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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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All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Measurements of Electron Beam-Induced Spin-Relaxation in Frozen-Spin Hydrogen-Deuteride (HD)

Targets of solid hydrogen-deuteride (HD) can be prepared in a frozen-spin state with spin relaxation times (T1) in excess of a year. The present work has studied their potential use in experiments with electron beams. Polarization relaxation rates have been measured during exposure to sub-nanoAmp currents of 9.7 MeV electron beams from the recently commissioned Upgraded Injector Test Facility (UITF) at Jefferson Lab (JLab). These UITF measurements can be used to anticipate the expected performance at the GeV energies typical of JLab experiments, since the energy deposition in a target is almost independent of beam energy. An in-beam dilution refrigerator equipped with superconducting solenoids has been used to maintain solid HD samples at ?0.1 K and ?1 tesla, and internal NMR coils have been used to monitor hydrogen polarization. Spin-relaxation in two frozen-spin targets, with initial H-polarizations of 40% and 34%, have been tracked while exposed to beams under varying conditions of current, dose, beam duty factor and temperature. At a fixed accumulated dose, the spin-relaxation rates drop with current, suggesting depolarization by the charge cloud of the beam. Imposing a duty factor on the otherwise continuous UITF beam, with forced millisecond scale gaps, has shown no obvious correlation to polarization loss rates. After an accumulated dose of ?2 ?C/cm2, beam-off spin-relaxation rates drop from their immeasurably long pre-irradiation values to the order of weeks (with beam-on T1 values typically an order of magnitude shorter), reflecting a buildup of paramagnetic charge centers within the HD lattice. The accumulated polarization loss was approximately proportional to dose in both targets, dropping to 1/e of their initial values after ?6 ?C/cm2. Thermal equilibrium polarizations of targets not in the frozen-spin state (with intentionally short T1) have been used to deduce the in situ temperature of solid HD while under electron bombardment. A model for depolarization by beam-associated paramagnetic impurities largely accounts for the data, and suggests that improvements in heat removal could lead to significant increases in the in-beam T1.

O'Connell, Thomas↗
DOE OSTI2024

Accelerating multiscale electronic stopping power predictions with time-dependent density functional theory and machine learning

Knowing the rate at which particle radiation releases energy in a material, the “stopping power,” is key to designing nuclear reactors, medical treatments, semiconductor and quantum materials, and many other technologies. While the nuclear contribution to stopping power, i.e., elastic scattering between atoms, is well understood in the literature, the route for gathering data on the electronic contribution has for decades remained costly and reliant on many simplifying assumptions, including that materials are isotropic. We establish a method that combines time-dependent density functional theory (TDDFT) and machine learning to reduce the time to assess new materials to hours on a supercomputer and provide valuable data on how atomic details influence electronic stopping. Our approach uses TDDFT to compute the electronic stopping from first principles in several directions and then machine learning to interpolate to other directions at a cost of 10 million times fewer core-hours. We demonstrate the combined approach in a study of proton irradiation in aluminum and employ it to predict how the depth of maximum energy deposition, the “Bragg Peak,” varies depending on the incident angle—a quantity otherwise inaccessible to modelers and far outside the scales of quantum mechanical simulations. The lack of any experimental information requirement makes our method applicable to most materials, and its speed makes it a prime candidate for enabling quantum-to-continuum models of radiation damage. The prospect of reusing valuable TDDFT data for training the model makes our approach appealing for applications in the age of materials data science.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Advancing Multiscale Simulation of Plasma-Surface Interfaces

We report the development of an atomistic-informed, surface-state-dependent predictive model for particle exchange in a carbon-tungsten plasma-surface interface. The predictive model uses machine learning (ML) techniques to learn the energy and angular distributions for particle exchange and rate functions for surface state evolution from molecular dynamics simulations of cumulative bombardment of tungsten by energetic carbon ions. Each predictive component is sensitive to the energy and trajectory of incident plasma species and the surface state. The surface state is represented by a set of surface state descriptors, which were derived from the atomistic surface state for each independent carbon bombardment event. These descriptors are representative of the composition and degree of amorphization of the outermost angstrom of surface material and were chosen to optimize predictive performance for particle exchange at the interface. The distributions for particle exchange (reflection/sputtering) are demonstrated to vary with each surface state descriptor, motivating the development of surface-state-dependent particle exchange models for plasma simulations. The performance of various ML methods was compared, including polynomial quantile regression, artificial neural networks, k-nearest neighbors, and random forest algorithms, with polynomial regression performing the best for interpolation and extrapolation of learned relationships. In addition to the particle exchange model, a neutral network was developed and used to identify data sufficiency throughout surface descriptor space, which will enable real-time feedback during future data production to ensure data is produced where it is most needed, and we provide commentary on improvements to the data production workflow for future endeavors.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2023

Shifts in bacterial traits under chronic nitrogen deposition align with soil processes in arbuscular, but not ectomycorrhizal-associated trees

Nitrogen (N) deposition increases soil carbon (C) storage by reducing microbial activity. These effects vary in soil beneath trees that associate with arbuscular (AM) and ectomycorrhizal (ECM) fungi. Variation in carbon C and N uptake traits among microbes may explain differences in soil nutrient cycling between mycorrhizal associations in response to high N loads, a mechanism not previously examined due to methodological limitations. Here, we used quantitative Stable Isotope Probing (qSIP) to measure bacterial C and N assimilation rates from an added organic compound, which we conceptualize as functional traits. As such, we applied a trait-based approach to explore whether variation in assimilation rates of bacterial taxa can inform shifts in soil function under chronic N deposition. We show taxon-specific and community-wide declines of bacterial C and N uptake under chronic N deposition in both AM and ECM soils. N deposition-induced reductions in microbial activity were mirrored by declines in soil organic matter mineralization rates in AM but not ECM soils. Our findings suggest C and N uptake traits of bacterial communities can predict C cycling feedbacks to N deposition in AM soils, but additional data, for instance on the traits of fungi, may be needed to connect microbial traits with soil C and N cycling in ECM systems. In conclusion, our study also highlights the potential of employing qSIP in conjunction with trait-based analytical approaches to inform how ecological processes of microbial communities influence soil functioning.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2020

What are the current limits on determination of protonation state using neutron macromolecular crystallography?

The rate of deposition of models determined by neutron diffraction, or a hybrid approach that combines X-ray and neutron diffraction, has increased in recent years. The benefit of neutron diffraction is that hydrogen atom (H) positions are detectable, allowing for the determination of protonation state and water molecule orientation. This study analyses all neutron models deposited in the Protein Data Bank to date, focusing on protonation state and properties of H (or deuterium, D) atoms as well as the details of water molecules. In particular, clashes and hydrogen bonds involving H or D atoms are investigated. As water molecules are typically the least reproducible part of a structural model, their positions in neutron models were compared to those in homologous high-resolution X-ray structures. For models determined by joint refinement against X-ray and neutron data, the water structure comparison was also carried out for models re-refined against the X-ray data alone. The homologues have generally fewer conserved water molecules where X-ray only was used and the positions of equivalent waters vary more than in the case of the hybrid X-ray model. As neutron diffraction data are generally less complete than X-ray data, the influence of neutron data completeness on nuclear density maps was also analyzed. Here, we observe and discuss systematic map quality deterioration as result of data incompleteness.

Hydrogen bond↗
DOE OSTI2024

Benchmarking of Different Inverse Point Kinetics Implementations for an Autocorrected Reactimeter Algorithm

In November 2017, the Transient Reactor Test Facility returned to operation. Since that time, many transient test series have been completed; each has provided valuable data for materials performance, reactor safety that can be applied in future designs. During each experimental series, detector count rates provided important information on the core behavior during transients. However, a limitation of these data is that variations in the neutron distribution during experiments cause errors when attempting to infer reactivity evolution from detector signals. Neutron physics codes can be used to compute the flux shape variations. However, this is a poor solution when the experimental data is used to do verification, validation and uncertainty quantification (VVUQ) on codes. Indeed, if the output of the code is used both as a reference and to correct what the reference is compared to, the circular dependency limits the quality of the VVUQ approach. To overcome this problem, an Autocorrected Reactimeter Algorithm (ACRA) has been developed. This approach infers time-dependent reactivity evolution by testing different spatial corrections and selecting the one that minimizes reactivity variations when the core is in a frozen configuration (i.e. when there is no variation in parameters affecting reactivity). However, the scope of this method was limited to transients where there were negligible thermal feedback. Indeed, the core is never in a frozen configuration when the fuel temperature varies during the whole transient. This is our motivation for the development of an improved version of the ACRA which does not require frozen configurations

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2023

Machine-Learning Assisted Identification of Battery Life Models

Predictive battery life models are commonly utilized to extrapolate degradation trends observed during accelerated aging tests for simulation of degradation in real-world applications. Thus, fitting accelerated aging data as accurately as possible and with low uncertainty is crucial for making believable projections of battery lifetime, but it is challenging to identify algebraic expressions that accurately fit multivariate degradation trends. A review of models published in literature reveal some common expressions for fitting calendar aging data, which is only dependent on temperature and state-of-charge, but no consistency across many models for fitting cycle aging data, indicating the need for a statistically rigorous data driven approach for developing empirical models. This talk will describe a machine-learning assisted method for identification of predictive battery life models utilizing bilevel optimization and symbolic regression. Bilevel optimization with cross-validation is used to statistically determine cell- and stress-dependent model parameters, while symbolic regression identifies both linear and multiplicative candidate expressions to predict stress-dependent degradation rates by selecting low-order subsets of features from a generated feature library. Because model expressions are identified empirically, it is crucial to ensure resulting models behave according to physical expectations, so the stability of models for interpolation or extrapolation is interrogated qualitatively through simulation and quantitatively through cross-validation and uncertainty quantification via bootstrap resampling. This model identification approach substantially improves upon models identified purely using expert judgement in terms of both accuracy and uncertainty. Model simulation and validation is then conducted by deriving a state-equation form of the predictive model, enabling simulation of battery aging under dynamic stresses. This enables validation of the predictive battery model on lab-based tests with varying conditions or on drive-cycle or application-cycle testing protocols. Parameter uncertainty can be carried forward into model simulation, giving lifetime estimates and confidence windows for cell- or system-level lifetime. The financial impact of battery model uncertainty can be estimated by incorporating uncertainty into a technoeconomic model.

battery↗
DOE OSTI2023

Numerical investigations to identify environmental factors for field-scale reactive transport of pathogens at riverbank filtration sites

While induced bank filtration is a proven method for facilitating sustainable drinking water production, it is at risk from surface water contaminations (e.g., pathogens). Induced bank filtration and pathogen transport in groundwater have been studied extensively. However, long-term studies that consider real-world conditions are missing. These conditions include seasonal changes to environmental conditions and waterworks operations. Therefore, to analyze the effect of seasonal changes on the transport of human pathogenic viruses and their indicators in induced bank filtration, concentrations of adenoviruses and pathogen indicators were monitored over 16 months at an active bank filtration plant at the Rhine River, in Düsseldorf (Germany). Based on this data, a 2D groundwater model was created in PFLOTRAN that simulated flow, heat transport, conservative transport of chloride and the resulting electrical conductivity, reactive transport of oxygen and nitrate, and colloid-based transport of coliforms, somatic coliphages, and adenoviruses. The results show that reduced travel time was the key factor determining periods with a low removal of coliforms and somatic coliphages in the aquifer. Travel time was controlled by river level variations during rainy seasons, and the waterworks extraction rates during dry seasons. Further, for adenovirus transport, travel distance in the subsurface appeared to be the key factor, while travel time had no significant impact. Coliform removal increased when the colmation layer permeability decreased, while coliphage and adenovirus removal was unaffected by the colmation layer permeability. Seasonal changes in temperature and oxygen content did not significantly impact the removal of coliphages and adenoviruses in groundwater. Denitrifying conditions correlated with a lowered coliform removal, but the modelling could not establish a connection between denitrifying conditions and coliform removal. Our study showed that removal of pathogens and pathogen indicators at induced bank filtration plants varies greatly in time and space (e.g., for coliforms from 1 to 4 log-levels at 20m travel distance), and that adenovirus transport differs considerably from transport of coliforms and somatic coliphages.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

(Doublon) Benchmarking of Different Inverse Point Kinetics Implementations for an Autocorrected Reactimeter Algorithm

In November 2017, the Transient Reactor Test Facility returned to operation. Since that time, many transient test series have been completed, such as the Transient Heatsink Overpower Response capsule (THOR), the Transient Water Irradiation System for TREAT (TWIST), and Sirius. Each has provided valuable data for materials performance and reactor safety that can be applied in future designs. During each experimental series, detector count rates provided important information on the core behavior during transients. However, a limitation of these data is that variations in the neutron distribution during experiments can cause errors when attempting to infer reactivity evolution from detector signals. Neutron physics codes can be used to compute the flux shape variations. However, this is a poor solution when the experimental data is used for code verification, validation and uncertainty quantification. Indeed, if the output of the code is used both as a reference and to correct what the reference is compared to, the circular dependency limits the quality of the verification, validation and uncertainty quantification approach. To overcome this problem, the autocorrected reactimeter algorithm (ACRA) has been developed. This approach infers a time-dependent reactivity evolution by testing different spatial corrections and selecting the one that minimizes reactivity variations when the core is in a frozen configuration (i.e., when there is no variation in parameters affecting reactivity). However, the scope of this method was limited to transients where there were negligible thermal feedback. Indeed, the core is never in a frozen configuration when the fuel temperature varies during the whole transient. This is our motivation for developing an improved version of the ACRA that does not require frozen configurations. To develop this new algorithm, we need a precise and unbiased implementation of the inverse point kinetic equations (IPKEs) as any error in the reactivity evaluation will be propagated into the choice of the optimal spatial correction. Indeed, the previous reactimeter algorithm would use approximations, such as a negligible flux amplitude derivative, to focus on rapidity. For the numerical validation of ACRA, we aim at absolute error under for reactivity derived from signals similar to the one of this study. In this summary, we test eight different IPKE implementations. Each will process a mockup signal built for this study, similar to those that the future ACRA will process. Each reactivity output will be compared to the reference reactivity that has been used to generate the mockup signal. The implementation minimizing the difference with the reference reactivity will be used in the development of a new ACRA formulation.

73 - NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2023

Insights into co-pyrolysis of polyethylene terephthalate and polyamide 6 mixture through experiments, kinetic modeling and machine learning

The non-isothermal pyrolysis of polyethylene terephthalate (PET), polyamide 6 (PA6), and their mixtures was studied in a thermogravimetric analyzer at different heating rates. Temperature of maximum decomposition (T max ) decreased by 25–45 °C and 35–55 °C for the PET:PA6 mixtures (3:1, 1:1, 1:3) compared to PET and PA6, respectively. The kinetic analysis was initially carried out using isoconversional method. However, the dependency of activation energy on conversion was observed for the co-pyrolysis of PET and PA6 that suggested the occurrence of multi-step reactions in the mixtures. Distributed activation energy model (DAEM) was used in this study to describe the multistep reactions occurring during pyrolysis of PET:PA6 mixtures. Here, in this work, a four-parallel reaction DAEM was developed to describe the pyrolysis kinetics of PET:PA6 mixtures. The apparent mean activation energies (E o ) for PET, PA6, and mixtures varied in the range of 244–255, 140–215, and 138–255 kJ mol –1 , respectively. The mass loss profiles of PET and PA6 mixtures were also modeled using artificial neural network (ANN). Out of 155 ANN models, the best prediction was made by ANN511 with R 2 greater than 0.997 for both test and unseen data. The interaction effects observed through TGA experiments and subsequent kinetic analysis were further assessed in terms of product composition using analytical pyrolysis coupled with gas chromatograph/mass spectrometer (Py-GC/MS). Co-pyrolysis of PET and PA6 resulted in the formation of new aromatic compounds with nitrogen-containing functional groups, which were not detected when PET or PA6 were pyrolyzed individually.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2025

A Software/Hardware Framework for Efficient and Safe Emergency Response in Post-Crash Scenarios of Battery Electric Vehicles

The adoption rate of battery electric vehicles (EVs) is rapidly increasing. Electric vehicles differ significantly from conventional internal combustion engine vehicles and vary widely across different manufacturers. Emergency responders (ERs) and recovery personnel may have less experience with EVs and lack timely access to critical information such as the extent of the stranded energy present, high-voltage safety hazards, and post-crash handling procedures in a user-friendly manner. This paper presents a software/hardware interactive tool named Electric Vehicle Information for Incident Response Solutions (EVIRS) to aid in the quick access to emergency response and recovery information. The current prototype of EVIRS identifies EVs using the VIN or Make, Model, and Year, and offers several useful features for ERs and recovery personnel. These features include integration and easy access to emergency response procedures tailored to an identified EV, vehicle structural schematics, the quick identification of battery pack specifications, and more. For EVs that are not severely damaged, EVIRS can perform calculations to estimate stranded energy in the EV’s battery and discharge time for various power loads using either EV dashboard information or operational data accessed through the CAN interface. Knowledge of this information may be helpful in the post-crash handling, management, and storage of an EV. The functionality and accuracy of EVIRS were demonstrated through laboratory tests using a 2021 Ford Mach-E and associated data acquisition system. The results indicated that when the remaining driving range was used as an input, EVIRS was able to estimate the pack voltage with an error of less than 3 V. Conversely, when pack voltage was used as an input, the estimated state of charge (SOC) error was less than 5% within the range of 30–90% SOC. Additionally, other features, such as retrieving emergency response guides for identified EVs and accessing lessons learned from archived incidents, have been successfully demonstrated through EVIRS for quick access. EVIRS can be a valuable tool for emergency responders and recovery personnel, both in action and during offline training, by providing crucial information related to assessing EV/battery safety risks, appropriate handling, de-energizing, transport, and storage in an integrated and user-friendly manner.

25 ENERGY STORAGE↗
DOE OSTI2023

ETF Grout Expansion: Evaluation of MgO Additions and Struvite Precipitation Protocol for Potential Expansion

The ETF ammonia tolerant grout waste form was developed by the Vitreous State Laboratory (VSL) to stabilize ammonium and thereby prevent emission of ammonia vapor during solidification of an ammonium-rich, concentrated sulfate aqueous waste stream generated at the Hanford Effluent Treatment Facility (ETF). VSL did not observe expansion in samples prepared during the waste form development work. However, Savannah River National Laboratory (SRNL) personnel did encounter expansion, in some cases up to 20+ volume percent expansion in ETF grout samples while preforming work scope to evaluate compositional ranges for ETF brine and of the precipitation and solidification reagent. The work requests can be found in Washington River Protection Services (WRPS) Statement of Work (SOW), Requisitions #: 339922 Revisions 0 and 1, March 11, 2021, and November 2021. Additional evaluation was requested by WRPS SOW Requisitions #: 356730, Revision 1, February 1, 2022, to determine whether differences in the slags or differences in sample preparation protocols used by VSL and SRNL were the cause of the expansion observed in the SRNL samples. G. Chen, WRPS, arranged a materials exchange between VSL and SRNL and coordinated the VSL protocol for grouting and curing 1L of Base Case ETF simulant. Materials were exchanged between VSL and SRNL and both laboratories performed the VSL struvite precipitation-solidification protocol and cured samples for at least 28 days. VSL and SRNL encountered expansion, at varying degrees, in all samples prepared under the material exchange and grouting protocol. Additional material exchange testing between VSL and SRNL and alternative materials evaluation to further investigate the causes of grout expansion and differentiate the effects of various dry materials, grouting procedures, and curing containers was requested by WRPS SOW Requisitions #: 356730, Revision 2, January 10, 2023. Pacific Northwest National Laboratory (PNNL) assembled and distributed materials to both laboratories for testing. Observations from this study indicated variations in MgO additions, water-to-dry mix (dm) ratios and curing containers did not correlate to ETF ammonia grout expansion. In comparison to previous work, SRNL observed the addition rate of NaOH to be a potential factor for the final pH in the struvite precipitation step. In the current testing NaOH was added in bulk (5-10 mL at one time) during the precipitation step. The resulting pH was 7.57-9.15 which is slightly higher than pH observed at a slower addition rate. Additional testing is needed to identify the presence and/or struvite stability in high-pH struvite precipitant ETF ammonia grout samples. Therefore, based on current data, it is unknown if NaOH addition rate is the fundamental cause of grout expansion.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2023

County-level assessment of behind-the-meter solar and storage to mitigate long duration power interruptions for residential customers

Customer concerns over electric system resilience could drive early adoption of behind-the-meter solar-plus-storage (BTM PVESS), especially as wildfire, hurricane, and other climate-driven risks to electric grids become more pronounced. However, the resilience benefits of BTM PVESS are poorly understood, especially for residential customers, owing to lack of data and methodological challenges, making it difficult to forecast adoption trends. In this paper, we develop a methodology to model the performance of BTM PVESS in providing backup power across a wide range of customer types, geography / climate conditions, and long duration power interruption scenarios, considering both whole-building backup and backup of specific critical loads. We combine novel, disaggregated end-use load profiles across the continental United States with temporally and geospatially aligned solar generation estimates. We then implement a PVESS dispatch algorithm to calculate the amount of load served during interruptions. We find that PVESS with 10 kWh of storage can meet a limited set of critical loads in most United States counties during any month of the year, though this capability drops to meeting only 86% of critical load, averaged across all counties and months, when heating and cooling are considered critical. Backup performance is lowest in winter months where electric heat is common (southeast and northwest U.S.) and in summer months in places with large cooling loads (southwest and southeast U.S.). Winter backup performance varies by roughly 20% depending on infiltration rates, while summer performance varies by close to 15% depending on the efficiency of the central air-conditioning system. Differences in temperature set-points in Harris County correspond to a 40% range in winter backup performance and a 20% range in summer performance. Economic calculations show that a customer’s resilience value of PVESS must be high to motivate adoption of these systems.

14 SOLAR ENERGY↗
DOE OSTI2024

FY24 Report on Water NSTF Testing: Lower Tank Inlet Piping Configuration

The following report serves as a summary of the accomplishments and testing results by the Natural convection Shutdown heat removal Test Facility (NSTF) experimental program over the past 12-month period. A major activity included reconfiguration of the chimney piping geometry which altered the discharge position into the tank from the original 50% tank height to a new lower position at a 10.9% tank height. This modification increases the volume of available coolant thus extending long-term operating capacity, however also results in a decreased liquid driving head which has the potential to reduce the natural circulation efficiency and decrease overall performance. Examination of the tradeoffs for these two configurations is an area of interest for RCCS designers and drove planned test activities this year. Ten matrix tests were performed in FY24, totaling 195 hours of heated operations and 12.9 MWh of electrical heating throughout the year, with eight classified as Accepted per NQA-1 and two classified as Trending. Testing prior to the facility reconfiguration examined the facility response to throttling at the tank inlet, demonstrating increased sensitivity of flow instabilities to throttling within the two-phase region when compared to throttling at the lower sensitivity single-phase inlet region. Testing after the chimney reconfiguration began with a baseline test at conditions of 80% inventory fill and prototypic thermal power input of 2.1 MW t . In addition to establishing a reference for nominal system behavior and performance, repeat testing with multiple subsequent runs demonstrated strong repeatability between tests performed at the same conditions in this new configuration. These tests also examined if influences would occur to system behavior with installation of new higher-resolution instrumentation within the upper chimney. In this critical region where boiling and flashing phenomena dominate, the test results provided confidence that the new instrumentation does not uncharacteristically influence the observed behaviors. An inventory parametric series was then initiated to examine system behavior with the new chimney configuration at six varying initial inventory levels, ranging from high 80% to low 20% fill. Two-phase oscillations with similar peak and mean flow rates were observed when comparing to the previous mid tank configuration. Generally, similar system response trends with inventory were also observed, such as two phase oscillations suppressing as inventories were lowered. However, in one absolute inventory comparison at the highest fill of 80%, flow oscillations saw a gradual growth over the 4 hours of two-phase operation in the new lower tank inlet configuration, opposite of the gradual dampening observed in the mid tank inlet configuration. This can be attributed to, in part, a greater hydrostatic head pressure above the two-phase discharge region where the boiling front is developed. Furthermore, the change resulted in greater sensitivity to liquid degassing phenomena during single-phase heating, causing loop instabilities to form which trigged moderate flow degradation during the period approaching saturation and boiling conditions. This behavior had been observed previously under some conditions but was a common occurrence in recent testing with the newer lower tank inlet configuration. Initial observations from these first data sets suggest an overall larger window of stability for the mid tank inlet configuration compared to the lower tank inlet. Lastly, the lowest inventory fill test was repeated over an extended testing window to examine depletion behavior. Natural circulation flow and effective heat removal performance were observed during most of the testing period; only after the tank became fully drained (0% fill) did flow stagnate and violent geysering events occur. This early observation confirms one relative advantage over the mid tank inlet configuration, which stagnated under comparable conditions at ~20% inventory remaining in the tank.

42 ENGINEERING↗
DOE OSTI2025

Process intensification approach to enhancing heat and mass transfer: Radio frequency (RF) assisted drying of paper and board

Conventional multi-cylinder drying of paper and board typically relies on both conductive drying from steam-heated cylinders and convective drying, where heated air flows over the paper web surface. Conduction primarily contributes to heat transfer, while convection is the main driver of mass transfer. However, conventional drying systems are heavily dependent on steam, usually powered by fossil fuels, and are often energy-inefficient with high levels of waste. Additionally, these surface-driven processes result in a lower percentage of energy absorption compared to the energy supplied, leading to significant energy losses. To improve this long-standing process, an experimental system was developed to investigate a process intensification approach involving the integration of Radio Frequency (RF) heating, a volumetric electromagnetic technology, alongside traditional conduction and convection drying methods. This study also emphasizes the use of sensors to continuously monitor key parameters such as moisture content, supply system temperatures, sample temperatures, air flows, and drying rates in real-time. The effect of RF as an auxiliary energy source in localized environments at varying moisture levels was explored to optimize industrial drying systems, quantify potential improvements, and provide insights for future studies. Experimental results from trials combining RF with convection and with the base case alternating conduction-convection drying processes are presented. It is shown that RF is a viable process intensification approach for paper drying improving the drying rate and energy intensity at higher moisture contents. These findings offer valuable insights for process intensification and contribute to the process development, modeling, and simulation of advanced paper drying techniques.

42 ENGINEERING↗
DOE OSTI2021

Influence of Agricultural Managed Aquifer Recharge (AgMAR) and Stratigraphic Heterogeneities on Nitrate Reduction in the Deep Subsurface

This data package contains input files for TOUGHREACT for a modeling study examining the effects of managed aquifer recharge on agricultural lands on nitrate cycling and transport in the Central Valley of CA near Modesto. The files contain all the geochemical species, reactions, and hydrological parameters for the model. The files are text files used for the TOUGH family of code created by LBNL. To use the files a license is required. https://tough.lbl.gov/licensing-download/toughreact-licensing-download/Accompanying Paper Abstract: Agricultural managed aquifer recharge (AgMAR) is a proposed management strategy whereby surface water flows are used to intentionally flood croplands with the purpose of recharging underlying aquifers. However, legacy nitrate (NO3-) contamination in agriculturally-intensive regions poses a threat to groundwater resources under AgMAR. To address these concerns, we use a reactive transport modeling framework to better understand the effects of AgMAR management strategies (i.e., by varying the frequency, duration between flooding events, and amount of water) on N leaching to groundwater under different stratigraphic configurations and antecedent moisture conditions. In particular, we examine the potential of denitrification and nitrogen retention in deep vadose zone sediments (~15 m) using variable AgMAR application rates on two-dimensional representations of differently textured soils, soils with discontinuous bands/channels, and soils with preferential flow paths characteristic of typical agricultural field sites. Our results indicate that finer textured sediments, such as silt loams, alone or embedded within high flow regions, are important reducing zones providing conditions needed for denitrification. Simulation results further suggest that applying water all-at-once rather than in increments for a fixed volume of recharge transports higher concentrations of NO3- deeper into the profile, which has the potential to exacerbate groundwater quality. This transport into deeper depths can be aggravated by wetter antecedent soil moisture conditions. However, applying water all-at-once also increases denitrification within the vadose zone by promoting anoxic conditions. We conclude that AgMAR management strategies can be designed to enhance denitrification in the subsurface and reduce N leaching to groundwater, while specifically accounting for lithologic heterogeneity, antecedent soil moisture conditions, and depth to the water table. Our findings are potentially relevant to other systems that experience flooding inundation such as riparian corridors, floodplains, wetlands, and other managed landscapes like dedicated recharge basins.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Microbial Models for Simulating Soil Carbon Dynamics: A Review

Abstract Soils store the largest amount of carbon (C) in the biosphere, and the C pool in soil is critical to the global C balance. Numerous microbial models have been developed over the last few decades to represent microbial processes that regulate the responses of soil organic carbon (SOC) to climate change. However, the representation of microbial processes varies, and how microbial processes are incorporated into SOC models has not been well explored. Here, we reviewed 71 microbial models to characterize the microbial processes incorporated into SOC models and analyzed variations in mechanistic complexity. We revealed that (a) four processes (microbial‐mediated decomposition, mineral interaction, microbial necromass recycling, and active and dormant microbial dynamics) are commonly incorporated in microbial models, (b) ∼48% of models simulate only one microbial process (i.e., microbial‐mediated decomposition) and 35% of models simulate two microbial processes: for example, microbial‐mediated decomposition and mineral interaction, (c) more than 80% microbial models use nonlinear equations, such as forward Michaelis‐Menten kinetics, to represent SOC decomposition, (d) the concept of persistence of SOC due to its intrinsic properties has been replaced by organo‐mineral interaction (∼39% of microbial models) that protects SOC from decomposition, and (e) various temperature and moisture modifiers and pH effects have been used to explain the environmental effect on microbial processes. In the future, to realistically incorporate microbial processes into Earth System Models, it is imperative to identify experimental evidence on rate limitation processes and firmly ground model structure on the field and laboratory data.

Environmental Sciences & Ecology↗
DOE OSTI2024

ELM-Wet: Inclusion of a wetland landunit with sub-grid representation of eco-hydrological patches and hydrological forcing in E3SM Land Model (ELM)

Wetlands emit the most biogenic methane (CH4) and present the greatest uncertainty in the global CH4 budget. Modeling these emissions is challenging due to the temporal and spatial variability in wetland structure and CH4 flux rates, along with complex interactions among hydrological, ecological, meteorological, and microbial processes that govern CH4 dynamics. To address these issues, we aim to enhance the accuracy of wetland representation in the U.S. Department of Energy’s Exascale Earth System Model (E3SM) Land Model, ELM. This effort led to the development of ELM-Wet, which incorporates a dedicated wetland landunit with subgrid representation of eco-hydrological patch types. We implement wetland-specific hydrology by imposing site-specific constraints on surface water levels, thereby allowing different patches to sustain varying depths of inundation. Additionally, we refined the calculation of aerenchyma transport diffusivity based on observed conductance across different vegetation types. We validated these enhancements through site-specific simulations of a coastal freshwater wetland, the Salvador WMA Freshwater Marsh (Ameriflux, site ID US-LA2), located in the coast of Louisiana (29.85N,90.29W) at an elevation of 0 m. The site was simulated with ELM-Wet and the default version ELMv1. We use Bayesian Optimization to parameterize CO2 and CH4 fluxes. Eddy covariance observations of CO2 and CH4 fluxes from 2012-2013 were used to train the model and data from 2021 were used for validation. In this repository, we include all the output of all the simulations performed using different versions of ELMv1 and ELM-Wet, all input required to the model, and the Matlab scripts we used to processed the output data. ELM-Wet_flmd.csv includes a detailed description of the datasets files.

54 ENVIRONMENTAL SCIENCES↗