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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 OSTI2026

Phosphorus sorption and its environmental predictors across pantropical forest soils sampled over the past decade

Tropical forest productivity is frequently constrained by soil phosphorus (P) availability, yet global Land Surface Model (LSM), which are used to simulate ecosystem processes, still represent P cycling in tropical regions only in a limited way, largely because of scarce observational data. Phosphorus adsorption and desorption of dissolved inorganic P to and from soil minerals (hereafter termed sorption), is an important process for predicting how much P is available to plants. This dataset was created to improve predictions of soil P sorption in tropical soils by identifying the isotherm equation that best describes pantropical soils. It includes raw measurements of environmental variables, such as soil properties and climate, together with P sorption data collected from 40 forest soil pits from 9 Forest Global Earth Observatory (ForestGEO) sites across 7 tropical countries during 2018-2022. The data are organized by site, country, and continent. Each site may include several soil pits. For each pit, P sorption was measured across a range of soil P concentrations to build sorption isotherm curves, typically with about 6 to 8 measurements per curve. While sorbed P varies across these concentration levels, the other environmental variables remain constant at the plot level.

Aluminum oxide↗
DOE OSTI2020

Opportunities for Using the Industrial Assessment Center Database for Industrial Water Use Analysis

The manufacturing sector accounted for approximately 5–6% of total U.S. water use in 2015. Of that amount, 75–80% is self supplied withdrawal from surface-water and groundwater sources and the remainder is from public water supplies. Although manufacturing facilities commonly locate in water-scarce areas, water scarcity still poses a great risk to the manufacturing sector. Reliable water is necessary for any facility that relies on it for process and comfort cooling, cleaning, employee use, and steam generation. One of these barriers to water efficiency is the lack of reliable data on overall U.S. industrial water use—how it is used and the quantities required for each sector. If a facility cannot be easily compared with a facility of similar size and sector, knowing if it is effectively using water conservation best practices is difficult. One potential source of industrial water use data is the U.S. Department of Energy (DOE)–sponsored Industrial Assessment Centers (IACs). IACs are university-based organizations that provide free audits to small- and medium-sized manufacturing facilities to identify productivity improvement and waste and energy reduction opportunities. The IACs also maintain a database of all the audits conducted, which currently holds more than 19,267 assessments and 145,000 recommendations (as of July 24, 2020). This database also contains energy utility (electricity, natural gas, and other fuels) and water utility data, making it a potential data source for industrial water use. This report attempts to create regression models to predict a small- or medium-sized industrial facility’s annual water use or cost based on its industrial subsector and several possible relevant variables. Using data collected by IAC assessments, models for several industrial subsectors were generated via stepwise regression techniques to determine which variables (annual sales, number of employees, facility/plant area, annual production hours, and a water stress metric) are relevant.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2023

n-Propylbenzene Droplet Combustion: Experiments and Numerical Modeling

n-Propylbenzene (PB, C9H12, 432K boiling point) is important as a constituent in surrogates for some transportation fuels but little is understood about its droplet burning characteristics. We present an experimental and numerical study of a fuel droplet burning under conditions where the sole means of gas transport is fuel evaporation at the droplet surface thereby creating a one-dimensional gas transport that is amenable to detailed numerical modeling. The one-dimensional gas transport that results is amenable to detailed numerical modeling. Initial droplet diameters (Do) ranged from 0.3 mm to about 5 mm to better understand the mechanisms that control burning over such a wide range of Do. For Do < 1 mm droplets were burned while anchored to 14 m SiC fibers within a sealed container in free-fall during which time the droplets were ignited by spark discharge and the burning process recorded by video cameras. Larger and free-floating droplets were studied in the spaced-based platform of the orbiting International Space Station for Do > 1.0 mm which provided unlimited times for experimental observation. Measurements of droplet, flame and soot shell diameters were made and are discussed in the presentation. The experimental results showed formation of optically thick soot clouds developing during burning that required a robust image analysis algorithm to enable measurement of droplet diameter. The measured droplet diameters were well predicted by a numerical model that included soot formation, a PB kinetic mechanism comprising 576 species and 27369 reactions, unsteady gas and liquid transport, variable properties and radiative transport. Flame diameters showed more variability compared to the simulations, depending on how the flame was defined (peak gas temperature and OH concentration). Droplets burned slower as Do was increased and the PB sooting propensity increased with Do. Flame extinction was observed for Do > 3.3 mm indicating a strong effect of radiation as Do was increased compared to the smaller droplet diameters examined where extinction was not experimentally observed. For the largest droplets examined the possibility that the observed extinction is indicative of a transition to cool flame burning is still under investigation.

Guo, Songtao↗
DOE OSTI2022

Continental United States may lose 1.8 petagrams of soil organic carbon under climate change by 2100

Abstract Aims High‐resolution information on soils’ vulnerability to climate‐induced soil organic carbon (SOC) loss can enable environmental scientists, land managers, and policy makers to develop targeted mitigation strategies. This study aims to estimate baseline and decadal changes in continental US surface SOC stocks under future emission scenarios. Location Continental United States. Time period 2014–2100. Methods We used recent SOC field observations ( n = 6,213 sites), environmental factors ( n = 32), and an ensemble machine learning (ML) approach to estimate baseline SOC stocks in surface soils across the continental United States at 100‐m spatial resolution, and decadal changes under the projected climate scenarios of Coupled Model Intercomparison Project Phase Six (CMIP6) earth system models (ESMs). Results Baseline SOC projections from ML approaches captured more than 50% of variability in SOC observations, whereas ESMs represented only 6–16% of observed SOC variability. ML estimates showed a mean total loss of 1.8 Pg C from US surface soils under the high‐emission scenario by 2100, whereas ESMs showed no significant change in SOC stocks with wide variation among ESMs. Both ML and ESM predictions agree on the direction of SOC change (net emissions or sequestration) across 46–51% of continental US land area. These differences are attributable to the high‐resolution site‐specific data used in the ML models compared to the relatively coarse grid represented in CMIP6 ESMs. Main conclusions Our high‐resolution estimates of baseline SOC stocks, identification of key environmental controllers, and projection of SOC changes from US land cover types under future climate scenarios suggest the need for high‐resolution simulations of SOC in ESMs to represent the heterogeneity of SOC. We found that the SOC change is sensitive to key soil related factors (e.g. soil drainage and soil order) that have not been historically considered as input parameters in ESMs, because currently more than 95% variability in the SOC of CMIP6 ESMs is controlled by net primary productivity, temperature, and precipitation. Using additional environmental factors to estimate the baseline SOC stocks and predict the future trajectory of SOC change can provide more accurate results.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2026

Predicting initial trans-membrane pressure across cycles in the ultrafiltration process using random forest

With growing freshwater scarcity, direct potable reuse (DPR) systems that reclaim wastewater for drinking are becoming increasingly important for sustainable water supply. Reliable operation requires minimizing downtime in ultrafiltration (UF) units, where membrane fouling leads to elevated trans-membrane pressure (TMP). This study develops data-driven regression models based on random forest (RF) and autoregressive (AR) approaches to forecast the initial TMP at the start of each UF filtration cycle in a pilot-scale DPR system. The RF model consistently outperforms baseline methods, including historical mean, last observation carried forward, and AR models, across multiple forecast horizons, achieving the lowest root mean square error. To evaluate how different classes of process variables contribute to TMP dynamics over time, we examine the feature importance of independent input variables across multiple forecast horizons. This analysis provides insight into the temporal relevance of operational and sensor-derived features, guiding control and monitoring strategies. Additionally, the impact of hyperparameter tuning on TMP prediction performance is assessed for both direct and recursive RF modelling approaches. The proposed RF framework establishes a robust foundation for predictive monitoring and real-time optimization of UF operations, supporting sustainable and reliable water reuse.

direct potable reuse↗
DOE OSTI2025

Water Stable Isotopes in Precipitation, Rivers, and Groundwater Across an Elevation Gradient in the Sierra Nevada Mountains (USA) Reflect Source Elevation

Understanding watershed processes is critical to predict the impacts of climate change and forest management on water resources. However, collecting hydrological data in mountainous terrain is challenging. Precipitation, river water, and groundwater H and O stable isotope data can provide insights into processes occurring at the mountain range scale. Water δ 2 H and δ 18 O values in precipitation vary with terrain elevation; thus, the resulting isotopic lapse rates of precipitation, groundwater, and river water have the potential to elucidate watershed processes and source elevations of major rivers. We analysed H and O stable isotope data of precipitation, groundwater, and river water over the course of one Water Year (Oct 2016—Oct 2017) in the Sierra Nevada mountains of California, USA. We calculated elevation-dependent isotopic lapse rates of these waters to estimate the source elevation of major rivers draining the west flank of the Sierra Nevada mountains. We also investigated the Cosumnes River's watershed in more detail to determine how river flow may be more fully partitioned. Here, we found that H and O stable isotopes in precipitation are temporally variable, but isotopic lapse rates are generally consistent with prior studies. However, groundwater samples across an elevation gradient provide a more consistent and accessible isotopic lapse rate to predict river water source elevations.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2019

Large carbon cycle sensitivities to climate across a permafrost thaw gradient in subarctic Sweden

Permafrost peatlands store large amounts of carbon potentially vulnerable to decomposition. However, the fate of that carbon in a changing climate remains uncertain in models due to complex interactions among hydrological, biogeochemical, microbial, and plant processes. In this study, we estimated effects of climate forcing biases present in global climate reanalysis products on carbon cycle predictions at a thawing permafrost peatland in subarctic Sweden. The analysis was conducted with a comprehensive biogeochemical model ( ecosys ) across a permafrost thaw gradient encompassing intact permafrost palsa with an ice core and a shallow active layer, partly thawed bog with a deeper active layer and a variable water table, and fen with a water table close to the surface, each with distinct vegetation and microbiota. Using in situ observations to correct local cold and wet biases found in the Global Soil Wetness Project Phase 3 (GSWP3) climate reanalysis forcing, we demonstrate good model performance by comparing predicted and observed carbon dioxide (CO 2 ) and methane (CH 4 ) exchanges, thaw depth, and water table depth. The simulations driven by the bias-corrected climate suggest that the three peatland types currently accumulate carbon from the atmosphere, although the bog and fen sites can have annual positive radiative forcing impacts due to their higher CH 4 emissions. Our simulations indicate that projected precipitation increases could accelerate CH 4 emissions from the palsa area, even without further degradation of palsa permafrost. The GSWP3 cold and wet biases for this site significantly alter simulation results and lead to erroneous active layer depth (ALD) and carbon budget estimates. Biases in simulated CO 2 and CH 4 exchanges from biased climate forcing are as large as those among the thaw stages themselves at a landscape scale across the examined permafrost thaw gradient. Future studies should thus not only focus on changes in carbon budget associated with morphological changes in thawing permafrost, but also recognize the effects of climate forcing uncertainty on carbon cycling.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

Collaborative Research: Advancing Arctic Climate Projection Capability at Seasonal to Decadal Scales (Final Technical Report)

The Regional Arctic System Model (RASM) at process resolving configurations has been used to (i) advance understanding of physical processes and feedbacks involved in Arctic amplification and (ii) understand and potentially reduce uncertainty in prediction of arctic climate change at seasonal to decadal scales. RASM consists the atmosphere (Weather and Research Forecasting model, WRF), ocean (Parallel Ocean Program, POP), sea ice (CICE), land hydrology (Variable Infiltration Capacity model, VIC), river routing scheme (RVIC), marine biogeochemistry components and the coupling framework (CPL7). Its domain is pan-Arctic, with the atmosphere and land components configured on a 50-km or 25-km grid and four configurations of the ocean and sea ice components: 1/12°(~9.3km) or 1/48°(~2.4km) and 45 or 60 vertical layers. These RASM configurations have been motivated by the emerging exascale capability for high performance computing to improve model fidelity. The dynamical downscaling of reanalysis allows comparison of RASM results with observations in place and time to: (i) advance system level understanding of physical processes and coupling involved in an event, (ii) optimize model parameter space, (iii) diagnose and reduce model biases and (iv) produce realistic and consistent across all the components initial conditions for predictions and predictability studies, which are all unique capabilities not available in global Earth System Models (ESMs). An evaluation of RASM 1.0 (Cassano et al. 2017) revealed that it had a cold bias over the oceans and a warm bias over land areas due largely to cloud and radiation biases in the model, with too little cloud cover simulated over land and too much cloud cover simulated over sub-polar oceans. This study has motivated an upgrade to WRF version 3.7.1 in RASM and allowed for the inclusion of the radiative impact of convective clouds. A variety of atmospheric physics parameterizations were evaluated against observations (e.g. data from the Arctic Clouds in Summer Experiment (ACSE); Sedlar et al. 2020) to identify an optimal suite of WRF physics options in RASM. The RASM with the optimized WRF physics were used to study the impact of strong mesoscale winds over the ocean around the southern tip of Greenland (DuVivier and Cassano 2016) and their impact on oceanic convection (DuVivier et al. 2017a). Data from the PolarWinds field campaign were used to evaluate WRF boundary layer physics and resolution impacts on the simulation of a Greenland barrier wind event (DuVivier et al. 2017b). The RVIC streamflow routing model has been implemented in RASM to realistically represent high-resolution streamflow processes (Hamman et al. 2017) and to couple the land buoyancy fluxes to the ocean. The RASM-RVIC high-resolution data set of all coastal freshwater fluxes for the Arctic drainage basin and surrounding areas for 1979-2014 was published as a separate product (https://doi.org/10.5281/zenodo.293037). The fidelity of atmospheric momentum transfer to and the response of polar marine Ekman layer in RASM and Community Earth System Model (CESM) was investigated by Roberts et al. (2015). The increased frequency of oceanic flux exchange in CESM, following the RASM guidance, caused a considerable increase in the median inertial ice speed across the Southern Ocean and parts of the Arctic. A comprehensive evaluation of the RASM1.0 atmosphere-ocean-sea ice-land interface was completed by Brunke et al. (2018). RASM was also demonstrated for its capability to simulate extreme events in agreement with observations in space and time (Lee et al. submitted). In particular, the development of three open water events, known as polynyas, have been simulated north of Greenland in February of 2011, 2017 and 2018, in agreement with satellite observations for the past four decades. The optimized RASM sea ice results have been favorably evaluated against satellite observations and a subset of eleven CMIP6 models (Watts et al. submitted). In a complementary project, Jin et al. (2018) have shown that RASM with higher-resolution and new sea-ice processes contributed to lower model errors in sea-ice conditions, concentrations of nutrients and ice algae, in comparison to results from the coarse-resolution (1°) CESM. In two other complementary studies, RASM results were used (i) to explain areas of concentrated use by bowhead whales, the seasonal progression in the use, and the physical environment within those areas (Citta et al. 2015) and (ii) for a synthesis of fall bowhead whales distribution and migration in the Bering-Chukchi-Beaufort (BCB) Sea to investigate whale movements and feeding to the local ocean hydrography and currents (Citta et al. 2018). However, the multi-decadal output from the CESM Large Ensemble yielded unrealistic forcing. Instead, the shorter NCEP CFSv2 9-month forecasts were successfully tested and afforded an increased ensemble size (~30) to demonstrate gains of dynamical downscaling at sub-seasonal to intra-annual time scales. The improved model physics and coupling among RASM model components have yielded more realistic representation of the sea ice cover and consistent across all model components initial conditions. Consequently, RASM demonstrates significant gains compared to simulation of sea ice in the NCEP reanalysis. In addition, RASM 6-month ensemble forecasts yield very realistic sea ice distribution, which demonstrates both significant gains of dynamical downscaling and the continued impact of the initial conditions on forecasts out to 6 months (https://nps.edu/web/rasm/predictions). A follow up study using RASM for dynamical downscaling of the more realistic CESM initialized Decadal Prediction Large Ensemble output is currently ongoing as part of the DOE RGMA HiLAT-RASM project.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

“Gray Zone” Simulations Using a Three-Dimensional Planetary Boundary Layer Parameterization in the Weather Research and Forecasting Model

Abstract Generating accurate weather forecasts of planetary boundary layer (PBL) properties is challenging in many geographical regions, oftentimes due to complex topography or horizontal variability in, for example, land characteristics. While recent advances in high-performance computing platforms have led to an increase in the spatial resolution of numerical weather prediction (NWP) models, the horizontal gridcell spacing (Δ x ) of many regional-scale NWP models currently fall within or are beginning to approach the gray zone (i.e., Δ x ≈ 100–1000 m). At these gridcell spacings, three-dimensional (3D) effects are important, as the most energetic turbulent eddies are neither fully parameterized (as in traditional mesoscale simulations) nor fully resolved [as in traditional large-eddy simulations (LES)]. In light of this modeling challenge, we have implemented a 3D PBL parameterization for high-resolution mesoscale simulations using the Weather Research and Forecasting Model. The PBL scheme, which is based on the algebraic model developed by Mellor and Yamada, accounts for the 3D effects of turbulence by calculating explicitly the momentum, heat, and moisture flux divergences in addition to the turbulent kinetic energy. In this study, we present results from idealized simulations in the gray zone that illustrate the benefit of using a fully consistent turbulence closure framework under convective conditions. While the 3D PBL scheme reproduces the evolution of convective features more appropriately than the traditional 1D PBL scheme, we highlight the need to improve the turbulent length scale formulation. Significance Statement The spatial resolution of weather models continues to increase at a rapid rate in accordance with the enhancement of computing power. As a result, smaller-scale atmospheric features become more explicitly resolved. However, most numerical models still ignore the impact of horizontal weather variations on boundary layer flows, which becomes more important at these smaller spatial scales. To address this issue, we have implemented a new modeling approach, using fundamental principles, which accounts for horizontal variability. Our results show that including three-dimensional effects of turbulence is necessary to achieve realistic boundary layer characteristics. This novel technique may be useful for many applications including complex terrain flows, pollutant dispersion, and surface–atmosphere interaction studies.

Juliano, Timothy W.↗
DOE OSTI2025

Hydrologic Model Data for the East Fork Poplar Creek Watershed Simulated with the Advanced Terrestrial Simulator (ATS): Streamflow and Network Expansion–Contraction Dynamics

This dataset supports hydrologic modeling and stream network expansion–contraction analysis for the East Fork Poplar Creek (EFPC) Watershed in Tennessee. It includes a Jupyter notebook for model setup, model configuration files, simulation outputs, and derived products used to evaluate model performance and investigate stream dynamics under varying hydrologic conditions. The dataset was generated using the Watershed Workflow Python package and the Advanced Terrestrial Simulator (ATS), enabling integrated surface–subsurface hydrologic simulations using a stream-aligned mesh. Outputs include high-resolution time series of streamflow, active network length, water table depth, and related hydrologic variables. Also included are spatially explicit stream persistency indices and classifications of reaches as perennial or non-perennial. These data facilitate reproducibility and support further research on stream intermittency and variability in network extent.The model data archive is organized in following directories:1) model_setup_inputsContains the Watershed Workflow Jupyter notebooks (accessed through any open source code editor), selected input datasets, and resulting ATS input files, including XML files (access through any open source code editor), computational mesh (.exo files can be viewed using Paraview), and meteorological forcing files (.h5 files can be accessed through h5py python package and HDFView open source software). 2) model_outputsIncludes ATS simulation outputs relevant to this study. Time series of spatially integrated or averaged variables (e.g., streamflow, water table depth) are provided as CSV files. Select spatial fields (e.g., ponded depth and water table depth) are saved as pickled Python objects to reduce file size, and can be accessed through pickle package in Python. Key geometry objects from Watershed Workflow—such as the surface mesh and river tree—are also included to support analysis of streamflow persistency and expansion–contraction dynamics. These files can also be accessed through Watershed Workflow Python package.3) model_evaluationProvides observed streamflow time series and field survey-based flow regime classifications used to evaluate model performance. Jupyter notebooks for processing ATS outputs and comparing model predictions with observations to build confidence in the model prior to scientific analysis are also included.4) Q_L_relationshipsContains workflows for generating time series of discharge, active network length, and related hydrologic variables used in the stream network expansion–contraction analysis. Includes routines for delineating baseflow-dominated periods. For each catchment, notebooks and processed data (as pickled DataFrames accessed through Pandas Python package) are provided. 5) figure_scriptsProvides the Jupyter notebooks used to generate the figures presented in the paper.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

The Role of Seasonality and the ENSO Mode in Central and East Pacific ENSO Growth and Evolution

Abstract A cyclostationary linear inverse model (CSLIM) is used to investigate the seasonal growth of tropical Pacific Ocean El Niño–Southern Oscillation (ENSO) events with canonical, central Pacific (CP), or eastern Pacific (EP) sea surface temperature (SST) characteristics. Analysis shows that all types of ENSO events experience maximum growth toward final states occurring in November and December. ENSO events with EP characteristics also experience growth into May and June, but CP events do not. A single dominant “ENSO mode,” growing from an equatorial heat content anomaly into a characteristic ENSO-type SST pattern in about 9 months (consistent with the delayed/recharge oscillator model of ENSO), is essential for the predictable development of all ENSO events. Notably, its seasonality is responsible for the late-calendar-year maximum in ENSO amplification. However, this ENSO mode alone does not capture the observed growth and evolution of diverse ENSO events, which additionally involve the seasonal evolution of other nonorthogonal Floquet modes. EP event growth occurs when the ENSO mode is initially “covered up” in combination with other Floquet modes. The ENSO mode’s slow seasonal evolution allows it to emerge while the other modes rapidly evolve and/or decay, leading to strongly amplifying and more predictable EP events. CP events develop when the initial state has a substantial contribution from Floquet modes with meridional mode–like SST structures. Thus, while nearly all ENSO events involve the seasonally varying ENSO-mode dynamics, the diversity and predictability of ENSO events cannot be understood without identifying contributions from the remaining Floquet modes. Significance Statement The purpose of this study is to identify structures that lead to seasonal growth of diverse types of El Niño–Southern Oscillation (ENSO) events. An important contribution from this study is that it uses an observationally constrained, empirically derived seasonal model. We find that processes affecting the evolution of diverse ENSO events are strongly seasonally dependent. ENSO events with eastern equatorial Pacific sea surface temperature (SST) characteristics are closely related to a single “ENSO mode” that resembles theoretical models of ENSO variability. ENSO events that have central equatorial Pacific SST characteristics include contributions from additional “meridional mode” structures that evolve via different physical processes. These findings are an important step in evaluating the seasonal predictability of ENSO diversity.

Vimont, Daniel J.↗
DOE OSTI2023

COMPUTATIONAL MODELING OF IGNITION AND PREMIXED FLAME PROPAGATION INITIATED BY A PRE-CHAMBER TURBULENT JET

Addressing the pressing need for reduced carbon emissions, Turbulent Jet Ignition (TJI) emerges as a promising technology for ultra-lean combustion, offering enhanced thermal efficiencies and minimized cyclic variability in spark-ignited engines. To facilitate rapid testing and integration of this technology, a robust computational modeling framework is crucial. This study delves into the predictive capabilities of computational models for main-chamber ignition and premixed flame propagation using a single-cycle TJI rig measured by Biswas et al. (Applied Thermal Engineering, vol 106, 2016). Employing an open-source compressible flow simulation solver with Large Eddy Simulation (LES) for turbulence modeling, the investigation integrates the conventional Laminar Finite Rate Chemistry (LFRC) model alongside the transported Probability Density Method (PDF) for turbulence-chemistry interaction. A fully-consistent Eulerian Monte-Carlo Fields (EMCF) method is utilized to approximate the transported PDF, while Interaction by Exchange with Mean is employed to close micro-mixing terms in stochastic differential equations. A reduced chemical reaction mechanism with 21 species and 84 reactions (DRM-19) is used for solving chemical kinetics, and a double Gaussian energy deposition model is used to approximate the spark ignition in the pre-chamber. An unstructured O-grid mesh with 0.3 million cells in the prechamber and 1 million cells in the main chamber is employed. Results are divided into two phases: pre-chamber initialization and full TJI simulations. Validation of the predicted pre-chamber flame propagation and the lean ignition in the main-chamber is carried out by using available experimental data. Under quiescent conditions, both the LFRC and transported PDF methods largely underestimate the flame speed and subsequent pressure growth in the pre-chamber. A linear momentum forcing technique is applied to investigate the impact of initial turbulence in the pre-chamber, demonstrating a notable influence on flame propagation. Fine-tuning of the forcing coefficient reproduces the sudden pressure growth observed in the experiment. The experimentally validated pre-chamber simulation serves as the initial condition for the full TJI simulations. It is found that the LFRC model fails to predict lean-ignition in the main-chamber, resulting in a misfiring event. Incorporation of turbulence-chemistry interaction using the transported PDF method substantially improves the prediction of the ignition event in the main-chamber, achieving fair qualitative agreement and quantitative validation of combustion parameters within ±10% of the reported experimental data. The rich simulation results consisting of a full set of statistical description of the thermo-chemical states enable us to gain deep insights into the ignition mechanisms in the main chamber, which is limited when done experimentally. A novel dual ignition phenomenon is revealed in the TJI rig for the first time. Initially, a primary ignition kernel is formed at a downstream location which eventually detaches from the main jet. As the jet momentum decreases, a secondary ignition event follows, this time at a more upstream location which eventually combines with the primary ignition kernel to form a single connected flame front. Investigation of these ignition sequences in chemical composition space reveal distinct differences between the two. The primary ignition event in the main-chamber is followed by a large concentration of active radicals from the pre-chamber jet, accelerating the chain-branching steps, characterizing what has been referred to as flame ignition. In contrast, the secondary ignition occurs in the absence of active radicals in the pre-chamber jet, hence characterized as jet ignition. Further analysis of the effect of pre-chamber jet characteristics on lean ignition in the main-chamber is conducted by setting up cases with different initial pressure ratios (por) between the two chambers, a non-dimensional parameter, ranging from 1.2 to 3.2. As the initial pressure ratio increases, jet momentum increases, with dual ignition observed in cases above por= 2.2. Case with por= 3.2 lead to misfiring. The effect of ignition sequence on global combustion characteristics of TJI is analyzed. Dual ignition events lead to non-monotonicity in combustion characteristics such as global reaction progress variable, flame penetration, and global heat release rate. In dual ignition events, although the rate of fuel consumption and global heat release rate is initially lower, the secondary ignition leads to a sudden increase in flame surface area, resulting in a sudden jump and promoting the overall performance of the TJI system.

42 ENGINEERING↗
DOE OSTI2023

Image-based time series analysis to establish differential disease progression for two Fusarium head blight pathogens in oat spikelets with variable resistance

Oat-based value-added products have increased their value as healthy foodstuff. Fusarium head blight (FHB) infections and the mycotoxins accumulated to the oat seeds, however, pose a challenge to oat production. The FHB infections are predicted to become more prevalent in the future changing climates and under more limited use of fungicides. Both these factors increase the pressure for breeding new resistant cultivars. Until now, however, genetic links in oats against FHB infection have been difficult to identify. Therefore, there is a great need for more effective breeding efforts, including improved phenotyping methods allowing time series analysis and the identification of molecular markers during disease progression. To these ends, dissected spikelets of several oat genotypes with different resistance profiles were studied by image-based methods during disease progression by Fusarium culmorum or F. langsethiae species. The chlorophyll fluorescence of each pixel in the spikelets was recorded after inoculation by the two Fusarium spp., and the progression of the infections was analyzed by calculating the mean maximum quantum yield of PSII (F v /F m ) values for each spikelet. The recorded values were (i) the change in the photosynthetically active area of the spikelet as percentage of its initial size, and (ii) the mean of F v /F m values of all fluorescent pixels per spikelet post inoculation, both indicative of the progression of the FHB disease. The disease progression was successfully monitored, and different stages of the infection could be defined along the time series. The data also confirmed the differential rate of disease progression by the two FHB causal agents. In addition, oat varieties with variable responses to the infections were indicated.

oats↗
DOE OSTI2024

Solving the Orszag–Tang vortex magnetohydrodynamics problem with physics-constrained convolutional neural networks

We study the 2D Orszag–Tang vortex magnetohydrodynamics (MHD) problem through the use of physics-constrained convolutional neural networks (PCNNs) for forecasting the density, ρ, and the magnetic field, B, as well as the prediction of B given the velocity field v of the fluid. In addition to translation equivariance from the convolutional architecture, other physics constraints were embedded: absence of magnetic monopoles, non-negativity of ρ, use of only relevant variables, and the periodic boundary conditions of the problem. The use of only relevant variables and the hard constraint of non-negative ρ were found to facilitate learning greatly. The divergenceless condition ∇·B=0 was implemented as a hard constraint up to machine precision through the use of a magnetic potential to define B=∇×A. Residual networks and data augmentation were also used to improve performance. This allowed for some of the residual models to function as surrogate models and provide reasonably accurate simulations. For the prediction task, the PCNNs were evaluated against a physics-informed neural network, which had the ideal MHD induction equation as a soft constraint. Several models were able to generate highly accurate fields, which are visually almost indistinguishable and have low mean squared error. Only methods with built-in hard constraints produced physical fields with ∇·B=0. The use of PCNNs for MHD has the potential to produce physically consistent real-time simulations to serve as virtual diagnostics in cases where inferences must be made with limited observables.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2020

Measurements of inclusive and differential cross-sections of combined $ t\overline{t}\gamma$ and tWγ production in the eμ channel at 13 TeV with the ATLAS detector

Inclusive and differential cross-sections for the production of top quarks in association with a photon are measured with proton-proton collision data corresponding to an integrated luminosity of 139 fb - 1 . The data were collected by the ATLAS detector at the LHC during Run 2 between 2015 and 2018 at a centre-of-mass energy of 13 TeV. The measurements are performed in a fiducial volume defined at parton level. Events with exactly one photon, one electron and one muon of opposite sign, and at least two jets, of which at least one is b -tagged, are selected. The fiducial cross-section is measured to be $ {39.6}_{-2.3}^{+2.7} $ fb. Differential cross-sections as functions of several observables are compared with state-of-the-art Monte Carlo simulations and next-to-leading-order theoretical calculations. These include cross-sections as functions of photon kinematic variables, angular variables related to the photon and the leptons, and angular separations between the two leptons in the event. All measurements are in agreement with the predictions from the Standard Model.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2022

An investigation of the multi-mode Richtmyer-Meshkov instability at a gas/HE interface using Pagosa

Here, in this work, we present a hydrocode Pagosa and explore the Richtmyer-Meshkov Instability (RMI) at an air/high explosive (HE) interface for the first time that is important but has not received much attention yet in the high explosive safety field. Thus, the presented Pagosa can be expected to predict the whole deflagration-to-detonation transition (DDT) process in future. In Pagosa, spatial discretization is implemented on cubic staggered grids by computing different variables at the vertex and the cell center, respectively, a special operator-splitting technique is employed to reduce the computational cost, and an artificial viscosity is added to handle the discontinuous shock waves in our simulations. To quantitatively evaluate the capability of Pagosa to solve these kinds of instabilities, the single mode Rayleigh-Taylor instability (RTI) and the multimode RMI at an air/SF 6 interface are first performed, respectively. The Pagosa results are compared with the related numerical solutions in the existing references and the experimental result. Moreover, a theoretical derivation of growth of RTI is also provided based on our numerical method. Subsequently, we explore the multi-mode RMI at an air/HE interface as well as the effects of several factors using Pagosa. Numerical results show that Pagosa is a powerful toolset to generate the right structures and the amplitude of RTI and RMI at an air/SF 6 interface. The solid HE can be penetrated by a strong shock wave and forms RMI deformations. The RMI at an air/HE interface behaves very different than at an air/SF 6 interface, periodic, decreased oscillation is observed due to material character, and is very sensitive to the initial simulation settings, that is, a tiny change in physical quantities will lead to a remarkable RMI structure, which is also observed in a shock bubble interaction. The findings in this work are significant, and will present a new insight for the high explosive field.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2026

Hydrogen uptake in graphite matrix at high temperature

Tritium management is a critical challenge for the next generation of nuclear reactors, such as Fluoride Salt Cooled High Temperature Reactors (FHRs) and High Temperature Gas-cooled Reactors (HTGRs), due to the higher production rate (up to 10,000 times) than conventional Light Water Reactors (LWRs). Graphitic materials employed as moderator, reflector, and fuel pebbles offer a potential pathway for tritium recovery by serving as a sink for tritium. Prediction of uptake capacity under reactor relevant conditions remains a challenge due to a lack of low partial pressure data and significant inter-grade variability of graphite. This study addresses these gaps by providing a comprehensive characterization of hydrogen (as a tritium surrogate) uptake and release behavior in the A3-3 graphite matrix (GM) used in fuel pebbles. Uptake measurements are performed at reactor relevant temperatures of 600- 800 °C, 1-200 Torr hydrogen pressure, and 15-120 min equilibration time, followed by thermal desorption spectroscopy up to 1100 °C. Uptake experiments at different equilibration times demonstrate the role of kinetics in hydrogen uptake, which can be modeled as a diffusion-with-trapping process. In the thermodynamics limit, the Sips adsorption model is shown to capture the uptake in A3-3 GM well. Our campaign provides a set of new results for hydrogen uptake in A3-3, including limiting uptake capacity at 600 °C, apparent diffusion coefficient at 600 °C, and the first estimates of the FHR/HTGR relevant (600 °C, 20 Pa partial pressure) equilibrium uptake capacity and time to saturation. Desorption data highlights a new site for hydrogen uptake, not observed in nuclear graphite, which we attribute to the non-graphitized binder. Using the Kissinger method, we estimate activation energy for release from the desorption peaks, confirming the activation energy for release from the basal planes and providing the first estimate for the activation energy of release from the binder.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2019

Adversarial uncertainty quantification in physics-informed neural networks

Here, we introduce a deep learning framework for quantifying and propagating uncertainty in systems governed by non-linear differential equations using physics-informed neural networks. Specifically, we employ latent variable models to construct probabilistic representations for the system states, and put forth an adversarial inference procedure for training them on data, while constraining their predictions to satisfy given physical laws expressed by partial differential equations. Such physics-informed constraints provide a regularization mechanism for effectively training deep generative models as surrogates of physical systems in which the cost of data acquisition is high, and training data-sets are typically small. This provides a flexible framework for characterizing uncertainty in the outputs of physical systems due to randomness in their inputs or noise in their observations that entirely bypasses the need for repeatedly sampling expensive experiments or numerical simulators. Moreover, we demonstrate the effectiveness of our approach through a series of examples involving uncertainty propagation in non-linear conservation laws, and the discovery of constitutive laws for flow through porous media directly from noisy data.

97 MATHEMATICS AND COMPUTING↗