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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 307 records · Page 17
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Thermal Calculations to Determine Maximum Zircaloy Temperatures During Reactor Operations for Campaign 1 Fuel Assemblies

The H-Canyon Facility plans on processing bundles from Non-Aluminum Spent Nuclear Fuel (NASNF) Risk Group 1 (UO 2 core fuels). Uranium-zirconium (UZr) alloys and zirconium clad UO 2 fuel containing UZr intermetallic compound have the potential to cause explosions during dissolution. UZr intermetallic typically only forms when uranium and zirconium (or zirconium alloy) cladding are in contact at high temperatures. Thermal modeling was performed to determine the maximum cladding temperature for Campaign 1 Zircoloy cladded fuel assembles during reactor operation to help determine if the UZr intermetallic compound could have formed during reactor operations. The temperature of 350°C was chosen as a conservative minimum value for the possible formation of intermetallic UZr compound. The modeling approach, inputs, and results are outlined in this document.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2024

Styrene Thermal Decomposition and Its Reaction with Acetylene under Shock Tube Pyrolysis Conditions: an Experimental and Kinetic Modeling Study

Styrene is an important compound for polymer production and a key intermediate in gas-phase kinetics of polycyclic aromatic hydrocarbons (PAHs). For the first time, the pyrolysis of styrene with and without the presence of acetylene is investigated in a single-pulse shock tube coupled to gas chromatography and mass spectrometry. For each reaction system, quantitative speciation profiles are probed within the temperature range of 1100-1730 K, nominal pressure of 20 bar, and reaction duration of similar to 4 ms. A kinetic model is built to simulate the results. The model explains how styrene is consumed under high-pressure pyrolytic conditions, how the secondary chemistry of intermediate products affect subsequent PAH formation, and how acetylene addition alters the reaction pathways. Throughout the temperature range, styrene breakdown is dominated by the bimolecular interaction between styrene and hydrogen atom, which produces benzene+vinyl or phenyl and ethylene through the stabilization of 2-phenylethyl and its subsequent dissociation. As a result, large amounts of phenyl accumulate, which react with styrene to form C14H12 species while simultaneously releasing H atoms through addition/elimination reactions. The reactivity of fuel consumption is preserved by the regeneration of H atoms as chain carriers. Several C14H10 compounds are formed as a result of the following breakdown of the C14H12 isomers, particularly stilbene, 1,1-diphenyl ethylene, and 9-methyl-9H-fluorene. The presence of acetylene as a co-reactant with styrene allows the Hydrogen-Abstraction-Acetylene-Addition (HACA) pathway to proceed from phenyl radical to enhance the production of phenylacetylene at very low temperatures and acenaphthylene. This hinders the formation of C14H12 isomers, exclusive products from pure styrene dissociation by competing with the styrene+phenyl routes.

Kinetic Modeling↗
DOE OSTI2022

Stellar population analysis of MaNGA early-type galaxies: IMF dependence and systematic effects

We study systematics associated with estimating simple stellar population (SSP) parameters – age, metallicity [M/H], α-enhancement [α/Fe], and initial mass function (IMF) shape – and associated M * /L gradients, of elliptical slow rotators (E-SRs), fast rotators (E-FRs), and S0s from stacked spectra of galaxies in the MaNGA survey. These systematics arise from (i) how one normalizes the spectra when stacking; (ii) having to subtract emission before estimating absorption line strengths; (iii) the decision to fit the whole spectrum or just a few absorption lines; (iv) SSP model differences (e.g. isochrones, enrichment, IMF). The MILES+Padova SSP models, fit to the H β , 〈Fe〉, TiO 2SDSS , and [MgFe] Lick indices in the stacks, indicate that out to the half-light radius R e : (a) ages are younger and [α/Fe] values are lower in the central regions but the opposite is true of [M/H]; (b) the IMF is more bottom-heavy in the center, but is close to Kroupa beyond about R e /2; (c) this makes M * /L about 2 × larger in the central regions than beyond R e /2. While the models of Conroy et al. return similar [M/H] and [α/Fe] profiles, the age and (hence) M * /L profiles can differ significantly even for solar abundances and a Kroupa IMF; different responses to non-solar abundances and IMF parametrization further compound these differences. There are clear (model independent) differences between E-SRs, E-FRs, and S0s: younger ages and less enhanced [α/Fe] values suggest that E-FRs and S0s are not SSPs, but relaxing this assumption is unlikely to change their inferred M * /L gradients significantly.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2024

Problem with gradual absorption in MSD/MSC calculations

Replacing classical exciton model with the better founded Multistep Direct (MSD) and Multistep Compound (MSC) mechanisms has been impeded by incapability of the latter models to describe central part of the neutron emission spectra at incident neutron energies of about 14 MeV and above. We have ascribed this deficiency to the decrease of absorption to the MSC mechanism resulting from the concept of gradual absorption. We were able to obtain very good reproduction of experimentally measured neutron spectra using MSD/MSC calculations when this option was turned off. Such treatment is, however, at odds with the fundamental distinction between MSD and MSC mechanisms that should proceed through the chain of open (P-space) and closed (Q-space) configurations respectively. By blocking gradual absorption we allow the first stage of MSC to be fully populated from the incident channel that at higher incident energies is impossible. We discuss various attempts of addressing the problem that, so far, remains open. In addition, we present an evidence for much tighter spin distribution of particle-hole states than normally assumed.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2023

Uncertainty quantification of a deep learning fuel property prediction model

Deep learning models are being widely used in the field of combustion. Given the black-box nature of typical neural network based models, uncertainty quantification (UQ) is critical to ensure the reliability of predictions as well as the training datasets, and for a principled quantification of noise and its various sources. Deep learning surrogate models for predicting properties of chemical compounds and mixtures have been recently shown to be promising for enabling data-driven fuel design and optimization, with the ultimate goal of improving efficiency and lowering emissions from combustion engines. In this study, UQ is performed for a multi-task deep learning model that simultaneously predicts the research octane number (RON), Motor Octane Number (MON), and Yield Sooting Index (YSI) of pure components and multicomponent blends. The deep learning model is comprised of three smaller networks: Extractor 1, Extractor 2, and Predictor, and a mixing operator. The molecular fingerprints of individual components are encoded via Extractor 1 and Extractor 2, the mixing operator generates fingerprints for mixtures/blends based on linear mixing operation, and the predictor maps the fingerprint to the target properties. Two different classes of UQ methods, Monte Carlo ensemble methods and Bayesian neural networks (BNNs), are employed for quantifying the epistemic uncertainty. Combinations of Bernoulli and Gaussian distributions with DropConnect and DropOut techniques are explored as ensemble methods. All the DropConnect, DropOut and Bayesian layers are applied to the predictor network. Aleatoric uncertainty is modeled by assuming that each data point has an independent uncertainty associated with it. The results of the UQ study are further analyzed to compare the performance of BNN and ensemble methods. Although this study is confined to UQ of fuel property prediction, the methodologies are applicable to other deep learning frameworks that are being widely used in the combustion community.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2021

Quantifying the impacts of compound extremes on agriculture

Agricultural production and food prices are affected by hydroclimatic extremes. There has been a growing amount of literature measuring the impacts of individual extreme events (heat stress or water stress) on agricultural and human systems. Yet, we lack a comprehensive understanding of the significance and the magnitude of the impacts of compound extremes. This study combines a fine-scale weather product with outputs of a hydrological model to construct functional metrics of individual and compound hydroclimatic extremes for agriculture. Then, a yield response function is estimated with individual and compound metrics, focusing on corn in the United States during the 1981–2015 period. Supported by statistical evidence, the findings suggest that metrics of compound hydroclimatic extremes are better predictors of corn yield variations than metrics of individual extremes. The results also confirm that wet heat is more damaging than dry heat for corn. This study shows the average yield damage from heat stress has been up to four times more severe when combined with water stress.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Simulating dielectric spectra: A demonstration of the direct electric field method and a new model for the nonlinear dielectric response

Here we demonstrate a method to compute the dielectric spectra of fluids in molecular dynamics (MD) by directly applying electric fields to the simulation. We obtain spectra from MD simulations with low magnitude electric fields (≈0.01 V/Å) in agreement with spectra from the fluctuation–dissipation method for water and acetonitrile. We examine this method’s trade-off between noise at low field magnitudes and the nonlinearity of the response at higher field magnitudes. We then apply the Booth equation to describe the nonlinear response of both fluids at low frequency (0.1 GHz) and high field magnitude (up to 0.5 V/Å). We develop a model of the frequency-dependent nonlinear response by combining the Booth description of the static nonlinear dielectric response of fluids with the frequency-dependent linear dielectric response of the Debye model. We find good agreement between our model and the MD simulations of the nonlinear dielectric response for both acetonitrile and water.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2024

A Compound Data Poisoning Technique with Significant Adversarial Effects on Transformer-based Sentiment Classification Tasks

Transformer-based models have demonstrated much success in various natural language processing tasks. However, they are often vulnerable to adversarial attacks, such as data poisoning, which can intentionally fool the model into generating incorrect results. In this article, we present a novel, compound variant of a data poisoning attack on a transformer-based model that maximizes the poisoning effect while minimizing the scope of poisoning. Here we do so by combining the established data poisoning technique (label flipping) with a novel adversarial artifact selection and insertion technique aimed at minimizing detectability and the scope of the poisoning footprint. We find that by using a combination of these two techniques, we achieve a state-of-the-art attack success rate of approximately 90% while poisoning only 0.5% of the original training set, thus minimizing the scope and detectability of the poisoning action. These findings have the potential to advance the development of better data poisoning detection methods.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

Implication of stratospheric aerosol geoengineering on compound precipitation and temperature extremes in Africa

Three Coupled Model Intercomparison Project 5 (CMIP5) models that simulated the G4 experiment of the Geoengineering Model Intercomparison Project (GeoMIP) were used to investigate the impact of stratospheric aerosol injection (SAI) on combined temperature and precipitation extremes in Africa that can have greater negative impacts on human and the environment than individual rainfall or temperature extremes. The examined compound extremes included the dry (R warm|dry and R cold|dry ) and wet (R warm|wet and R cold|wet ) modes assessed during the injection (SAI, 2050-2069) and post-injection (postSAI, 2070-2089) periods compared with the historical period (1986-2005). We found a significant projected change in the occurrence of both wet and dry modes during SAI and postSAI related to the historical period. The magnitude and sign of this change depend on the season and the geographical location. During the SAI and postSAI, the wet (R warm|wet and R cold|wet ) modes are projected to be significantly lower while the dry modes are noted to increase in a large part of African continent depending on the season and the geographical location and may consequently leads to an increase of the droughts prone areas. Furthermore, the termination effect is noted to re duce the occurrence of dry modes, which may reduce the potential negative effects of the injection after halting. As the effect may vary from one region to another and according to the season, it suggested assessing the key sector impacts of SAI. Thus, this change in dry modes due to SAI could affect all activities which depend on water resources such as water supply, agriculture and food production, energy demand, and production with adverse effects on health, security, and sustainable development, but this needs to be assessed and quantified at regional scales.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Local, regional, and global adaptations to a compound pandemic-weather stress event

Global food security can be threatened by short-term extreme events that negatively impact food production, food purchasing power, and agricultural economic activity. At the same time, environmental pollutants like greenhouse gases (GHGs) can be reduced due to the same short-term extreme stressors. Stress events include pandemics like COVID-19 and widespread droughts like those experienced in 2015. Here we consider the question: what if COVID-19 had co-occurred with a 2015-like drought year? Using a coupled biophysical-economic modeling framework, we evaluate how this compound stress would alter both agricultural sector GHG emissions and change the number of undernourished people worldwide. We further consider three interdependent adaptation options: local water use for crop production, regional shifts in cropland area, and global trade of agricultural products. We find that GHG emissions decline due to reduced economic activity in the agricultural sector, but this is paired with large increases in undernourished populations in developing nations. Local and regional adaptations that make use of natural resources enable global-scale reductions in impacted populations via increased global trade.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

Predicting the Cetane Number, Yield Sooting Index, Kinematic Viscosity, and Cloud Point for Catalytically Upgraded Pyrolysis Oil Using Artificial Neural Networks

Abstract The conversion of biomass using fast pyrolysis has the potential to be significantly less expensive at scale compared to alternative methods such as fermentation and gasification. Selective upgrading of the products of fast pyrolysis through chemical catalysis produces compounds with lower oxygen content and lower acidity; however, identifying the specific catalytic pathways for producing viable fuels and fuel additives often requires a trial-and-error approach. Specifically, key properties of the compounds must be experimentally tested to evaluate the viability of the resultant compounds. The present work proposes predictive models constructed with artificial neural networks (ANNs) for cetane number (CN), yield sooting index (YSI), kinematic viscosity (KV), and cloud point (CP), with blind test set median absolute errors of 5.14 cetane units, 3.36 yield sooting index units, 0.07 millimeters squared per second, and 4.89 degrees Celsius, respectively. Furthermore, the cetane number, yield sooting index, kinematic viscosity, and cloud point were predicted for over three hundred expected products from the catalytic upgrading of pyrolysis oil. It was discovered that 130 of these compounds have predicted cetane numbers greater than 40, with four of these compounds possessing predicted yield sooting index values significantly less than that of diesel fuel and predicted viscosities and cloud points comparable to that of diesel fuel.

09 BIOMASS FUELS↗
DOE OSTI2021

Air separation via a two-step solar thermochemical cycle based on (Ba,La) x Sr 1-x FeO 3-δ : Thermodynamic analysis

A two-step solar thermochemical cycle was considered for air separation to produce N2 based on (Ba,La) x Sr 1-x FeO 3-δ perovskite reduction/oxidation (redox) reactions for A-site fractions of 0 ≤ x ≤ 0.2. The cycle steps encompassed (1) thermal reduction and O 2 release via concentrated solar input and (2) re-oxidation with air to uptake O 2 and produce high-purity N 2 . Thermogravimetry at temperatures between 400 and 1100 °C in atmospheres of 0.005 to 90% O 2 /Ar at 1 bar was performed to measure equilibrium nonstoichiometries. The compound energy formalism was applied to model redox thermodynamics for both Ba 2+ and La 3+ substitution. Non-linear regression was used to determine the empirical parameters based on the thermogravimetric measurements. The model was used to define partial molar reaction enthalpies and entropies and predicted equilibrium oxygen nonstoichiometry as functions of oxide stoichiometry, site fraction, temperature, and O 2 partial pressure. The thermodynamic analysis showed the materials are appealing for air separation at temperatures below 800 °C.

42 ENGINEERING↗
DOE OSTI2024

Rapid measurement of soluble xylo-oligomers using near-infrared spectroscopy (NIRS) and multivariate statistics: calibration model development and practical approaches to model optimization

Rapid monitoring of biomass conversion processes using techniques such as near-infrared (NIR) spectroscopy can be substantially quicker and less labor-, resource-, and energy-intensive than conventional measurement techniques such as gas or liquid chromatography (GC or LC) due to the lack of solvents and preparation methods, as well as removing the need to transfer samples to an external lab for analytical evaluation. The purpose of this study was to determine the feasibility of rapid monitoring of a biomass conversion process using NIR spectroscopy combined with multivariate statistical modeling, and to examine the impact of (1) subsetting the samples in the original dataset by process location and (2) reducing the spectral range used in the calibration model on model performance. We develop multivariate calibration models for the concentrations of soluble xylo-oligosaccharides (XOS), monomeric xylose, and total solids at multiple points in a biomass conversion process which produces and then purifies XOS compounds from sugar cane bagasse. A single model using samples from multiple locations in the process stream showed acceptable performance as measured by standard statistical measures. However, compared to the single model, we show that separate models built by segregating the calibration samples according to process location show improved performance. We also show that combining an understanding of the sample spectra with simple multivariate analysis tools can result in a calibration model with a substantially smaller spectral range that provides essentially equal performance to the full-range model. We demonstrate that real-time monitoring of soluble xylo-oligosaccharides (XOS), monomeric xylose, and total solids concentration at multiple points in a process stream using NIR spectroscopy coupled with multivariate statistics is feasible. Segregation of sample populations by process location improves model performance. Models using a reduced spectral range containing the most relevant spectral signatures show very similar performance to the full-range model, reinforcing the importance of performing robust exploratory data analysis before beginning multivariate modeling.

09 BIOMASS FUELS↗
DOE OSTI2025

A physiologically based pharmacokinetic (PBPK) model to align dosimetry of the isobutyl metabolic series in rats and humans

Here, we developed a physiologically based pharmacokinetic (PBPK) model in rats and humans for the isobutyl metabolic series including isobutyl acetate, isobutanol, isobutyraldehyde, and isobutyric acid. Chemical manufactures routinely use these compounds as solvents, for chemical synthesis, as potential biofuels, de-icing fluids, and additives for food and/or fragrance in consumer products. Human exposure to isobutyl compounds can occur through inhalation or oral routes. We previously developed a PBPK model for the propyl metabolic series and utilized it as a framework to create the isobutyl PBPK model due to the chemical similarities between the two series. To support model development, we measured in vitro metabolism of isobutyl acetate in rat and human blood and liver S9 fractions. Compared to rats, humans demonstrated faster isobutyl acetate hydrolysis in liver S9 fractions, while the hydrolysis rates in blood were similar between the two species. We used concentrations of isobutyl compounds measured in air and blood from rats exposed to isobutyl acetate and isobutanol as well as other published data to further parameterize the model. Following exposure to either isobutyl acetate or isobutanol, we observed isobutanol concentrations highest among the isobutyl compounds in the blood of rats. Overall, the model accurately predicts measured time course concentrations of isobutyl acetate, isobutanol, and isobutyric acid in available data in rats and humans. Sensitivity analyses identified alveolar ventilation rates, isobutyl metabolism rates, and cardiac output as the most sensitive parameters affecting concentrations of isobutyl compounds in blood. The isobutyl PBPK model enables comparisons of internal dose metrics across various isobutyl compound exposures and species and allows for calculation of equivalent external exposures that result in the same dose metric. Regulators can employ this PBPK model to predict and align internal dose metrics of isobutyl compounds for risk assessment purposes.

2-methyl-1-propanol↗
DOE OSTI2025

Uncertainty quantification of optical models in fission fragment deexcitation

Here, we take the first step towards incorporating compound nuclear observables at astrophysically relevant energies into the experimental evidence used to constrain optical models, by propagating the uncertainty in two global optical potentials, one phenomenological and one microscopic, to correlated fission observables using the Monte Carlo Hauser-Feshbach formalism. We compare to a wide range of historic and recent experimental fission measurements, and discuss in detail regions of disagreement. We find that the parametric optical model uncertainty in neutron-fragment correlated observables involving neutron energy is significant. On the other hand, we observe that other experimental features, particularly neutron-fragment correlations near the 132 Sn shell closure and the high energy component of neutron spectra, are unlikely to be explained by the optical potential, and will require further experimental and theoretical effort to explain.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2025

Predicting Liquid–Liquid Phase Separation of Submicrometer Proxies for Atmospheric Secondary Aerosol

Liquid–liquid phase separation (LLPS) of atmospheric aerosols can significantly impact climate, air quality, and human health. However, their complex composition, small size, and history-dependent properties result in great uncertainty in the modeling of aerosol phase state and atmospheric processes. Herein, using cryogenic transmission electron microscopy (cryo-TEM), we examined model submicron aerosols composed of organic compounds and ammonium sulfate, and established a parameterization for the separation relative humidity (SRH) that accounts for chemical composition, particle size, and equilibration time. We evaluated different variables that describe chemical composition: O/C ratio, partition coefficient, solubility, molar mass, and polarizability. The O/C ratio fits the SRH of micrometer droplets best, and by using a scaling factor to translate the micrometer SRH parameterization to submicron aerosols, we incorporate the effects of size and equilibration time. The measured scaling factor for the submicron mean SRH (30nm – 1μm, 20 min equilibration times) is 0.80, the factor becomes 1 with equilibration time over 1 hour, and is equal to 0, meaning that SRH is absent, when the aerosol dry diameter is smaller than 30 nm. Furthermore, our parameterization will aid in universal SRH modeling, potentially leading to more accurate predictions of aerosol mass, optical properties, hygroscopicity, and heterogeneous chemistry.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2024

Problem with gradual absorption in MSD/MSC calculations

Replacing classical exciton model with the better founded Multi-step Direct (MSD) and Multistep Compound (MSC) mechanisms has been impeded by incapability of the latter models to describe central part of the neutron emission spectra at incident neutron energies of about 14 MeV and above. We have ascribed this deficiency to the decrease of absorption to the MSC mechanism resulting from the concept of gradual absorption. We were able to obtain very good reproduction of experimentally measured neutron spectra using MSD/MSC calculations when this option was turned off. Such treatment is, however, at odds with the fundamental distinction between MSD and MSC mechanisms that should proceed through the chain of open (P-space) and closed (Q-space) configurations respectively. By blocking gradual absorption we allow the first stage of MSC to be fully populated from the incident channel that at higher incident energies is impossible. We discuss various attempts of addressing the problem that, so far, remains open. In addition, we present an evidence for much tighter spin distribution of particle-hole states than normally assumed.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2026

Simulated moving bed-inspired method for continuous adsorptive denitrogenation of model fuel

Efficient, continuous routes for removing nitrogen-containing compounds from hydrothermal liquefaction-derived synthetic aviation fuel are needed to enable direct blending with conventional jet fuels. Here, we report a simulated moving bed-inspired process for adsorptive denitrogenation of a model fuel. Unlike conventional simulated moving bed systems, which are designed for sharp separations between similar solutes, this approach was run deliberately outside the classical separation region so that both pyridine and indole were removed together from the hydrocarbon stream. Alcohol solvents were used to regenerate the silica adsorbent, maintaining performance over extended operation and avoiding the downtime and energy demand associated with calcination. Under these conditions, the system demonstrates removal of more than 98% of nitrogen while cutting solvent use by 28% compared to batch operation. Classical modeling tools predicted column concentration profiles even in this nontraditional regime, suggesting a straightforward path to scaling. Together, these results motivate solvent-efficient, continuous denitrogenation strategies that could be integrated with biorefinery processes.

Adsorption↗