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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 19 records
All sourcesNASA NTRSDOE OSTI
DOE OSTI2019

Physical conditions for UO formation in laser-produced uranium plumes

Laser-induced plasma spectroscopy is a powerful method for elemental detection and analysis, however the broadening and merging of densely packed spectral lines can make it especially challenging for studying high-Z elements such as uranium and other actinides. In order to understand the processes occurring in laser-induced plasma, it is necessary to evaluate the variation of its thermodynamic parameters. We performed spatiotemporal diagnostics of uranium species in plasma plumes produced by nanosecond near-infrared laser pulses in low- pressure environment. Spatially- and temporally-resolved emission spectroscopy experiments are supported by modeling of uranium emission and its persistence in the plume. Saha-Eggert equation as well as Boltzmann diagrams generated from numerous U I transitions are used to infer temperature. We report the rst measurements of uranium Stark broadening parameters for U I 499.01 nm and U II 500.82 nm transitions. The Stark widths of selected U transitions were measured by comparing their linewidths with the broadening of O I 777.19 nm line. Electron density was found to be on the order of 1016 cm??3, while the temperature was found to be in the range of 3000{8000 K. In addition to enhancing the fundamental understanding of high-Z plasmas under reduced-pressure environments, the knowledge of Stark broadening parameters could improve modeling capabilities and analytical performance of techniques that rely on emission plasma spectroscopy.

(140.3440) Laser-induced breakdown, (280.4788) Opt↗
DOE OSTI2019

Reference Natural Gas Flames at Nominally Autoignitive Engine-Relevant Conditions

Laminar natural gas flames are investigated at engine-relevant thermochemical conditions where the ignition delay time r is short due to very high ambient temperatures and pressures. At these conditions, it is not possible to measure or calculate well-defined values for the laminar flame speed S-l, laminar flame thickness delta(l), and laminar flame time scale tau(l) = delta(l)/S-l due to the explosive thermochemical state. Here, the corresponding reference values, S-R, delta(R), and tau(R) = S-R/S-R, that account for the effects of autoignition, are numerically estimated to investigate the enhancement of flame propagation, and the competition with autoignition that arises under nominally autoignitive conditions (characterised here by the number tau/tau(R)). Large values of tau/tau(R) indicate that autoignition is unimportant, values near or below unity indicate that flame propagation is not possible, and intermediate values indicate that a combination of both flame propagation and autoignition may be important, depending upon factors such as device geometry, turbulence, stratification, et cetera. The reference quantities are presented for a wide range of temperatures, equivalence ratios, pressures, and hydrogen concentrations, which includes conditions relevant to stationary gas turbine reheat burners and boosted spark ignition engines. It is demonstrated that the transition from flame propagation to autoignition is only dependent on residence time, when the results are non-dimensionalised by the reference values. The temporal evolution of the reference values are also reported for a modelled boosted SI engine. It is shown that the nominally autoignitive conditions enhance flame propagation, which may be an ameliorating factor for the onset of engine knock. The calculations are performed using a recently-developed, detailed 177 species mechanism for C0-C3 chemistry that is derived from theoretical chemistry and is suitable for a wide range of thermochemical conditions as it is not tuned or optimised for a particular operating condition. (C) 2018 The Combustion Institute. Published by Elsevier Inc. All rights reserved.

autoignition↗
DOE OSTI2018

Effects of surface initial condition on aqueous corrosion of glass-A study with a model nuclear waste glass

Being a nonequilibrium material, the structure of glass varies with the sample history. Thus, the initial surface condition of a glass can vary with the preparation condition and have a large impact on its reactivity. This paper shows that the aqueous corrosion behavior of international simple glass (ISG) varies depending on the initial surface state. The ISG glass samples were prepared as-polished-only and polished-then-annealed and they were immersed in aqueous solution saturated with soluble SiO 2 at 30°C (modeling a mild condition) and at 90°C (modeling a severe condition). Molecular dynamics simulations were performed to obtain coordination numbers of network formers of ISG to assist oxygen speciation calculations. The surface structures of as-prepared and corroded ISG samples were analyzed using various imaging and spectroscopic techniques. Among these analyses, only the oxygen speciation with x-ray photoelectron spectroscopy showed discernable differences between two uncorroded surfaces with different preparation histories; all other methods could not differentiate the surface preparation history before aqueous corrosion. Such minor difference in chemical structures was found to have a profound impact on corrosion behaviors in the mild condition. In the harsh condition, the surface history dependence was not as drastic as the corrosion in the mild condition. The analysis results of the corroded surfaces suggested that the thickness and structure of the alteration layer formed on ISG in aqueous corrosion can vary with the initial surface state.

nuclear, defects, corrosion↗
DOE OSTI2019

Coastal songbird condition on migratory stopover varies by geographical location and bird age

Abstract The period of migration can pose significant energetic challenges as birds attempt to reach their destinations. Suitable stopover habitat is, therefore, important to the success of migrating individuals, especially as they move along major migration corridors and geographic features, like coastlines. In this study, we used metrics of individual body condition (i.e. fat score, size-corrected body mass, and refueling rate) of fall migrants as they moved across the Gulf of Maine region, a complex coastal landscape. We investigated the extent to which these body condition indices varied by stopover site geography (island vs. mainland) and how these spatial patterns varied with species-specific characteristics such as migratory distance, foraging guild, and age. Geography was an important factor explaining variation in all 3 condition indices, and age explained additional variance in 2 of the 3. In general, individuals captured on islands exhibited significantly lower energetic condition than individuals on the mainland, and this pattern held true across all migratory strategies and foraging guilds. Immature individuals had, on average, lower energy reserves, with less fat and lower size-corrected mass than adults among all stopover sites. We also found that at all sites, size-corrected body mass, on average, significantly increased over the capture day, providing evidence that both island and mainland sites provided energetically beneficial stopover habitat. Our finding that birds offshore are in lower body condition at capture than those along the coast is suggestive that birds use mainland sites for longer stopover bouts than island sites, or that poorer condition birds reorient to land during over-water movements disproportionately use off-shore islands as their initial landing area. Decreases in either island or mainland stopover site availability or quality may affect individual fitness, with population-level consequences, but through different ways.

Zoology↗
DOE OSTI2026

Data for FUN-PROSE: A Deep Learning Approach to Predict Condition-Specific Gene Expression in Fungi

mRNA levels of all genes in a genome is a critical piece of information defining the overall state of the cell in a given environmental condition. Being able to reconstruct such condition-specific expression in fungal genomes is particularly important to metabolically engineer these organisms to produce desired chemicals in industrially scalable conditions. Most previous deep learning approaches focused on predicting the average expression levels of a gene based on its promoter sequence, ignoring its variation across different conditions. Here we present FUN-PROSE—a deep learning model trained to predict differential expression of individual genes across various conditions using their promoter sequences and expression levels of all transcription factors. We train and test our model on three fungal species and get the correlation between predicted and observed condition-specific gene expression as high as 0.85. We then interpret our model to extract promoter sequence motifs responsible for variable expression of individual genes. We also carried out input feature importance analysis to connect individual transcription factors to their gene targets. A sizeable fraction of both sequence motifs and TF-gene interactions learned by our model agree with previously known biological information, while the rest corresponds to either novel biological facts or indirect correlations.

Genomics↗
DOE OSTI2019

The modeling of the turbulent reaction rate under high-pressure conditions: A priori evaluation of the Conditional Source-term Estimation concept

With the goal of obtaining an accurate model of the turbulent reaction rate for Large Eddy Simulations (LESs), the modeling of turbulence-chemistry-thermodynamic interaction is addressed through an a priori study of a Direct Numerical Simulation (DNS) database representing high-pressure turbulent combustion. The DNS database consists of simulations of a temporal mixing layer in which a single-step chemical reaction occurs. The potential of the single-conditioned Conditional Source-term Estimation (CSE) approach to model the filtered turbulent reaction rate needed for conducting LES is examined. Evaluations conducted with the mixture fraction as a conditioning variable at two filter widths and with the probability density function (PDF) extracted from the DNS database that represents the mixture fraction, show that the deviation between the model and template is large and substantially increases with filter width. To address this deviation, the Double-conditioned CSE (DCSE) approach is explored with two different second conditioning variables, the first conditioning variable still being the mixture fraction; four filter widths are considered. The first choice of the second conditioning variable is a normalized progress variable based on the CO 2 mass fraction and the second choice of the second conditioning variable is a normalized temperature. With each second conditional variable, the DCSE results represent a substantial improvement over CSE, by as much as an order of magnitude when measured by the relative error from the DNS-extracted filtered reaction rate. A quantitative test based on a root mean square identifies the reason for the DCSE success compared to CSE: unlike in CSE, the DCSE is able to substantially reduce the fluctuations of the modeled reaction rate from the filtered reaction rate over the entire range of the filtered reaction rate values, and particularly at the larger reaction rate values for which the DCSE model has higher fidelity. The results are shown to be equally favorable for the two conditioning variables. Consideration of DNS realizations at higher free-stream pressure or larger Reynolds number shows that the results are essentially equally successful at other pressure or Reynolds number values.

42 ENGINEERING↗
DOE OSTI2021

Dissecting the metabolic reprogramming of maize root under nitrogen-deficient stress conditions

The growth and development of maize (Zea mays L.) largely depends on its nutrient uptake through the root. Hence, studying its growth, response, and associated metabolic reprogramming to stress conditions is becoming an important research direction. A genome-scale metabolic model (GSM) for the maize root was developed to study its metabolic reprogramming under nitrogen stress conditions. The model was reconstructed based on the available information from KEGG, UniProt, and MaizeCyc. Transcriptomics data derived from the roots of hydroponically grown maize plants were used to incorporate regulatory constraints in the model and simulate nitrogen-non-limiting (N+) and nitrogen-deficient (N-) condition. Model-predicted flux-sum variability analysis achieved 70% accuracy compared with the experimental change of metabolite levels. In addition to predicting important metabolic reprogramming in central carbon, fatty acid, amino acid, and other secondary metabolism, maize root GSM predicted several metabolites (l-methionine, l-asparagine, l-lysine, cholesterol, and l-pipecolate) playing a regulatory role in the root biomass growth. Furthermore, this study revealed eight phosphatidylcholine and phosphatidylglycerol metabolites which, even though not coupled with biomass production, played a key role in the increased biomass production under N-deficient conditions. Overall, the omics-integrated GSM provides a promising tool to facilitate stress condition analysis for maize root and engineer better stress-tolerant maize genotypes.

Plant Sciences↗
DOE OSTI2020

Highlighting the Impact of Yaw Control by Parsing Atmospheric Conditions Based on Total Variation: Preprint

Identification of atmospheric conditions within a multivariate atmospheric dataset is a necessary step in the validation of wind plant control strategies. Most often, operating conditions are characterized in terms of aggregated observations and assume that the atmosphere is 'quasi-steady'. Aggregation of observations without regard to covariance between time series discounts the dynamical nature of the atmosphere and is not sufficiently representative of wind plant operating conditions. Identification and characterization of continuous time periods with atmospheric conditions that have a high value for analysis or simulation sets the stage for more advanced model validation and the development of real-time control and operation strategies. Controlling observational data for statistical stationarity highlights significant enhancements to the power production of waked turbines under wake steering wind plant control. Considering the combined energy ratio of the turbine to which yaw offsets are prescribed as well as the waked turbine show only moderate improvements when filtering for total validation. The same quality control that highlights wake steering also serves to underpin the off-nominal operation of the controlled turbine.

17 WIND ENERGY↗
DOE OSTI2023

Voltage-Based Strategies for Preventing Battery Degradation under Diverse Fast-Charging Conditions

Maintaining safe operating conditions is a key challenge for high-performance lithium-ion battery applications. The lithium-plating reaction remains a risk during charging, but limited studies consider the highly variable charging conditions possible in commercial cells. Here we combine pseudo-2D electrochemical modeling with data visualization methods to reveal important relationships between the measurable cell voltage and difficult-to-predict Li-plating onset criteria. An extensively validated model is used to compute Li plating for thousands of multistep charging conditions spanning diverse rates, temperatures, states-of-charge, and cell aging. Here we observe an empirical cell operating voltage limit below which plating does not occur across all conditions, and this limit varies with the battery state-of-charge and aging. A model sensitivity analysis also indicates that, when comparing two charging voltage profiles, the capacity difference at 4.0 V correlates well with the difference in the plating onset capacity. These results encourage simple strategies for Li-plating prevention that are complementary to existing battery controls.

25 ENERGY STORAGE↗
DOE OSTI2021

Metabolic Interactions between Brachypodium and Pseudomonas fluorescens under Controlled Iron-Limited Conditions

Plant iron (Fe) nutritional status has a significant impact on rhizosphere microbial communities. While this effect has been attributed to alterations in the composition of root exudates, the underlying mechanisms are not known. Here, we investigated the effect of Fe deficiency on the interactions between the grass Brachypodium distachyon and the common soil bacteria Pseudomonas fluorescens SBW25. Increased phytosiderophore production was the most significant response of Brachypodium root exudation to Fe deficiency. We observed upregulated expression of phytosiderophore biosynthesis genes but lower accumulated exudate concentrations in the presence of Pseudomonas, indicating that the bacteria degrade these phytosiderophores. Pseudomonas did not produce endogenous siderophores under the low Fe conditions. Rather, the bacterial transcriptome revealed upregulation of four genes in response to Fe deficiency that contain motifs associated with cellular uptake of small molecules. Collectively, these results suggest that rhizosphere Pseudomonas fluorescens may take up phytosiderophores produced by grasses in response to Fe deficiency, and we propose target genes that may be involved. Our findings provide insight into the molecular basis for the exchange of C, N, and Fe between plants and bacteria under Fe deficient conditions. These interactions may contribute to differences in the rhizosphere microbial community structure across soil pH regimes.

Boiteau, Rene M.↗
DOE OSTI2021

A Framework for Characterizing the Initial Thermal Conditions of Light-Duty Vehicles in Response to Representative Utilization Patterns, Ambient Conditions, and Vehicle Technologies

It is widely understood that the thermal state of a light-duty vehicle at the beginning of a trip influences the vehicle performance throughout the drive cycle. Cold starts, or initial states with component temperatures near ambient conditions, are strongly correlated with reduced vehicle performance and energy efficiency and with increased emissions. Despite this understanding, there is little literature available that characterizes initial thermal states beyond empirical studies and simplified analyses of dwell times. We introduce a framework that considers vehicle activity patterns, including the previous drive event, duration of the previous dwell event, and relevant ambient conditions occurring during these events. Moreover, the framework allows for technologies to influence the prominence of cold starts and warm starts. An example application of the methodology using a powertrain model approach revealed the significance of all constituent factors: controlling for ambient conditions, the shortest dwell lengths corresponded to an average initial oil temperature of 70°C versus 23°C for the long dwell length simulations, and aggressive conditioning cycles corresponded to an average initial oil temperature of 51°C versus only 35°C for a short conditioning cycle. The coldest ambient conditions evaluated (-30°C to -10°C) produced a weighted initial oil temperature of 19°C versus 57°C for the warmest ambient conditions evaluated (30°C to 50°C). Finally, we introduce weighting factors that account for the real-world frequency of conditioning cycle and dwell length combinations. The procedure outlined may be useful for discovering real-world performance improvements associated with vehicle technologies, a topic of interest to both vehicle manufacturers and regulators.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2021

Role of Inlet Boundary Conditions on Fuel-Air Mixing at Supercritical Conditions

Advanced engine design and alternative fuels present the possibility of fuel injection at purely supercritical conditions in diesel engines and gas turbines. The complex interactions that govern this phenomenon still need significant research, particularly the boundary conditions for fuel injection are critical for accurate simulation. However, the flow inside the injector itself is often omitted to reduce the computational efforts, and thus, velocity, mass flux, or total pressure is specified at the injector exit (or domain inlet), often with simplified velocity profiles and turbulence levels. This simplified inlet boundary treatment has minimal effects on results for conventional fuel injection conditions, however, the validity of this approach at supercritical conditions has not been assessed. Comprehensive real-gas and binary fluid mixing models have been implemented for computational fluid dynamics (CFD) analysis of fuel-air mixing at supercritical conditions. The model is verified using prior CFD results from the literature. The model is used to investigate the effects of the shape of axial velocity and mass fraction profiles at the inlet boundary with the goal to improve the comparison of predictions to experimental data. Results show that the boundary conditions have a significant effect on the predictions, and none of the cases match precisely with experimental data. Furthermore, the study reveals that the physical location of the inlet boundary might be difficult to infer correctly from the experiments and highlights the need for high-quality, repeatable measurements at supercritical conditions to support the development of relevant high-fidelity models for fuel-air mixing.

02 PETROLEUM↗
DOE OSTI2019

Integrated Two-Stage Alkaline-Oxidative Pretreatment of Hybrid Poplar. Part 1: Impact of Alkaline Pre-Extraction Conditions on Process Performance and Lignin Properties

We previously demonstrated that a two-stage pretreatment comprising of an alkaline pre-extraction followed by a Cu-catalyzed alkaline–oxidative treatment is effective at pretreating hardwoods under relatively mild reaction conditions. In this work, we focus on characterizing how biomass source and reaction conditions used during the alkaline pre-extraction impact the subsequent processing stages as well as lignin yields and properties. Specifically, three hybrid poplars were subjected to the first stage alkaline pre-extraction under various conditions including differences in time (15–300 min), temperature (95–155 °C), and alkali loadings (50–200 mg NaOH/g biomass), and the impact on total mass solubilization, lignin recovery, and lignin purity was determined. Empirical correlations were developed between reaction conditions and mass solubilization and lignin recovery during the pre-extraction stage. For select conditions, lignin properties were assessed and include β-O-4 content determined by 13C NMR, molecular mass distributions as determined by gel permeation chromatography, and susceptibility to depolymerization to aromatic monomers using thioacidolysis and formic acid catalyzed depolymerization. We found alkaline pre-extraction performed at higher temperatures generated lignins exhibiting lower contamination by polysaccharides, lower aromatic monomer yields from depolymerization, lower molar masses, and lower β-O-4 contents relative to the lower temperature pre-extractions. Lastly, the pre-extracted biomass from select conditions was assessed for its response to the subsequent Cu-catalyzed alkaline–oxidative treatment and enzymatic hydrolysis. It was demonstrated that minor differences in delignification during pre-extraction have quantifiable impacts on the subsequent efficacy of the second stage of pretreatment and enzymatic hydrolysis with improved lignin removal during the first pre-extraction stage resulting in improved hydrolysis yields.

09 BIOMASS FUELS↗