs structures of human nAChRs. Therefore, we constructed three dimensional structures of the ligand binding domain of human nAChR α7 subtype and then developed a predictive model based on the constructed structures to predict human nAChR α7 binding activity of tobacco constituents. The predictive model correctly predicted 11 out of 12 test compounds to be binders of nAChR α7. The model is a useful tool for high-throughput screening of potential addictive tobacco constituents. These results could inform regulatory science research by providing a new validated predictive tool using cutting-edge computational methodology to high throughput screen tobacco additives and constituents for their binding interaction with the human α7 nicotinic receptor. The tool represents a prediction model capable of screening thousands of chemicals found in tobacco products for addiction potential, which improves the understanding of the potential effects of additives.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2012

High Energy Physics at Tufts University Final Report

In this Final Report, we the researchers of the high energy physics group at Tufts University summarize our works and achievements in three frontier areas of elementary particle physics: (i) Neutrino physics at the Intensity Frontier, (ii) Collider physics at the Energy Frontier, and (iii) Theory investigations of spin structure and quark-gluon dynamics of nucleons using quantum chromodynamics. With our Neutrino research we completed, or else brought to a useful state, the following: Data-taking, physics simulations, physics analysis, physics reporting, explorations of matter effects, and detector component fabrication. We conducted our work as participants in the MINOS, NOvA, and LBNE neutrino oscillation experiments and in the MINERvA neutrino scattering experiment. With our Collider research we completed or else brought to a useful state: Data-taking, development of muon system geometry and tracking codes, software validation and maintenance, physics simulations, physics analysis, searches for new particles, and study of top-quark and B-quark systems. We conducted these activities as participants in the ATLAS proton-proton collider experiment at CERN and in the CDF proton-antiproton collider experiment at Fermilab. In our Theory research we developed QCD-based models, applications of spin phenomenology to fundamental systems, fitting of models to data, presenting and reporting of new concepts and formalisms. The overarching objectives of our research work have always been: 1) to test and clarify the predictions of the Standard Model of elementary particle physics, and 2) to discover new phenomena which may point the way to a more unified theoretical framework.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2012

Lead Slowing-Down Spectrometry Time Spectral Analysis for Spent Fuel Assay: FY12 Status Report

Executive Summary Developing a method for the accurate, direct, and independent assay of the fissile isotopes in bulk materials (such as used fuel) from next-generation domestic nuclear fuel cycles is a goal of the Office of Nuclear Energy, Fuel Cycle R&D, Material Protection and Control Technology (MPACT) Campaign. To meet this goal, MPACT supports a multi-institutional collaboration, of which PNNL is a part, to study the feasibility of Lead Slowing Down Spectroscopy (LSDS). This technique is an active nondestructive assay method that has the potential to provide independent, direct measurement of Pu and U isotopic masses in used fuel with an uncertainty considerably lower than the approximately 10% typical of today’s confirmatory methods. This document is a progress report for FY2012 PNNL analysis and algorithm development. Progress made by PNNL in FY2012 continues to indicate the promise of LSDS analysis and algorithms applied to used fuel assemblies. PNNL further refined the semi-empirical model developed in FY2011 based on singular value decomposition (SVD) to numerically account for the effects of self-shielding. The average uncertainty in the Pu mass across the NGSI-64 fuel assemblies was shown to be less than 3% using only six calibration assemblies with a 2% uncertainty in the isotopic masses. When calibrated against the six NGSI-64 fuel assemblies, the algorithm was able to determine the total Pu mass within <2% uncertainty for the 27 diversion cases also developed under NGSI. Two purely empirical algorithms were developed that do not require the use of Pu isotopic fission chambers. The semi-empirical and purely empirical algorithms were successfully tested using MCNPX simulations as well applied to experimental data measured by RPI using their LSDS. The algorithms were able to describe the 235U masses of the RPI measurements with an average uncertainty of 2.3%. Analyses were conducted that provided valuable insight with regard to design requirements (e.g. Pb stack size, neutron source location) of an LSDS for the purpose of assaying used fuel assemblies. Sensitivity studies were conducted that provide insight as to how the LSDS instrument can be improved by making it more sensitive to the center of the fuel assemblies. In FY2013, PNNL will continue efforts to develop and refine design requirements of an LSDS for the ultimate purpose of assaying used fuel assemblies. Future efforts will be directed toward more extensive experimental benchmarking of currently implemented time-spectra analysis algorithms.

Kulisek, Jonathan A.↗
DOE OSTI2014

Post-irradiation Examination Plan for ORNL and University of California Santa Barbara Assessment of UCSB ATR-2 Irradiation Experiment

New and existing databases will be combined to support development of physically based models of transition temperature shifts (TTS) for high fluence-low flux (φ < 10 11 n/cm 2 -s) conditions, beyond the existing surveillance database, to neutron fluences of at least 1×10 20 n/cm 2 (>1 MeV). All references to neutron flux and fluence in this report are for fast neutrons (>1 MeV). The reactor pressure vessel (RPV) task of the Light Water Reactor Sustainability (LWRS) Program is working with various organizations to obtain archival surveillance materials from commercial nuclear power plants to allow for comparisons of the irradiation-induced microstructural features from reactor surveillance materials with those from similar materials irradiated under high flux conditions in test reactors

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2014

NEAMS Software V&V Plan for the MARMOT Software

In order to ensure the accuracy and quality of the microstructure based materials models being developed in conjunction with MARMOT simulations, MARMOT must undergo exhaustive verification and validation. Only after this process can we confidently rely on the MARMOT code to predict the microstructure evolution within the fuel. Therefore, in this report we lay out a V&V plan for the MARMOT code, highlighting where existing data could be used and where new data is required.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗