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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 1,225 records · Page 68
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

An integrated approach to optimizing concentration shock wave electrodialysis using 2D multicell simulation and response surface models

Shock wave electrodialysis (SWED) is a highly promising technique for energy-efficient ion separation in the context of a circular economy. This paper presents a approach way of modeling and improving SWED using a two-dimensional multicell model combined with the COMSOL program and response surface methodology. The model integrates the Nernst-Planck equation, Darcy's law, and first-order electroosmosis to examine the local concentration, flux of ionic species, distribution of current, and velocity of flow in SWED cells under various operating conditions. We first illustrate the clear depiction of concentration, velocity, and electric potential distribution through contours which aids in identifying optimal operating conditions and designing scalable SWED systems. The results emphasize the significance of surface charge density and voltage in influencing the features of shock waves for obtaining effective ion separation while optimizing energy consumption and improving current efficiency by controlling the retention time of feed flow. Here, this study defines two crucial characteristics of shock waves, namely the length of the flat depletion zone of a fully developed shock wave (shock wave height) and the distance of shock wave propagation (shock wave length). These properties significantly impact separation performance, as determined by the simulation results. Additionally, the response surface methodology is incorporated with the COMSOL models to develop predictive models and graph responses, enabling a more comprehensive understanding of the interactions between parameters and performance indicators, such as removal ratio, energy consumption, and water recovery. Finally, this work suggests design tactics for expanding SWED processes and outlines potential areas for further research. This research provides valuable insights into the prospective applications, design optimization, and scalability of SWED in the field of electrokinetic separation technologies for green chemistry and a circular economy.

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

On the relevance of volume increase for the length changes of mortar bars in sulfate solutions

The ingress of sulfate ions into cementitious materials leads to the formation of ettringite, gypsum and other phases. The increase in solid volume through the formation of these phases is often assumed to be the only reason for expansion. In this paper we systematically compare the volume increase predicted by thermodynamic modeling to macroscopic expansion for mortars made with CEM I in different sulfate solutions and for mortars made with a range of blended cements in sodium sulfate solution. It is shown that the length changes cannot be explained by simple volume increase alone. A more plausible explanation of expansion lies in the theory of crystallization pressure, in which crystals forming from a supersaturated solution may exert pressure on their surroundings. It is observed that expansion occurs in systems where thermodynamic modeling predicts the co-existence of ettringite with gypsum. In such a case, if monosulfate and gypsum are both present locally, the solution can be highly supersaturated with respect to ettringite, whose formation in confined conditions (such as within C–S–H) can then exert expansive forces.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Defect-specific strength factors and superposition model for predicting strengthening of ion irradiated Fe18Cr alloy

Here, a high-purity binary alloy Fe18Cr was subjected to heavy-ion irradiation in order to provide improved understanding of the irradiation effect on radiation hardening associated with dislocation loop and network formation, α’ precipitation. The specimens were irradiated with 8 MeV Fe ions (~2 μm ion range) to midrange doses of 0.37 and 3.7 dpa at 300, 350 and 450 °C using dose rates of ~10 –5 –10 –3 dpa/s. Nanoindentation testing was performed to extract the bulk equivalent hardness at low depth from these ion irradiated specimens. High-quality transmission electron microscopy (TEM) images were acquired by utilizing a flash electropolishing method on the Focused Ion Beam (FIB) prepared liftouts. An accurate calculation of the strength factor was provided based on detailed TEM characterization of the irradiated microstructures. A newly refined hardening superposition method was applied to combine the strengthening components from each microstructure. The good agreement between microstructure predicted strength and measured strength further demonstrated the fidelity to use hardening model to quantify the mechanical properties of ion irradiated materials.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Event-by-event neutron–photon multiplicity correlations in 252Cf(sf)

Excited nuclear fragments are emitted during nuclear fission. The de-excitation of these fission fragments takes place as sequential emission of neutrons followed by photons. A correlation between neutron and photon multiplicities accompanying fission is thus expected. Fission event generators based on established statistical nuclear physics models predict a negative event-by-event correlation in neutron-photon multiplicity. A survey of published experimental results of an event-by-event covariance between the neutron - photon multiplicities emitted following the spontaneous fission of 252 Cf is presented. Analytic unfolding expressions are developed in this work to determine the bias introduced by background sources, particle misclassification, pulse pileup, and inelastic photon production. The published experimental data are re-analyzed using these unfolding techniques and are found to be in qualitative agreement with the predictions of model-based calculations. Finally, in particular, we have concluded that there exists a significant event-by-event neutron-photon emission competition following the spontaneous fission of 252 Cf.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2012

Lower hybrid current drive at high density in the multi-pass regime

Assessing the performance of lower hybrid current drive (LHCD) at high density is critical for developing non-inductive current drive systems on future steady-state experiments. Excellent LHCD efficiency has been observed during fully non-inductive operation ({eta}=2.0-2.5 Multiplication-Sign 10{sup 19} AW{sup -1}m{sup -2} at n{sub e}=0.5 Multiplication-Sign 10{sup 20} m{sup -3}) on Alcator C-Mod [I. H. Hutchinson et al., Phys. Plasmas 1, 1511 (1994)] under conditions (n{sub e}, magnetic field and topology, and LHCD frequency) relevant to ITER [S. Shiraiwa et al., Nucl. Fusion 51, 103024 (2011)]. To extend these results to advanced tokamak regimes with higher bootstrap current fractions on C-Mod, it is necessary to increase n{sub e} to 1.0-1.5 Multiplication-Sign 10{sup 20} m{sup -3}. However, the number of current-carrying, non-thermal electrons generated by LHCD drops sharply in diverted configurations at densities that are well below the density limit previously observed on limited tokamaks. In these cases, changes in scrape off layer (SOL) ionization and density profiles are observed during LHCD, indicating that significant power is transferred from the LH waves to the SOL. Fokker-Planck simulations of these discharges utilizing ray tracing and full wave propagation codes indicate that LH waves in the high density, multi-pass absorption regime linger in the plasma edge, and SOL region, where absorption near or outside the LCFS results in the loss of current drive efficiency. Modeling predicts that non-thermal emission increases with stronger single-pass absorption. Experimental data show that increasing T{sub e} in high density LH discharges results in higher non-thermal electron emission, as predicted by the models.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2020

Spatially varying colloidal phase behavior on multi-dimensional energy landscapes

A method is reported to determine equilibrium concentration profiles and local phase behavior for colloids on multi-dimensional energy landscapes. A general expression is derived based on local particle concentration and osmotic pressure differences that are balanced by forces on colloids due to energy landscape gradients. This analysis is applied to colloidal particles in high frequency AC electric fields within octupolar electrodes, where the energy landscape can be shaped in two dimensions. These results are also directly applicable to any particles having induced dipoles in spatially non-uniform electromagnetic fields. Predictions based on modelling colloids with an effective hard disk equation of state indicate inhomogeneous solid and fluid states coexisting on different shaped energy landscapes including multiple minima. Model predictions show excellent agreement with time-averaged Brownian Dynamic simulations at equilibrium. Findings demonstrate a general approach to understand colloidal phase behavior on energy landscapes due to external fields, which could enable control of colloidal microstructure on morphing energy landscapes and the inverse design of fields to assemble hierarchically structured colloidal materials.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2014

Search for the infrared emission features from deuterated interstellar polycyclic aromatic hydrocarbons

We report the results of a search for emission features from interstellar deuterated polycyclic aromatic hydrocarbons (PAHs) in the 4 μm region with the Infrared Camera (IRC) on board AKARI. No significant excess emission is seen in 4.3-4.7 μm in the spectra toward the Orion Bar and M17 after the subtraction of line emission from the ionized gas. A small excess of emission remains at around 4.4 and 4.65 μm, but the ratio of their intensity to that of the band emission from PAHs at 3.3-3.5 μm is estimated as 2%-3%. This is an order of magnitude smaller than the values previously reported and also those predicted by the model of deuterium depletion onto PAHs. Since the subtraction of the ionized gas emission introduces an uncertainty, the deuterated PAH features are also searched for in the reflection nebula GN 18.14.0, which does not show emission lines from ionized gas. We obtain a similar result that excess emission in the 4 μm region, if present, is about 2% of the PAH band emission in the 3 μm region. The present study does not find evidence for the presence of the large amount of deuterated PAHs that the depletion model predicts. The results are discussed in the context of deuterium depletion in the interstellar medium.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2021

Striations in moderate pressure dc driven nitrogen glow discharge

Plasma stratification has been studied for more than a century. Despite the many experimental studies reported on this topic, theoretical analyses and numerical modeling of this phenomenon have been mostly limited to rare gases. In this work, a one-dimensional fluid model with detailed kinetics of electrons and vibrationally excited molecules is employed to simulate moderate-pressure (i.e. a few Torrs) dc discharge in nitrogen in a 15.5 cm long tube of radius 0.55 cm. The model also considers ambipolar diffusion to account for the radial loss of ions and electrons to the wall. The proposed model predicts self-excited standing striations in nitrogen for a range of discharge currents. The impact of electron transport parameters and reaction rates obtained from a solution of local two-term and a multi-term Boltzmann equation on the predictions are assessed. In-depth kinetic analysis indicates that the striations result from the undulations in electron temperature caused due to the interaction between ionization and vibrational reactions. Furthermore, the vibrationally excited molecules associated with the lower energy levels are found to influence nitrogen plasma stratification and the striation pattern strongly. A balance between ionization processes and electron energy transport allows the formation of the observed standing striations. Simulations were conducted for a range of discharge current densities from ~0.018 to 0.080 mA cm –2 , for an operating pressure of 0.7 Torr. Parametric studies show that the striation length decreases with increasing discharge current. Furthermore, the predictions from the model are compared against experimental measurements and are found to agree favorably.

42 ENGINEERING↗
DOE OSTI2023

Foundations of machine learning for low-temperature plasmas: methods and case studies

Abstract Machine learning (ML) and artificial intelligence have proven to be an invaluable tool in tackling a vast array of scientific, engineering, and societal problems. The main drivers behind the recent proliferation of ML in practically all aspects of science and technology can be attributed to: (a) improved data acquisition and inexpensive data storage; (b) exponential growth in computing power; and (c) availability of open-source software and resources that have made the use of state-of-the-art ML algorithms widely accessible. The impact of ML on the field of low-temperature plasmas (LTPs) could be particularly significant in the emerging applications that involve plasma treatment of complex interfaces in areas ranging from the manufacture of microelectronics and processing of quantum materials, to the LTP-driven electrification of the chemical industry, and to medicine and biotechnology. This is primarily due to the complex and poorly-understood nature of the plasma-surface interactions in these applications that pose unique challenges to the modeling, diagnostics, and predictive control of LTPs. As the use of ML is becoming more prevalent, it is increasingly paramount for the LTP community to be able to critically analyze and assess the concepts and techniques behind data-driven approaches. To this end, the goal of this paper is to provide a tutorial overview of some of the widely-used ML methods that can be useful, amongst others, for discovering and correlating patterns in the data that may be otherwise impractical to decipher by human intuition alone, for learning multivariable nonlinear data-driven prediction models that are capable of describing the complex behavior of plasma interacting with interfaces, and for guiding the design of experiments to explore the parameter space of plasma-assisted processes in a systematic and resource-efficient manner. We illustrate the utility of various supervised, unsupervised and active learning methods using LTP datasets consisting of commonly-available, information-rich measurements (e.g. optical emission spectra, current–voltage characteristics, scanning electron microscope images, infrared surface temperature measurements, Fourier transform infrared spectra). All the ML demonstrations presented in this paper are carried out using open-source software; the datasets and codes are made publicly available. The FAIR guiding principles for scientific data management and stewardship can accelerate the adoption and development of ML in the LTP community.

Physics↗
DOE OSTI2022

Interpolating between small- and large-$\mathscr{g}$ expansions using Bayesian model mixing

Bayesian model mixing (BMM) is a statistical technique that can be used to combine models that are predictive in different input domains into a composite distribution that has improved predictive power over the entire input space. We explore the application of BMM to the mixing of two expansions of a function of a coupling constant $\mathscr{g}$ that are valid at small and large values of $\mathscr{g}$ respectively. This type of problem is quite common in nuclear physics, where physical properties are straightforwardly calculable in strong and weak interaction limits or at low and high densities or momentum transfers, but difficult to calculate in between. Interpolation between these limits is often accomplished by a suitable interpolating function, e.g., Padé approximants, but it is then unclear how to quantify the uncertainty of the interpolant. We address this problem in the simple context of the partition function of zero-dimensional Φ 4 theory, for which the (asymptotic) expansion at small $\mathscr{g}$ and the (convergent) expansion at large $\mathscr{g}$ are both known. We consider three mixing methods: linear mixture BMM, localized bivariate BMM, and localized multivariate BMM with Gaussian processes. Here, we find that employing a Gaussian process in the intermediate region between the two predictive models leads to the best results of the three methods. The methods and validation strategies we present here should be generalizable to other nuclear physics settings.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2015

Search for photonic signatures of gauge-mediated supersymmetry in 8 TeV p p collisions with the ATLAS detector

A search is presented for photonic signatures motivated by generalized models of gauge-mediated supersymmetry breaking. This search makes use of 20.3 fb -1 of proton-proton collision data at √s=8 TeV recorded by the ATLAS detector at the LHC, and explores models dominated by both strong and electroweak production of supersymmetric partner states. Four experimental signatures incorporating an isolated photon and significant missing transverse momentum are explored. These signatures include events with an additional photon, lepton, b-quark jet, or jet activity not associated with any specific underlying quark flavor. No significant excess of events is observed above the Standard Model prediction and model-dependent 95% confidence-level exclusion limits are set.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2020

A Machine Learning-based Approach to Predict the Aggregate Flexibility of HVAC Systems

Use of renewable energy resources can play a significant role in mitigation of climate change since they reduce the dependency on fossil fuels. However, high penetration of renewable energy resources into the power grid may cause some reliability problems. Ancillary services are key to realize the potential of renewable resources without disturbing the grid reliability. In recent years, many control approaches focused on exploiting the aggregate flexible heating, ventilation, and air-conditioning (HVAC) loads of buildings. However, there are still research gaps in assessing in advance the resulting indoor temperature responses for given total power and weather profiles before implementing the controls in the buildings. Such an assessment is critical to guarantee satisfying both the grid related objective (reliability) and the building related objective (occupants' comfort). Towards addressing this research gap, this paper presents a machine-learning approach for predicting the ratings of indoor temperature responses. In this approach, a prediction model was developed using a dataset generated by a set of model-free control simulations. The developed model achieved high prediction accuracy and showed the potential of the proposed approach.

Amasyali, Kadir↗
DOE OSTI2022

Quantitative Metrics for Grid Resilience Evaluation and Optimization

Power system resilience has become a critical topic in recent years because of the increasing trend of extreme events and the growing integration of intermittent renewable energy sources. To enhance grid resilience against high-impact, low-frequency events, two questions should be answered: how to quantify the resilience of a given grid and how to incorporate the quantification into power system planning, operation, and restoration. Here this paper develops a new set of quantitative metrics with clear physical interpretation to comprehensively evaluate power system resilience. Using microgrids as an example, an event-based corrective scheduling (ECS) model and an online model predictive control (OMPC) model are developed to integrate the proposed quantitative resilience metrics into power system optimization models for resilience enhancement. The ECS model employs extreme event data to investigate the optimal restoration solution and to help microgrid operators prepare to respond to similar events. The OMPC model provides online decision-making support for operators to handle ongoing outages in the most resilient fashion. The effectiveness and superiority of the proposed quantitative resilience metrics and the resilience enhancement models are demonstrated through simulations and comparative studies on an IEEE test feeder and a real distribution feeder in Southern California.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2017

Elucidating Performance Limitations in Alkaline-Exchange- Membrane Fuel Cells

Water management is a serious concern for alkaline-exchange-membrane fuel cells (AEMFCs) because water is a reactant in the alkaline oxygen-reduction reaction and hydroxide conduction in alkaline-exchange membranes is highly hydration dependent. Here in this article, we develop and use a multiphysics, multiphase model to explore water management in AEMFCs. We demonstrate that the low performance is mostly caused by extremely non-uniform distribution of water in the ionomer phase. A sensitivity analysis of design parameters including humidification strategies, membrane properties, and water transport resistance was undertaken to explore possible optimization strategies. Furthermore, the strategy and issues of reducing bicarbonate/carbonate buildup in the membrane-electrode assembly with CO 2 from air is demonstrated based on the model prediction. Overall, mathematical modeling is used to explore trends and strategies to overcome performance bottlenecks and help enable AEMFC commercialization.

25 ENERGY STORAGE↗
DOE OSTI2021

Impact of short-read sequencing on the misassembly of a plant genome

Abstract Background Availability of plant genome sequences has led to significant advances. However, with few exceptions, the great majority of existing genome assemblies are derived from short read sequencing technologies with highly uneven read coverages indicative of sequencing and assembly issues that could significantly impact any downstream analysis of plant genomes. In tomato for example, 0.6% (5.1 Mb) and 9.7% (79.6 Mb) of short-read based assembly had significantly higher and lower coverage compared to background, respectively. Results To understand what the causes may be for such uneven coverage, we first established machine learning models capable of predicting genomic regions with variable coverages and found that high coverage regions tend to have higher simple sequence repeat and tandem gene densities compared to background regions. To determine if the high coverage regions were misassembled, we examined a recently available tomato long-read based assembly and found that 27.8% (1.41 Mb) of high coverage regions were potentially misassembled of duplicate sequences, compared to 1.4% in background regions. In addition, using a predictive model that can distinguish correctly and incorrectly assembled high coverage regions, we found that misassembled, high coverage regions tend to be flanked by simple sequence repeats, pseudogenes, and transposon elements. Conclusions Our study provides insights on the causes of variable coverage regions and a quantitative assessment of factors contributing to plant genome misassembly when using short reads and the generality of these causes and factors should be tested further in other species.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

An Instrumented Capsule Design to Measure Thermal Conductivity in Miniature UO2 Specimens

Numerous separate effects irradiations of miniature nuclear fuel specimens have been conducted in the High Flux Isotope Reactor (HFIR) under the experimental platform designated as MiniFuel. MiniFuel is a static irradiation capability in which microstructural evolution and fuel performance phenomena are observed during postirradiation examination thereby offering a snapshot of the terminal fuel characteristics. This approach inherently requires fielding an irradiation where the experimental conditions are determined using predictive models and the pertinent outcomes are measured at the end of the test. Static irradiations can provide useful insights to the relationships between fuel performance and the pivotal irradiation conditions, namely temperature and burnup, but the ability to monitor fuel performance in situ would further support fuel development and qualification. To this end, an instrumented experiment design is being developed at Oak Ridge National Laboratory to capture thermal conductivity degradation and fission gas release during HFIR irradiation. These phenomena will be monitored using unique capsule designs that each target a different phenomenon. This paper details the thermal conductivity capsule (TCC) design and its expected performance envelope as determined using computer models. Each TCC will contain a miniature UO2 disc specimen (~0.5 mm thick × 5 mm diameter) sandwiched between metallic slugs with embedded thermocouples. The coupling of in situ temperature measurements, known thermal conductivity of the metallic components, and heat generation rates computed using high-fidelity neutronics models make the thermal conductivity measurement possible. This paper describes the reactor physics and heat transfer models used to predict the capsule’s performance and the methodology for calculating the fuel specimen’s thermal conductivity from the thermocouple measurements.

Gorton, Jacob [ORNL] (ORCID:0000000269806083)↗
DOE OSTI2015

PUNCS: Towards Predictive Understanding of Nitrogen Cycling in Soils

In anoxic environments, the major nitrate/nitrite-consuming processes are respiratory ammonification (also known as dissimilatory nitrate reduction to ammonium) and denitrification (i.e., the formation of the gaseous products N 2 O and N 2 ). Respiratory ammonification oxidizes more carbon per mole of nitrate than denitrification and generates a cation (NH 4+ ), which is retained in soils and bioavailable for plants. Thus, these processes have profoundly different impacts on N retention and greenhouse gas (CO 2 , N 2 O) emissions. Microbes capable of respiratory ammonification or denitrification coexist but the environmental controls over these competing nitrate/nitrite-reducing processes are largely unknown. With the current level of understanding, predictions under what environmental conditions respiratory ammonification activity predominates leading to N-retention rather than N-loss are tenuous. Further, the diversity of genes encoding the ammonium-forming nitrite reductase NrfA is poorly defined hampering the development of tools to assess and monitor this activity in environmental systems. Incomplete denitrification leads to N 2 O, a gas implicated in ozone layer destruction and climate change. The conversion of the greenhouse gas N 2 O to benign N 2 is catalyzed by N 2 O reductase, the characteristic enzyme system of complete denitrifiers. Thus, efforts to estimate N 2 O conversion to N 2 have focused on the well-characterized denitrifier nosZ genes; however, our understanding of the diversity of genes and organisms contributing to N 2 O consumption is incomplete. This paucity of information limits the development of more accurate, predictive models for C- and N-fluxes and greenhouse gas emissions. A comprehensive analysis of the key catalyst of respiratory ammonification, ammonia-forming nitrite reductase NrfA, revealed the evolutionary history of nrfA and identified novel diagnostic features, allowing optimized primer design for nrfA monitoring. Further, a novel group of functional “atypical” nosZ genes was found indicating that a much broader diversity of genes and organisms contribute to consumption of N 2 O. The atypical nosZ genes are distributed in soil ecosystems and often outnumber their typical counterparts, emphasizing their potential role in N 2 O consumption in soils and possibly other environments. Kinetic studies revealed that organisms with atypical NosZ exhibit significantly higher affinity to N 2 O, indicating that the relative activity of bacteria with typical versus atypical NosZ control N 2 O emissions and determine a soil’s N 2 O sink capacity. Collectively, the discoveries made under the PUNCS project improve understanding of N- and associated C-cycling processes in soils, enable the design of enhanced monitoring tools, and allow a larger research community to generate comprehensive datasets required to generate Earth System Models with higher predictive power.

54 ENVIRONMENTAL SCIENCES↗