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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 883 records · Page 49
All sourcesNASA NTRSDOE OSTI
DOE OSTI2017

Observational Evidence of Magnetic Reconnection for Brightenings and Transition Region Arcades in IRIS Observations

By using a new method of forced-field extrapolation, we study the emerging flux region AR11850 observed by the Interface Region Imaging Spectrograph and Solar Dynamical Observatory . Our results suggest that the bright points (BPs) in this emerging region exhibit responses in lines formed from the upper photosphere to the transition region, which have relatively similar morphologies. They have an oscillation of several minutes according to the Atmospheric Imaging Assembly data at 1600 and 1700 Å. The ratio between the BP intensities measured in 1600 and 1700 Å filtergrams reveals that these BPs are heated differently. Our analysis of the Helioseismic and Magnetic Imager vector magnetic field and the corresponding topology in AR11850 indicates that the BPs are located at the polarity inversion line and most of them are related to magnetic reconnection or cancelation. The heating of the BPs might be different due to different magnetic topology. We find that the heating due to the magnetic cancelation would be stronger than the case of bald patch reconnection. The plasma density rather than the magnetic field strength could play a dominant role in this process. Based on physical conditions in the lower atmosphere, our forced-field extrapolation shows consistent results between the bright arcades visible in slit-jaw image 1400 Å and the extrapolated field lines that pass through the bald patches. It provides reliable observational evidence for testing the mechanism of magnetic reconnection for the BPs and arcades in the emerging flux region, as proposed in simulation studies.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2020

Comparative Study of Differentially Private Data Synthesis Methods

When sharing data among researchers or releasing data for public use, there is a risk of exposing sensitive information of individuals in the data set. Data synthesis is a statistical disclosure limitation technique for releasing synthetic data sets with pseudo individual records. Traditional data synthesis techniques often rely on strong assumptions of a data intruder’s behaviors and background knowledge to assess disclosure risk. Differential privacy (DP) formulates a theoretical approach for a strong and robust privacy guarantee in data release without having to model intruders’ behaviors. Efforts have been made aiming to incorporate the DP concept in the data synthesis process. Here, we examine current DIfferentially Private Data Synthesis (DIPS) techniques for releasing individual-level surrogate data for the original data, compare the techniques conceptually and evaluate the statistical utility and inferential properties of the synthetic data via each DIPS technique through extensive simulation studies. Our work sheds light on the practical feasibility and utility of the various DIPS approaches, and suggests future research directions for DIPS.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

SVM-Based Synchronized Fault Detection for 100% Renewable Microgrids

Traditional protection schemes face significant challenges when applied to microgrids with high penetrations of renewables with inverter-based resources (IBRs). The proliferation of advanced sensing and communication technologies has generated copious data, offering an opportunity to overcome these limitations using data-driven machine learning approaches. This work proposes a novel approach based on a support vector machine (SVM) for detecting faults within a 100% renewable microgrid. The approach encompasses a systematic offline training stage for the development of a linear SVM-based fault detection algorithm. This process covers offline data collection from the microgrid under study, the extraction of features such as positive- and negative-sequence components and the total harmonic distortion of the voltage and current measurements of the relays, and the design of the linear SVM-based classifier. During the online implementation, however, different classifiers can exhibit asynchronicity in detecting the fault inception at different subcycle-to-cycle period-level delays. To circumvent this asynchronicity issue, a separate algorithm is developed for each relay to estimate the fault inception time as close to the real fault time. The performance of the proposed SVM-based synchronized fault detection method is evaluated using online time-domain simulation studies on a microgrid test system. The results corroborate the reliability of the fault detection scheme when tested under various fault cases (fault types, locations, and impedances) and non-fault cases during both grid-tied and islanded operation modes.

100% microgrid↗
DOE OSTI2023

Structure-informed clustering for population stratification in association studies

Background: Identifying variants associated with complex traits is a challenging task in genetic association studies due to linkage disequilibrium (LD) between genetic variants and population stratification, unrelated to the disease risk. Existing methods of population structure correction use principal component analysis or linear mixed models with a random effect when modeling associations between a trait of interest and genetic markers. However, due to stringent significance thresholds and latent interactions between the markers, these methods often fail to detect genuinely associated variants. Results: To overcome this, we propose CluStrat, which corrects for complex arbitrarily structured populations while leveraging the linkage disequilibrium induced distances between genetic markers. It performs an agglomerative hierarchical clustering using the Mahalanobis distance covariance matrix of the markers. In simulation studies, we show that our method outperforms existing methods in detecting true causal variants. Applying CluStrat on WTCCC2 and UK Biobank cohorts, we found biologically relevant associations in Schizophrenia and Myocardial Infarction. CluStrat was also able to correct for population structure in polygenic adaptation of height in Europeans. Conclusions: CluStrat highlights the advantages of biologically relevant distance metrics, such as the Mahalanobis distance, which captures the cryptic interactions within populations in the presence of LD better than the Euclidean distance.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2019

Merging Cluster Collaboration: A Panchromatic Atlas of Radio Relic Mergers

Golovich et al. (2017b) presents an optical imaging and spectroscopic survey of 29 radio relic merging galaxy clusters. In this paper, we study this survey to identify substructure and quantify the dynamics of the mergers. Using a combined photometric and spectroscopic approach, we identify the minimum number of substructures in each system to describe the galaxy populations and estimate the line of sight velocity difference between likely merging subclusters. Herein, we find that the line-of-sight velocity components of the mergers are typically small compared with the maximum three dimensional relative velocity (usually < 1000 km s -1 and often consistent with zero). This suggests that the merger axes of these systems are generally in or near the plane of the sky matching findings in magneto-hydrodynamical simulations. In 28 of the 29 systems we identify substructures in the galaxy population aligned with the radio relic(s) and presumed associated merger induced shock. From this ensemble, we identify eight systems to include in a \gold" sample that is prime for further observation, modeling, and simulation study. Additional papers will present weak lensing mass maps and dynamical modeling for each merging system, ultimately leading to new insight into a wide range of astrophysical phenomena at some of the largest scales in the universe.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2024

SVM-Based Synchronized Fault Detection for 100% Renewable Microgrids: Preprint

Traditional protection schemes face significant challenges when applied to microgrids with high penetrations of renewables with inverter-based resources (IBRs). The proliferation of advanced sensing and communication technologies has generated copious data, offering an opportunity to overcome these limitations using data-driven machine learning approaches. This work proposes a novel approach based on a support vector machine (SVM) for detecting faults within a 100% renewable microgrid. The approach encompasses a systematic offline training stage for the development of a linear SVM-based fault detection algorithm. This process covers offline data collection from the microgrid under study, the extraction of features such as positive- and negative-sequence components and the total harmonic distortion of the voltage and current measurements of the relays, and the design of the linear SVM-based classifier. During the online implementation, however, different classifiers can exhibit asynchronicity in detecting the fault inception at different subcycle-to-cycle period-level delays. To circumvent this asynchronicity issue, a separate algorithm is developed for each relay to estimate the fault inception time as close to the real fault time. The performance of the proposed SVM-based synchronized fault detection method is evaluated using online time-domain simulation studies on a microgrid test system. The results corroborate the reliability of the fault detection scheme when tested under various fault cases (fault types, locations, and impedances) and non-fault cases during both grid-tied and islanded operation modes.

100% microgrid↗
DOE OSTI2024

Turning Normal to Abnormal: Reversing CO 2 /C2–Hydrocarbon Selectivity in HKUST–1

Metal–organic frameworks (MOFs) can efficiently purify hydrocarbons from CO 2 , but their rapid saturation, driven by preferential hydrocarbon adsorption, requires energy-intensive adsorption–desorption processes. To address these challenges, an innovative approach is developed, enabling control over MOF flexibility through densification and defect engineering, resulting in an intriguing inverse CO 2 /C2 hydrocarbon selectivity. Here, in this study, the densification process induces the shearing of the crystal lattice and contraction of pores in a defective CuBTC MOF. These changes have led to a remarkable transformation in selectivity, where the originally hydrocarbon-selective CuBTC MOF becomes CO 2 -selective. The selectivity values for densified CuBTC are significantly reversed when compared to its powder form, with notable improvements observed in CO 2 /C 2 H 6 (4416 vs 0.61), CO 2 /C 2 H 4 (15 vs 0.28), and CO 2 /C 2 H 2 (4 vs 0.2). The densified material shows impressive separation, regeneration, and recyclability during dynamic breakthrough experiments with complex quinary gas mixtures. Simulation studies indicate faster CO 2 passage through the tetragonal structure of densified CuBTC compared to C 2 H 2 . Experimental kinetic diffusion studies confirm accelerated CO 2 diffusion over hydrocarbons in the densified MOF, attributed to its small pore window and minimal interparticle voids. This research introduces a promising strategy for refining existing and future MOF materials, enhancing their separation performance.

36 MATERIALS SCIENCE↗
DOE OSTI2017

Development of Smart Ventilation Control Algorithms for Humidity Control in High-Performance Homes in Humid U.S. Climates

Past field research and simulation studies have shown that high performance homes experience elevated indoor humidity levels for substantial portions of the year in humid climates. This is largely the result of lower sensible cooling loads, which reduces the moisture removed by the cooling system. These elevated humidity levels lead to concerns about occupant comfort, health and building durability. Use of mechanical ventilation at rates specified in ASHRAE Standard 62.2-2013 are often cited as an additional contributor to humidity problems in these homes. Past research has explored solutions, including supplemental dehumidification, cooling system operational enhancements and ventilation system design (e.g., ERV, supply, exhaust, etc.). This project’s goal is to develop and demonstrate (through simulations) smart ventilation strategies that can contribute to humidity control in high performance homes. These strategies must maintain IAQ via equivalence with ASHRAE Standard 62.2-2013. To be acceptable they must not result in excessive energy use. Smart controls will be compared with dehumidifier energy and moisture performance. This work explores the development and performance of smart algorithms for control of mechanical ventilation systems, with the objective of reducing high humidity in modern high performance residences. Simulations of DOE Zero-Energy Ready homes were performed using the REGCAP simulation tool. Control strategies were developed and tested using the Residential Integrated Ventilation (RIVEC) controller, which tracks pollutant exposure in real-time and controls ventilation to provide an equivalent exposure on an annual basis to homes meeting ASHRAE 62.2-2013. RIVEC is used to increase or decrease the real-time ventilation rate to reduce moisture transport into the home or increase moisture removal. This approach was implemented for no-, one- and two-sensor strategies, paired with a variety of control approaches in six humid climates (Miami, Orlando, Houston, Charleston, Memphis and Baltimore). The control options were compared to a baseline system that supplies outdoor air to a central forced air cooling (and heating) system (CFIS) that is often used in hot humid climates. Simulations were performed with CFIS ventilation systems operating on a 33% duty-cycle, consistent with 62.2-2013. The CFIS outside airflow rates were set to 0%, 50% and 100% of 62.2-2013 requirements to explore effects of ventilation rate on indoor high humidity. These simulations were performed with and without a dehumidifier in the model. Ten control algorithms were developed and tested. Analysis of outdoor humidity patterns facilitated smart control development. It was found that outdoor humidity varies most strongly seasonally—by month of the year—and that all locations follow the similar pattern of much higher humidity during summer. Daily and hourly variations in outdoor humidity were found to be progressively smaller than the monthly seasonal variation. Patterns in hourly humidity are driven by diurnal daily patterns, so they were predictable but small, and were unlikely to provide much control benefit. Variation in outdoor humidity between days was larger, but unpredictable, except by much more complex climate models. We determined that no-sensor strategies might be able to take advantage of seasonal patterns in humidity, but that real-time smart controls were required to capture variation between days. Sensor-based approaches are also required to respond dynamically to indoor conditions and variations not considered in our analysis. All smart controls face trade-offs between sensor accuracy, cost, complexity and robustness.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2014

A novel fast-neutron detector concept for energy-selective imaging and imaging spectroscopy

We present and discuss the operational principle of a new fast-neutron detector concept suitable for either energy-selective imaging or for imaging spectroscopy. The detector is comprised of a series of energy-selective stacks of converter foils immersed in a noble-gas based mixture, coupled to a position-sensitive charge readout. Each foil in the various stacks is made of two layers of different thicknesses, fastened together: a hydrogen-rich (plastic) layer for neutron-to-proton conversion, and a hydrogen-free coating to selectively stop/absorb the recoil protons below a certain energy cut-off. The neutron-induced recoil protons, that escape the converter foils, release ionization electrons in the gas gaps between consecutive foils. The electrons are then drifted towards and localized by a position-sensitive charge amplification and readout stage. Comparison of the images detected by stacks with different energy cut-offs allows energy-selective imaging. Neutron energy spectrometry is realized by analyzing the responses of a sufficient large number of stacks of different energy response and unfolding techniques. In this paper, we present the results of computer simulation studies and discuss the expected performance of the new detector concept. Potential applications in various fields are also briefly discussed, in particularly, the application of energy-selective fast-neutron imaging for nuclear safeguards application, with the aim of determining the plutonium content in Mixed Oxide (MOX) fuels.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2017

Computer-based human-machine interfaces for emergency operation - 233

The Halden Reactor Project (HRP) has many years of experience in developing and evaluating principles for computer-based human-machine interfaces (HMIs). We have synthesized the lessons learned in an integrated design concept that is implemented on a boiling water reactor (BWR) simulator in the Halden Man Machine Laboratory (HAMMLAB). The design concept is intended to alert and support the operators in monitoring and controlling the plant during normal operation, emergency situations and outages. The computer-based HMIs include an alarm system, operating displays, operating procedures and large screen displays. The design concept has been tested in a simulator study in HAMMLAB. This paper describes the design and initial testing of the computer-based emergency operating procedures (EOPs) and other displays to support the operators when using the EOPs. (authors)

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2014

Tailoring characteristic thermal stability of Ni-Au binary nanocrystals via structure and composition engineering: theoretical insights into structural evolution and atomic inter-diffusion

We report on the structural evolution and atomic inter-diffusion characteristics of the bimetallic Ni-Au nanocrystals (NCs) by molecular dynamics simulations studies. Our results reveal that the thermal stability dynamics of Ni-Au NCs strongly depends on the atomic configurations. By engineering the structural construction with Ni:Au = 1:1 atomic composition, compared with core-shell Au@Ni and alloy NCs, the melting point of core-shell Ni@Au NCs is significantly enhanced up to 1215 K. Unexpectedly, with atomic ratio of Au:Ni= 1:9, the melting process initiates from the atoms in the shell of Ni@Au and alloy NCs, while starts from the core of Au@Ni NCs. The corresponding features and evolution process of structural motifs, mixing and segregation are illustrated via a series of dynamic simulations videos. Moreover, our results revealed that the face centered cubic phase Au{sub 0.75}Ni{sub 0.25} favorably stabilizes in NCs form but does not exist in the bulk counterpart, which elucidates the anomalies of previously reported experimental results on such bimetallic NCs.

36 MATERIALS SCIENCE↗
DOE OSTI2012

The second-order differential phase contrast and its retrieval for imaging with x-ray Talbot interferometry

Purpose: The x-ray differential phase contrast imaging implemented with the Talbot interferometry has recently been reported to be capable of providing tomographic images corresponding to attenuation-contrast, phase-contrast, and dark-field contrast, simultaneously, from a single set of projection data. The authors believe that, along with small-angle x-ray scattering, the second-order phase derivative {Phi}{sup Double-Prime }{sub s}(x) plays a role in the generation of dark-field contrast. In this paper, the authors derive the analytic formulae to characterize the contribution made by the second-order phase derivative to the dark-field contrast (namely, second-order differential phase contrast) and validate them via computer simulation study. By proposing a practical retrieval method, the authors investigate the potential of second-order differential phase contrast imaging for extensive applications. Methods: The theoretical derivation starts at assuming that the refractive index decrement of an object can be decomposed into {delta}={delta}{sub s}+{delta}{sub f}, where {delta}{sub f} corresponds to the object's fine structures and manifests itself in the dark-field contrast via small-angle scattering. Based on the paraxial Fresnel-Kirchhoff theory, the analytic formulae to characterize the contribution made by {delta}{sub s}, which corresponds to the object's smooth structures, to the dark-field contrast are derived. Through computer simulation with specially designed numerical phantoms, an x-ray differential phase contrast imaging system implemented with the Talbot interferometry is utilized to evaluate and validate the derived formulae. The same imaging system is also utilized to evaluate and verify the capability of the proposed method to retrieve the second-order differential phase contrast for imaging, as well as its robustness over the dimension of detector cell and the number of steps in grating shifting. Results: Both analytic formulae and computer simulations show that, in addition to small-angle scattering, the contrast generated by the second-order derivative is magnified substantially by the ratio of detector cell dimension over grating period, which plays a significant role in dark-field imaging implemented with the Talbot interferometry. Conclusions: The analytic formulae derived in this work to characterize the second-order differential phase contrast in the dark-field imaging implemented with the Talbot interferometry are of significance, which may initiate more activities in the research and development of x-ray differential phase contrast imaging for extensive preclinical and eventually clinical applications.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2014

Robust estimation procedure in panel data model

The panel data modeling has received a great attention in econometric research recently. This is due to the availability of data sources and the interest to study cross sections of individuals observed over time. However, the problems may arise in modeling the panel in the presence of cross sectional dependence and outliers. Even though there are few methods that take into consideration the presence of cross sectional dependence in the panel, the methods may provide inconsistent parameter estimates and inferences when outliers occur in the panel. As such, an alternative method that is robust to outliers and cross sectional dependence is introduced in this paper. The properties and construction of the confidence interval for the parameter estimates are also considered in this paper. The robustness of the procedure is investigated and comparisons are made to the existing method via simulation studies. Our results have shown that robust approach is able to produce an accurate and reliable parameter estimates under the condition considered.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

Grain boundary relaxation in doped nano-grained aluminum

Here, simulation studies are done to understand the role of dopants that segregate preferentially to grain boundaries on the stability of nanocrystalline aluminum. A dopant design framework based on thermodynamic principles, is used to identify the specific dopant type with the highest potential to segregate to grain boundaries in nanocrystalline aluminum. Various elements are evaluated as potential dopants and magnesium is identified to have the highest tendency to segregate to grain boundaries and release the excess free energyleading to the relaxation of the grain boundaries. A systematic combination of atomic structure analysis is then done to correlate grain boundary relaxation and the mechanical response of the magnesium-doped nanocrystalline aluminum at ambient temperature. The atomistic simulations reveal that the preferential partitioning of magnesium dopants to the grain boundaries reduces the excess volume within this region which stabilizes the nanostructure. At low contents, the magnesium dopants are observed initially partition to the grain boundaries, but once saturation of the grain boundaries is reached, excess dopants are accommodated in the crystalline interiors. It is found that the addition of the magnesium dopants even in the dilute limit, enhances the strength of the nanocrystalline aluminum. The formation of large, disordered GBs in doped nanocrystalline aluminum under tensile load allowed it to accommodate the deformation and prohibit crack growth.

36 MATERIALS SCIENCE↗
DOE OSTI2024

A Corrected Score Function Framework for Modelling Circadian Gene Expression

Many biological processes display oscillatory behaviour based on an approximately 24 h internal timing system specific to each individual. One process of particular interest is gene expression, for which several circadian transcriptomic studies have identified associations between gene expression during a 24 h period and an individual's health. A challenge with analysing data from these studies is that each individual's internal timing system is offset relative to the 24 h day-night cycle, where day–night cycle time is recorded for each collected sample. Laboratory procedures can accurately determine each individual's offset and determine the internal time of sample collection. However, these laboratory procedures are labour-intensive and expensive. Here, in this paper, we propose a corrected score function framework to obtain a regression model of gene expression given internal time when the offset of each individual is too burdensome to determine. A feature of this framework is that it does not require the probability distribution generating offsets to be symmetric with a mean of zero. Simulation studies validate the use of this corrected score function framework for cosinor regression, which is prevalent in circadian transcriptomic studies. Illustrations with data from three circadian transcriptomic studies further demonstrate that the proposed framework consistently mitigates bias relative to using a score function that does not account for this offset.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2019

Contribution of intragranular misorientations to the cold rolling textures of ferritic stainless steels

A combined experimental and simulation study of intragranular misorientation and texture development in ferritic stainless steels is presented herein. Cold rolling was performed on materials having different grain shapes to reveal variations of misorientations and texture with variations in microstructure. The experimental results were compared with predictions of the Visco-Plastic Self-Consistent (VPSC) model and the recently developed Grain-Fragmentation Visco-Plastic Self-Consistent (GF-VPSC) model. It is shown that the GF-VPSC model, incorporating the development of intragranular misorientations, provides a much better prediction of the texture strength compared to the standard VPSC model. The predictions of intragranular misorientation are also in good agreement with experimental measurements. Both experiments and simulations point to the importance of anisotropy of intragranular misorientation distributions in determining texture development and, importantly, texture strength.

36 MATERIALS SCIENCE↗
DOE OSTI2015

PROBING THE ROTATION OF CORE-COLLAPSE SUPERNOVA WITH A CONCURRENT ANALYSIS OF GRAVITATIONAL WAVES AND NEUTRINOS

The next time a core-collapse supernova (SN) explodes in our galaxy, various detectors will be ready and waiting to detect its emissions of gravitational waves (GWs) and neutrinos. Current numerical simulations have successfully introduced multi-dimensional effects to produce exploding SN models, but thus far the explosion mechanism is not well understood. In this paper, we focus on an investigation of progenitor core rotation via comparison of the start time of GW emission and that of the neutronization burst. The GW and neutrino detectors are assumed to be, respectively, the KAGRA detector and a co-located gadolinium-loaded water Cherenkov detector, either EGADS or GADZOOKS!. Our detection simulation studies show that for a nearby SN (0.2 kpc) we can confirm the lack of core rotation close to 100% of the time, and the presence of core rotation about 90% of the time. Using this approach there is also the potential to confirm rotation for considerably more distant Milky Way SN explosions.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2020

Residential Battery Modeling for Control-Oriented Techno-Economic Studies: Preprint

Electrochemical batteries, which serve as electric energy storage devices, are becoming increasingly popular among residential buildings that incorporate solar photovoltaic (PV) systems to help meet their energy needs. Battery economics are affected by performance degradation over time, and managing this degradation can help extend the battery's lifespan. The tradeoff between operational costs/benefits and managing battery degradation is of significant research interest. One of the key factors for assessing battery degradation is the dispatch strategy used to control the charging and discharging of the battery. Conventional dispatch strategies typically use simple rule-based methods, and these overly aggressive charging/discharging cycles can significantly reduce a battery’s life span. Our research seeks to develop optimized dispatch strategies for grid-connected PV homes with a goal of extending battery life while simultaneously taking into consideration utility costs and occupant comfort. To achieve this goal, we adapted lithium-ion battery life- and cyclic-degradation models for use in high-fidelity building simulations, so whole-building and grid-interactive controllers can dispatch the batteries along with other flexible loads. With the help of a co-simulation platform, we performed a simulation study to compute the optimized dispatch strategies for relevant operating conditions brought about by changing geographical locations, weather conditions, and utility pricing. Comparing the optimized strategies with the conventional strategies resulted in a >50% decrease in capacity degradation and >10% average reduction in operational costs during the months of January and July in Fort Collins, Colorado; Phoenix, Arizona; and Portland, Oregon.

30 DIRECT ENERGY CONVERSION↗