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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 181 records · Page 10
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Kinetic Modeling of Secondary Organic Aerosol in a Weather-Chemistry Model: Parameterizations, Processes, and Predictions for GOAmazon

Secondary organic aerosol (SOA) forms and evolves in the atmosphere through many pathways and processes, over diverse spatial and time scales. Hence, there is a need to represent these widely-varying kinetic processes in large-scale atmospheric models to allow for accurate predictions of the abundance, properties, and impacts of SOA. In this work, we integrated a kinetic, process-level model (simpleSOM-MOSAIC) into a weather-chemistry model (WRF-Chem) to simulate the oxidation chemistry and microphysics of atmospheric SOA. simpleSOM-MOSAIC simulates multigenerational gas-phase chemistry, autoxidation reactions, heterogeneous oxidation, oligomerization, and phase-state-influenced gas/particle partitioning of SOA. As a case study, the integrated WRF-Chem-simpleSOM-MOSAIC (WC-SSM) model was used to simulate the photochemical evolution downwind of a large city (Manaus, Brazil) in the Amazon and, in turn, study the anthropogenic and biogenic interactions in an otherwise pristine environment. Consistent with previous work, we found that OA was enhanced by up to a factor of four in the urban plume due to elevated hydroxyl radical (OH) concentrations, relative to the background, and that this OA was dominated by SOA from biogenic precursors (80%). Further, in addition to accurately simulating the OA enhancement in the urban plume, the model reproduced the magnitude of the OA oxygen-to-carbon (O:C) ratio and broadly tracked the evolution of the aerosol size distribution. Our work highlights the importance of including an integrated, kinetic representation of SOA processes in an atmospheric model

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2015

Quantum process tomography by 2D fluorescence spectroscopy

Reconstruction of the dynamics (quantum process tomography) of the single-exciton manifold in energy transfer systems is proposed here on the basis of two-dimensional fluorescence spectroscopy (2D-FS) with phase-modulation. The quantum-process-tomography protocol introduced here benefits from, e.g., the sensitivity enhancement ascribed to 2D-FS. Although the isotropically averaged spectroscopic signals depend on the quantum yield parameter Γ of the doubly excited-exciton manifold, it is shown that the reconstruction of the dynamics is insensitive to this parameter. Applications to foundational and applied problems, as well as further extensions, are discussed.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2014

The exit-time problem for a Markov jump process

The purpose of this paper is to consider the exit-time problem for a finite-range Markov jump process, i.e, the distance the particle can jump is bounded independent of its location. Additionally, such jump diffusions are expedient models for anomalous transport exhibiting super-diffusion or nonstandard normal diffusion. We refer to the associated deterministic equation as a volume-constrained nonlocal diffusion equation. The volume constraint is the nonlocal analogue of a boundary condition necessary to demonstrate that the nonlocal diffusion equation is well-posed and is consistent with the jump process. A critical aspect of the analysis is a variational formulation and a recently developed nonlocal vector calculus. Finally, this calculus allows us to pose nonlocal backward and forward Kolmogorov equations, the former equation granting the various moments of the exit-time distribution.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2016

Defense Waste Processing Facility Simulant Chemical Processing Cell Studies for Sludge Batch 9

The Savannah River National Laboratory (SRNL) received a technical task request from Defense Waste Processing Facility (DWPF) and Saltstone Engineering to perform simulant tests to support the qualification of Sludge Batch 9 (SB9) and to develop the flowsheet for SB9 in the DWPF. These efforts pertained to the DWPF Chemical Process Cell (CPC). CPC experiments were performed using SB9 simulant (SB9A) to qualify SB9 for sludge-only and coupled processing using the nitric-formic flowsheet in the DWPF. Two simulant batches were prepared, one representing SB8 Tank 40H and another representing SB9 Tank 51H. The simulant used for SB9 qualification testing was prepared by blending the SB8 Tank 40H and SB9 Tank 51H simulants. The blended simulant is referred to as SB9A. Eleven CPC experiments were run with an acid stoichiometry ranging between 105% and 145% of the Koopman minimum acid equation (KMA), which is equivalent to 109.7% and 151.5% of the Hsu minimum acid factor. Three runs were performed in the 1L laboratory scale setup, whereas the remainder were in the 4L laboratory scale setup. Sludge Receipt and Adjustment Tank (SRAT) and Slurry Mix Evaporator (SME) cycles were performed on nine of the eleven. The other two were SRAT cycles only. One coupled flowsheet and one extended run were performed for SRAT and SME processing. Samples of the condensate, sludge, and off-gas were taken to monitor the chemistry of the CPC experiments.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2017

Impact of Salt Waste Processing Facility Streams on the Nitric-Glycolic Flowsheet in the Chemical Processing Cell

An evaluation of the previous Chemical Processing Cell (CPC) testing was performed to determine whether the planned concurrent operation, or “coupled” operations, of the Defense Waste Processing Facility (DWPF) with the Salt Waste Processing Facility (SWPF) has been adequately covered. Tests with the nitricglycolic acid flowsheet, which were both coupled and uncoupled with salt waste streams, included several tests that required extended boiling times. This report provides the evaluation of previous testing and the testing recommendation requested by Savannah River Remediation. The focus of the evaluation was impact on flammability in CPC vessels (i.e., hydrogen generation rate, SWPF solvent components, antifoam degradation products) and processing impacts (i.e., acid window, melter feed target, rheological properties, antifoam requirements, and chemical composition).

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2021

Defense Waste Processing Facility Nitric-Glycolic Flowsheet Chemical Process Cell Chemistry: Part 2

The conversions of nitrite to nitrate, the destruction of glycolate, and the conversion of glycolate to formate and oxalate were modeled for the Nitric-Glycolic flowsheet using data from Chemical Process Cell (CPC) simulant runs conducted by Savannah River National Laboratory (SRNL) from 2011 to 2016. The goal of this work was to develop empirical correlation models to predict these values from measurable variables from the chemical process so that these quantities could be predicted a-priori from the sludge or simulant composition and measurable processing variables. The need for these predictions arises from the need to predict the REDuction/OXidation (REDOX) state of the glass from the Defense Waste Processing Facility (DWPF) melter. This report summarizes the work on these correlations based on the aforementioned data. Previous work on these correlations was documented in a technical report covering data from 2011-2015. This current report supersedes this previous report. Further refinement of the models as additional data are collected is recommended. The glass REDOX depends on the concentrations of nitrate and manganese (oxidants), and of glycolate, formate, oxalate, carbon, and antifoam (reductants) in the melter feed. The waste sludge contains nitrite, nitrate, manganese (Mn), and oxalate. Virtually all of the nitrite is converted to nitrate or NO+NO 2 +N 2 O gases in the CPC. The portion of the nitrite converted to nitrate increases the amount of nitrate in the sludge. The amount of glycolate in the final melter feed depends on the amount of the glycolic acid feed that is destroyed. Similarly, the amounts of formate and oxalate formed during the decomposition of glycolic acid are required. The material balance on carbon was found to not close in most cases. Generally, there was less carbon at the end of testing compared to the inputs. The most uncertain product variable was glycolate, so material balances were performed where the glycolate concentration was adjusted, usually upward, to close the balance. Correlation versus the original, as-measured, data was generally poor, but correlation against the material balance adjusted values was greatly improved. It was also shown that the correlation of the measured REDOX versus the predicted REDOX was much better when the material balance adjusted glycolate values were used. Three data series were primarily used during the regressions of the data; these series were 1) Sludge Batch 9 NG flowsheet simulant runs NG51-62 (SB9-NG); 2) Scaled Runs + Bounding Hydrogen Runs (SR+BH); and 3) Runs GN43-50 and 57 (43-50,57). The glycolate destruction was found to correlate with acid stoichiometry (AS), percent reducing acid (PRA), and for some data series, headspace to simulant volume ratio (HSV), mercury (Hg), and nitrate. Although glycolate destruction for pairs of data series (e.g., [SB9-NG] and [SR+BH]) were found to depend on HSV, the combination of all three data series was not found to have significant dependence on this variable. The best model for glycolate destruction depended on AS, nitrate, and Hg. This model predicted the product glycolate compositions of the data to within 92-106%. The conversion of glycolate to formate was high when noble metals and Hg were not present, with values up to 100%. When noble metals and Hg were present, this conversion ranged from zero to 7%, and was dependent on AS. Lower AS gave higher conversions to formate. The conversion to oxalate was found to depend on the AS and the initial concentration of nitrite. An alternative fit versus AS and the form of ruthenium (Ru) used is a possible alternative. This fit was somewhat less statistically significant. This second model predicts that more oxalate is formed when Ru-nitrosyl nitrate is used rather than Ru chloride. The conversion of glycolate to oxalate ranged from zero to 6%. The conversion of nitrite to nitrate depended primarily on AS and PRA, with HSV and Hg being significant when these variables were varied. For multiple series of data, nitrite was also needed to SRNL-STI-2017-00172 5HYLVLRQ viL distinguish between data series, and the effect of HSV became insignificant. The best model for nitrite to nitrate conversion depended on AS, PRA, nitrite, and Hg. The 95% confidence intervals on the predicted values of glycolate destruction, glycolate to oxalate conversion, and nitrite to nitrate conversion were used to determine the uncertainty in the predicted REDOX when starting with only the composition of the sludge, AS, and PRA. Using the 95% confidences on an individual value (that is the confidence in getting a particular value for one single test as opposed to what the mean would be for multiple tests), the uncertainty in the predicted REDOX was calculated. The uncertainty in the actual product composition glycolate, oxalate, formate, and nitrate concentrations translated to an uncertainty in the REDOX value of ±0.1,which is approximately the uncertainty claimed in the REDOX model itself.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2022

Thermal Process Intensification: Transforming the Way Industry Uses Thermal Process Energy

The US Department of Energy’s (DOE’s) Advanced Manufacturing Office held the virtual workshop entitled “Thermal Process Intensification: Transforming the Way Industry Uses Thermal Process Energy” in November and December 2020. The workshop brought together participants from universities/laboratories, industries, equipment manufacturers, technology vendors, nongovernmental organizations, and subject-matter experts to discuss transformative technologies and strategies to substantially improve the performance (e.g., energy productivity, thermal efficiency, reduced greenhouse gas [GHG] emissions, reduced number of process steps) of thermal processing systems in the industrial sector.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2024

Development and Validation of a Process Model and Open-Source Process Simulator for Microalgae-Based Tertiary Phosphorus Recovery

Microalgae-based tertiary wastewater treatment has the potential to meet stringent effluent phosphorus limits, with the added benefit of producing a marketable feedstock. However, the lack of validated mechanistic models and their implementation in process simulators have limited the adoption of this technology. In this study, an updated lumped pathway metabolic model (Phototrophic-Mixotrophic Process Model, PM 2 ), including both photoautotrophic and heterotrophic metabolisms of microalgae, was developed to predict effluent phosphorus concentration and biomass yield in response to dynamic influent and varying environmental conditions. The model was implemented in QSDsan – an open-source, Python-based design and simulation platform – for robust simulation under uncertainty. A global sensitivity analysis was performed to prioritize model parameters for calibration. The model was then calibrated and validated using batch experimental data and 45 days of continuous online monitoring data from a full-scale (568 m 3 ·d -1 ) microalgae-based tertiary wastewater treatment plant (EcoRecover process). In particular, along with dynamic influent composition, temperature and light intensity data with diel variation were provided as model inputs to reflect the microalgal behavior under day-night cycling. Overall, the QSDsan-based microalgae process simulator was able to predict effluent phosphorus within 0.02–0.04 mg-P·L -1 , while also capturing the general trends of state variables according to nutrient availability.

Lumped pathway metabolic model↗
DOE OSTI2017

Galaxy–Galaxy Weak-lensing Measurements from SDSS. I. Image Processing and Lensing Signals

We present our image processing pipeline that corrects the systematics introduced by the point-spread function (PSF). Using this pipeline, we processed Sloan Digital Sky Survey (SDSS) DR7 imaging data in r band and generated a galaxy catalog containing the shape information. Based on our shape measurements of the galaxy images from SDSS DR7, we extract the galaxy–galaxy (GG) lensing signals around foreground spectroscopic galaxies binned in different luminosities and stellar masses. We estimated the systematics, e.g., selection bias, PSF reconstruction bias, PSF dilution bias, shear responsivity bias, and noise rectification bias, which in total is between −9.1% and 20.8% at 2 σ levels. The overall GG lensing signals we measured are in good agreement with Mandelbaum et al. The reduced χ {sup 2} between the two measurements in different luminosity bins are from 0.43 to 0.83. Larger reduced χ {sup 2} from 0.60 to 1.87 are seen for different stellar mass bins, which is mainly caused by the different stellar mass estimator. The results in this paper with higher signal-to-noise ratio are due to the larger survey area than SDSS DR4, confirming that more luminous/massive galaxies bear stronger GG lensing signals. We divide the foreground galaxies into red/blue and star-forming/quenched subsamples and measure their GG lensing signals. We find that, at a specific stellar mass/luminosity, the red/quenched galaxies have stronger GG lensing signals than their counterparts, especially at large radii. These GG lensing signals can be used to probe the galaxy–halo mass relations and their environmental dependences in the halo occupation or conditional luminosity function framework.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2017

Modeling Global Soil Carbon and Soil Microbial Carbon by Integrating Microbial Processes into the Ecosystem Process Model TRIPLEX-GHG

Microbial physiology plays a critical role in the biogeochemical cycles of the Earth system. However, most traditional soil carbon models are lacking in terms of the representation of key microbial processes that control the soil carbon response to global climate change. In this study, the improved process-based model TRIPLEX-GHG was developed by coupling it with the new MEND (Microbial-ENzyme-mediated Decomposition) model to estimate total global soil organic carbon (SOC) and global soil microbial carbon. The new model (TRIPLEX-MICROBE) shows considerable improvement over the previous version (TRIPLEX-GHG) in simulating SOC. We estimated the global soil carbon stock to be approximately 1195 Pg C, with 348 Pg C located in the high northern latitudes, which is in good agreement with the well-regarded Harmonized World Soil Database (HWSD) and the Northern Circumpolar Soil Carbon Database (NCSCD). We also estimated the global soil microbial carbon to be 21 Pg C, similar to the 23 Pg C estimated. We found that the microbial carbon quantity in the latitudinal direction showed reversions at approximately 30°N, near the equator and at 25°S. A sensitivity analysis suggested that the tundra ecosystem exhibited the highest sensitivity to a 1°C increase or decrease in temperature in terms of dissolved organic carbon (DOC), microbial biomass carbon (MBC) and mineral-associated organic carbon (MOC). Furthermore, our work represents the first step towards a new generation of ecosystem process models capable of integrating key microbial processes into soil carbon cycles.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Experimental and computational design tools for industrial drying processes: Challenges in process‐limit prediction

Abstract The coating and drying of inks and slurries are important steps to manufacture a plethora of products. Drying processes, particularly, comprise energy‐intensive steps that affect product cost and quality. Prior work has highlighted failures of various multicomponent diffusivity models to conserve mass in dryer modeling and challenges in predicting process limits given variability in published values of key thermodynamic parameters. Herein, we develop a computational model and benchtop drying experiments to investigate these concerns for drying polymer‐laden coatings. Model predictions of process limits in a single‐zone drying oven demonstrate that published variability in Flory–Huggins parameter yields large variations in predicted operating temperatures above which blistering occurs. This indicates that caution should be exercised when choosing approaches to obtain or predict the Flory–Huggins parameter, and that both benchtop drying experiments and a set of additional experiments, such as sorption experiments, are needed to fully characterize and optimize a given drying process.

Parrish, Chance↗
DOE OSTI2015

Evidence of Cu I /Cu II Redox Process by X‐ray Absorption and EPR Spectroscopy: Direct Synthesis of Dihydrofurans from β‐Ketocarbonyl Derivatives and Olefins

Abstract The Cu I /Cu II and Cu I /Cu III catalytic cycles have been subject to intense debate in the field of copper‐catalyzed oxidative coupling reactions. A mechanistic study on the Cu I /Cu II redox process, by X‐ray absorption (XAS) and electron paramagnetic resonance (EPR) spectroscopies, has elucidated the reduction mechanism of Cu II to Cu I by 1,3‐diketone and detailed investigation revealed that the halide ion is important for the reduction process. The oxidative nature of the thereby‐formed Cu I has also been studied by XAS and EPR spectroscopy. This mechanistic information is applicable to the copper‐catalyzed oxidative cyclization of β‐ketocarbonyl derivatives to dihydrofurans. This protocol provides an ideal route to highly substituted dihydrofuran rings from easily available 1,3‐dicarbonyls and olefins.

Yi, Hong↗
DOE OSTI2020

Relating Texture and Thermomechanical Processing Variables in Mg–Zn–Ca Alloys

It is well known that the strong basal texture commonly produced in magnesium alloy sheets leads to poor formability at room temperature. A sizable body of work has explored how changing the alloy composition and rolling conditions can yield more desirable textures; however important thermomechanical variables, such as the feed rate during rolling, are often not included in the literature, making it difficult to correlate how changes in processing affect the final crystallographic texture. This work explores the texture evolution and grain refinement in Mg-Zn-Ca alloys during plane strain compression (PSC) using a Gleeble thermomechanical simulator. This instrument allows for precise control and capture of the thermomechanical history of the sample. The texture and grain morphology of the compressed samples were characterized using Electron Backscatter Diffraction (EBSD). The texture results will be used to identify which alloys and processing conditions should be scaled up for future rolling studies.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Accounting for connectivity alters the apparent roles of spatial and environmental processes on metacommunity assembly

Understanding the relative contributions of spatial and environmental processes on community assembly is a central question in ecology. Despite this long-standing interest, our understanding of how landscape structure may drive spatial processes of community assembly remains poorly understood in part because of the challenge of tracking community assembly across landscapes and quantifying key aspects of landscapes that may impact assembly processes. Here, we examined the roles of spatial and environmental processes on structuring assemblies of ants in 72 cleared patches embedded within a forested landscape.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2018

Identification of major matrix metalloproteinase-20 proteolytic processing products of murine amelogenin and tyrosine-rich amelogenin peptide using a nuclear magnetic resonance spectroscopy based method

Objective: The aim of this study was to identify major matrix metalloproteinase-20 (MMP20) proteolytic processing products of amelogenin over time and determine if the tyrosine-rich amelogenin peptide (TRAP) was a substrate of MMP20. Design: Recombinant 15N-labeled murine amelogenin and 13C,15N-labeled TRAP were incubated with MMP20 under conditions where amelogenin self-assembles into nanospheres. Digestion products were fractionated by reverse-phase high-performance liquid chromatography at various time points. Product identification took advantage of the intrinsic disorder property of amelogenin that results in little change to its fingerprint 1H-15N heteronuclear single-quantum coherence nuclear magnetic resonance spectrum in 2% acetic acid upon removing parts of the protein, allowing cleavage site identification by observing which amide cross peaks disappear. Results: The primary product in five out of the six major reverse-phase high-performance liquid chromatography bands generated after a 24 hour incubation of murine amelogenin with MMP20 were: S55-L163, P2-L147, P2-E162, P2-A167, and P2-R176. After 72 hours these products were replaced with five major reverse-phase high-performance liquid chromatography bands containing: L46-A170, P2-S152, P2-F151, P2-W45, and short N-terminal peptides. TRAP was completely digested by MMP20 into multiple small peptides with the initial primary site of cleavage between S16 and Y17. Conclusions: Identification of the major MMP20 proteolytic products of amelogenin confirm a dynamic process, with sites towards the C-terminus more rapidly attacked than sites near the N-terminus. This observation is consistent with nanosphere models where the C-terminus is exposed and the N-terminus more protected. One previously reported end-product of the MMP20 proteolytic processing of amelogenin, TRAP, is shown to be an in vitro substrate for MMP20.

amelogenesis imperfecta, protease, amelogenin, ena↗