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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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DOE OSTI2016

To The Biorefinery: Delivered Forestland and Agricultural Resources

It can be challenging and costly to transport biomass feedstock supplies from the roadside, or farmgate, to a biorefinery. Given the geographic dispersion and lowbulk density of cellulosic feedstocks, cost effective scaling of commercial biorefinery operations requires overcoming many challenges. The Biomass Research and Development Board’s Feedstock Logistics Interagency Working Group identified four primary barriers related to biorefinery commercialization: • Capacity and efficiency of harvest and collection equipment • High-moisture content leading to degradation of biomass • Variable biomass quality upon arrival at the biorefinery • Costly transportation options.1 Further, feedstock supply systems do not currently mitigate risks such as low crop yield, fire, or competition for resource use. Delivery and preprocessing improvements will allow for the development of a commercial-scale bioenergy industry that achieves national production and cost targets.

To The Biorefinery Factsheet↗
DOE OSTI2018

A High Current, Short Pulse, RF Synchronized Electron Gun for the Stanford Linear Accelerator

For the generation of intense single and multiple bunches of electrons (>8 nc per bunch) for accelerator studies at SLAC, a high peak current photoemission electron gun has been developed. A gallium arsenide photocathode is illuminated by the optical beam from a frequency doubled, actively mode-locked and Q-switched Nd:YAG laser. The mode-locked optical pulses are of variable, sub-nanosecond width and occur with a spacing of 8.4 nsec, synchronized with the 2856 MHz accelerator rf. The gun is designed to be space charge limited at 15 A and 200 kV, although emission of 60 A was obtained with a 57 kV test structure, corresponding to a current density of 180 A/cm 2 . With the proper choice of laser wavelength, the electron beam may be 40% longitudinally polarized.

Sinclair, C.↗
DOE OSTI2020

Energy storage emerging: A perspective from the Joint Center for Energy Storage Research

Energy storage is an integral part of modern society. A contemporary example is the lithium (Li)-ion battery, which enabled the launch of the personal electronics revolution in 1991 and the first commercial electric vehicles in 2010. Most recently, Li-ion batteries have expanded into the electricity grid to firm variable renewable generation, increasing the efficiency and effectiveness of transmission and distribution. Important applications continue to emerge including decarbonization of heavy-duty vehicles, rail, maritime shipping, and aviation and the growth of renewable electricity and storage on the grid. This perspective compares energy storage needs and priorities in 2010 with those now and those emerging over the next few decades. The diversity of demands for energy storage requires a diversity of purpose-built batteries designed to meet disparate applications. Advances in the frontier of battery research to achieve transformative performance spanning energy and power density, capacity, charge/discharge times, cost, lifetime, and safety are highlighted, along with strategic research refinements made by the Joint Center for Energy Storage Research (JCESR) and the broader community to accommodate the changing storage needs and priorities. Innovative experimental tools with higher spatial and temporal resolution, in situ and operando characterization, first-principles simulation, high throughput computation, machine learning, and artificial intelligence work collectively to reveal the origins of the electrochemical phenomena that enable new means of energy storage. This knowledge allows a constructionist approach to materials, chemistries, and architectures, where each atom or molecule plays a prescribed role in realizing batteries with unique performance profiles suitable for emergent demands.

Trahey, Lynn↗
DOE OSTI2026

Soil Water Retention and Hydraulic Conductivity Data and Model at Pump House in East River Watershed, Colorado 2019-2024

This data package includes soil water retention and hydraulic conductivity data and model fitting results from measurements of ex-situ soil samples and in-situ soil sensors near Pump House at Mount Crested Butte in the East River Watershed. Soil water retention curves (SWRC) characterize soil water content as a function of soil water potential. SWRC depends on soil texture and pore structure and can be used to describe the constraints on biogeochemical processes in terms of soil water availability. In this data package, the sample identification follows the format ER-X-Y, where ER refers to East River, X is the location identifier, and Y is the depth identifier at the same X (shallow Y=1). Specifically, ER-PHS, ER-LMC, ER-LMF, and ER-SMN are associated with ecohydrology sites under the East-Taylor Watershed Community Observatory Sites directory, and ER-RBTn (upslope n=1) are sampling transects during the 2019 Rootball Campaign. The sample and location information can be found in metadata.csv. Sampling and Measurements Each sample falls into one of the three sampling methods – (1) intact cores, (2) repacked samples, or (3) soil sensors – and one of the two measurement methods – (a) laboratory or (b) in-situ. Both intact cores and repacked samples were measured using the laboratory methods, which include measurements of soil water potential (HYPROP & WP4C, METER), saturated (KSAT, METER) and unsaturated hydraulic conductivity (HYPROP). The in-situ method uses a pair of co-located soil sensors to measure volumetric water content (TEROS12, METER) and soil water potential (TEROS21, METER), and the hydraulic conductivity was not measured. In comparison, the laboratory methods progress from full saturation to dry conditions, and the in-situ method includes both dry-to-wet and wet-to-dry cycles. The sampling and measurement methods for each sample can be found in metadata.csv, and more information about the measurements is detailed in the Methods section below. Models Retention and hydraulic conductivity data were fitted with four van-Genuchten-type models (specified by “model_name” column in the files): (1) traditional constrained van Genuchten model (“vG_constrained”), (2) traditional unconstrained van Genuchten model (“vG_unconstrained”), (3) PDI-variant of the constrained van Genuchten model (“vG_constrained_PDI”), and (4) PDI-variant of the unconstrained van Genuchten model (“vG_unconstrained_PDI”). The difference between the constrained (1: n) and the unconstrained (2: n, m) van Genuchten models is the number of pore-size distribution parameters in the model equations, giving the unconstrained model more degrees of freedom when fitting the data. Between the traditional and the PDI-variant models, model fitting differs the most at the dry end of the measurements. The traditional models allow infinite suction at the residual water content (water content does not drop below residual water content), and the PDI-variant models enforce a soil water potential value of pF=6.8 (~ -630 MPa) at oven-dryness (water content reaches 0). The inclusion of the van-Genuchten-type models is due to their common application. If other retention models are required, users can access the data in data.csv for further data fitting. More information about the models can be found in the Methods section below. Fitting Tasks The model fitting can be categorized into three levels of tasks (specified by “fitting_task” column in the files). Level 1 (“fit_retention”) only includes retention data fitting (the only level available for the in-situ method). Level 2 (“fit_retention_conductivity”) includes both retention and hydraulic conductivity data fitting, and the saturated hydraulic conductivity (Ks, a parameter of the hydraulic conductivity functions) is fixed by the measurements from KSAT. Level 3 (“fit_retention_conductivity_Ks”) also includes both retention and hydraulic conductivity data fitting, but Ks is a fitted parameter without the constraints from KSAT measurements. Among the same retention models (e.g. vG_constrained models of the same sample), level 1 should produce the best retention data fitting. Level 2 should have the highest misfit of the retention and hydraulic conductivity data, because the retention and hydraulic conductivity functions share common model parameters, and the unsaturated hydraulic conductivity (HYPROP) data fitting is subject to Ks measured independently by KSAT. Level 3 should have mid-level misfits of the retention and hydraulic conductivity data. While level 3 fits the hydraulic conductivity data better than level 2, the fitted Ks value might be unreasonable due to the lack of constraints at the wet end of the measurements. General recommendation when using this data package: (1) Choice of sampling methods: Intact cores and in-situ soil sensors could be prioritized because these sampling methods are less destructive. While the repacked samples were packed to the target bulk density (estimated post-sampling, when sample volume was known), these samples had altered pore structures. Nevertheless, intact cores might suffer from sample gaps that would lead to overestimation of Ks (sample gaps can be inferred from the “soil_sample_volume” column in metadata.csv when the value is < 249). In-situ method also has higher uncertainty in characterizing the wet end of the SWRC because of sensor limitations and the difficulty in reaching full saturation under natural conditions. (2) Choice of fitting tasks: When only retention data is needed, level 1 (“fit_retention”) should be prioritized. When both retention and hydraulic conductivity data are needed, level 2 (“fit_retention_conductivity”) could be prioritized. (3) Choice of models: This could depend on what the downstream models call for. If no specific model is required, model misfit could be used as a ranking criterion. Model misfit values in terms of RMSE can be found in model_parameters.csv. The following files are included in this data package: (1) metadata.csv – This file includes the general information of each sample, including location (description, geocoordinates, elevation), sampling and measurements details (method, depth, time or period, volume, instruments), and soil physical properties (bulk density, saturated hydraulic conductivity, only applicable to physical soil samples). (2) data.csv – This file includes soil water potential, volumetric water content, and unsaturated hydraulic conductivity data of each sample. Column “instrument” specifies the instrument (HYPROP, WP4C, or TEROS) used to perform the measurements. (3) model_fit.csv – This file includes soil water potential, volumetric water content, and unsaturated hydraulic conductivity fitted from the four models and three fitting tasks. Column “model_name” specifies the retention model used, and “fitting_task” specifies the level of data fitting. Missing values indicate that the variable does not apply to that fitting task. (4) model_parameters.csv – This file includes the fitted model parameters, model misfits, and conventional water content thresholds (field capacity and wilting point) from the four models and three fitting tasks. Column “model_name” specifies the retention model used, and “fitting_task” specifies the level of data fitting. Missing values indicate that the parameter does not apply to that model and/or that fitting task. (5) data_Ks.csv – This file includes the saturated hydraulic conductivity measurements from KSAT. (6) /figure/*.png – This folder includes three quick visualizations of the data, retention model fitting results and misfits, and hydraulic conductivity model fitting results, misfits, and parameters. The model fitting results are separated by samples and fitting tasks and colored by models. Zoom-in required. (7) /hyprop/*.bdhx – This folder includes proprietary hyprop files that require the free Labros SoilView-Analysis (METER) to open. Users can explore data fitting using other retention models (i.e. Brooks-Corey, Fredlund-Xing, Kosugi, bimodal models). Be aware that Ks value is pre-entered under “Fitting tab, Conductivity functions parameters” for level 2 fitting. If the value is lost, please refer to metadata.csv under “Ks” column. (8) Six file-level metadata that summarize file, header, column, and variable information of all files. This work was supported by the Watershed Function Science Focus Area at Lawrence Berkeley National Laboratory funded by the US Department of Energy, Office of Science, Biological and Environmental Research under Contract No. DE-AC02-05CH11231.

EARTH SCIENCE > LAND SURFACE > SOILS↗
DOE OSTI2026

Soil Water Retention and Hydraulic Conductivity Data and Model at Snodgrass Mountain in East River Watershed, Colorado 2020-2025

This data package includes soil water retention and hydraulic conductivity data and model fitting results from measurements of ex-situ soil samples and in-situ soil sensors at Snodgrass Mountain. Soil water retention curves (SWRC) characterize soil water content as a function of soil water potential. SWRC depends on soil texture and pore structure and can be used to describe the constraints on biogeochemical processes in terms of soil water availability. In this data package, the sample identification follows the format SG-X-Y, where SG refers to Snodgrass Mountain, X is the location identifier, and Y is the depth identifier at the same X (shallow Y=1). Specifically, SG-EHS is associated with ecohydrology sites under the East-Taylor Watershed Community Observatory Sites directory, and SG-ERTn (upslope n=1) are points along the Snodgrass electrical resistivity tomography transect not associated with the existing site names in the directory. The sample and location information can be found in metadata.csv. Sampling and Measurements Each sample falls into one of the three sampling methods – (1) intact cores, (2) repacked samples, or (3) soil sensors – and one of the two measurement methods – (a) laboratory or (b) in-situ. Both intact cores and repacked samples were measured using the laboratory methods, which include measurements of soil water potential (HYPROP & WP4C, METER), saturated (KSAT, METER) and unsaturated hydraulic conductivity (HYPROP). The in-situ method uses a pair of co-located soil sensors to measure volumetric water content (TEROS12, METER) and soil water potential (TEROS21, METER), and the hydraulic conductivity was not measured. In comparison, the laboratory methods progress from full saturation to dry conditions, and the in-situ method includes both dry-to-wet and wet-to-dry cycles. The sampling and measurement methods for each sample can be found in metadata.csv, and more information about the measurements is detailed in the Methods section below. Models Retention and hydraulic conductivity data were fitted with four van-Genuchten-type models (specified by “model_name” column in the files): (1) traditional constrained van Genuchten model (“vG_constrained”), (2) traditional unconstrained van Genuchten model (“vG_unconstrained”), (3) PDI-variant of the constrained van Genuchten model (“vG_constrained_PDI”), and (4) PDI-variant of the unconstrained van Genuchten model (“vG_unconstrained_PDI”). The difference between the constrained (1: n) and the unconstrained (2: n, m) van Genuchten models is the number of pore-size distribution parameters in the model equations, giving the unconstrained model more degrees of freedom when fitting the data. Between the traditional and the PDI-variant models, model fitting differs the most at the dry end of the measurements. The traditional models allow infinite suction at the residual water content (water content does not drop below residual water content), and the PDI-variant models enforce a soil water potential value of pF=6.8 (~ -630 MPa) at oven-dryness (water content reaches 0). The inclusion of the van-Genuchten-type models is due to their common application. If other retention models are required, users can access the data in data.csv for further data fitting. More information about the models can be found in the Methods section below. Fitting Tasks The model fitting can be categorized into three levels of tasks (specified by “fitting_task” column in the files). Level 1 (“fit_retention”) only includes retention data fitting (the only level available for the in-situ method). Level 2 (“fit_retention_conductivity”) includes both retention and hydraulic conductivity data fitting, and the saturated hydraulic conductivity (Ks, a parameter of the hydraulic conductivity functions) is fixed by the measurements from KSAT. Level 3 (“fit_retention_conductivity_Ks”) also includes both retention and hydraulic conductivity data fitting, but Ks is a fitted parameter without the constraints from KSAT measurements. Among the same retention models (e.g. vG_constrained models of the same sample), level 1 should produce the best retention data fitting. Level 2 should have the highest misfit of the retention and hydraulic conductivity data, because the retention and hydraulic conductivity functions share common model parameters, and the unsaturated hydraulic conductivity (HYPROP) data fitting is subject to Ks measured independently by KSAT. Level 3 should have mid-level misfits of the retention and hydraulic conductivity data. While level 3 fits the hydraulic conductivity data better than level 2, the fitted Ks value might be unreasonable due to the lack of constraints at the wet end of the measurements. General recommendation when using this data package: (1) Choice of sampling methods: Intact cores and in-situ soil sensors could be prioritized because these sampling methods are less destructive. While the repacked samples were packed to the target bulk density (estimated post-sampling, when sample volume was known), these samples had altered pore structures. Nevertheless, intact cores might suffer from sample gaps that would lead to overestimation of Ks (sample gaps can be inferred from the “soil_sample_volume” column in metadata.csv when the value is < 249). In-situ method also has higher uncertainty in characterizing the wet end of the SWRC because of sensor limitations and the difficulty in reaching full saturation under natural conditions. (2) Choice of fitting tasks: When only retention data is needed, level 1 (“fit_retention”) should be prioritized. When both retention and hydraulic conductivity data are needed, level 2 (“fit_retention_conductivity”) could be prioritized. (3) Choice of models: This could depend on what the downstream models call for. If no specific model is required, model misfit could be used as a ranking criterion. Model misfit values in terms of RMSE can be found in model_parameters.csv. The following files are included in this data package: (1) metadata.csv – This file includes the general information of each sample, including location (description, geocoordinates, elevation), sampling and measurements details (method, depth, time or period, volume, instruments), and soil physical properties (bulk density, saturated hydraulic conductivity, only applicable to physical soil samples). (2) data.csv – This file includes soil water potential, volumetric water content, and unsaturated hydraulic conductivity data of each sample. Column “instrument” specifies the instrument (HYPROP, WP4C, or TEROS) used to perform the measurements. (3) model_fit.csv – This file includes soil water potential, volumetric water content, and unsaturated hydraulic conductivity fitted from the four models and three fitting tasks. Column “model_name” specifies the retention model used, and “fitting_task” specifies the level of data fitting. Missing values indicate that the variable does not apply to that fitting task. (4) model_parameters.csv – This file includes the fitted model parameters, model misfits, and conventional water content thresholds (field capacity and wilting point) from the four models and three fitting tasks. Column “model_name” specifies the retention model used, and “fitting_task” specifies the level of data fitting. Missing values indicate that the parameter does not apply to that model and/or that fitting task. (5) data_Ks.csv – This file includes the saturated hydraulic conductivity measurements from KSAT. (6) /figure/*.png – This folder includes three quick visualizations of the data, retention model fitting results and misfits, and hydraulic conductivity model fitting results, misfits, and parameters. The model fitting results are separated by samples and fitting tasks and colored by models. Zoom-in required. (7) /hyprop/*.bdhx – This folder includes proprietary hyprop files that require the free Labros SoilView-Analysis (METER) to open. Users can explore data fitting using other retention models (i.e. Brooks-Corey, Fredlund-Xing, Kosugi, bimodal models). Be aware that Ks value is pre-entered under “Fitting tab, Conductivity functions parameters” for level 2 fitting. If the value is lost, please refer to metadata.csv under “Ks” column. (8) Six file-level metadata that summarize file, header, column, and variable information of all files. This work was supported by the Watershed Function Science Focus Area at Lawrence Berkeley National Laboratory funded by the US Department of Energy, Office of Science, Biological and Environmental Research under Contract No. DE-AC02-05CH11231.

EARTH SCIENCE > LAND SURFACE > SOILS↗
DOE OSTI2026

Soil Water Retention and Hydraulic Conductivity Data and Model at Trail Creek in Taylor River Watershed, Colorado 2024-2025

This data package includes soil water retention and hydraulic conductivity data and model fitting results from measurements of ex-situ soil samples and in-situ soil sensors near Trail Creek. Soil water retention curves (SWRC) characterize soil water content as a function of soil water potential. SWRC depends on soil texture and pore structure and can be used to describe the constraints on biogeochemical processes in terms of soil water availability. In this data package, the sample identification follows the format TR-X-Y, where TR refers to Trail Creek, X is the treatment block identifier, and Y is the location identifier. Specifically, TR-ASCC1 is the control treatment block under the Adaptive Silviculture for Climate Change (ASCC) project, and TR-ASCC2 is the clear-cut treatment block. TR-ASCC-EHSn is associated with ecohydrology sites under the East-Taylor Watershed Community Observatory Sites directory, and TR-ASCC-ERTn (upslope n=1) are ecohydrology sites along the electrical resistivity tomography transects. The sample and location information can be found in metadata.csv, and the data from the soil sensors will be included in a future data version when the observation period becomes sufficiently long for data analysis. Sampling and Measurements Each sample falls into one of the two sampling methods – (1) intact cores or (2) soil sensors – and one of the two measurement methods – (a) laboratory or (b) in-situ. The intact cores were measured using the laboratory methods, which include measurements of soil water potential (HYPROP & WP4C, METER), saturated (KSAT, METER) and unsaturated hydraulic conductivity (HYPROP). The in-situ method uses a pair of co-located soil sensors to measure volumetric water content (TEROS12, METER) and soil water potential (TEROS21, METER), and the hydraulic conductivity was not measured. In comparison, the laboratory methods progress from full saturation to dry conditions, and the in-situ method includes both dry-to-wet and wet-to-dry cycles. The sampling and measurement methods for each sample can be found in metadata.csv, and more information about the measurements is detailed in the Methods section below. Models Retention and hydraulic conductivity data were fitted with four van-Genuchten-type models (specified by “model_name” column in the files): (1) traditional constrained van Genuchten model (“vG_constrained”), (2) traditional unconstrained van Genuchten model (“vG_unconstrained”), (3) PDI-variant of the constrained van Genuchten model (“vG_constrained_PDI”), and (4) PDI-variant of the unconstrained van Genuchten model (“vG_unconstrained_PDI”). The difference between the constrained (1: n) and the unconstrained (2: n, m) van Genuchten models is the number of pore-size distribution parameters in the model equations, giving the unconstrained model more degrees of freedom when fitting the data. Between the traditional and the PDI-variant models, model fitting differs the most at the dry end of the measurements. The traditional models allow infinite suction at the residual water content (water content does not drop below residual water content), and the PDI-variant models enforce a soil water potential value of pF=6.8 (~ -630 MPa) at oven-dryness (water content reaches 0). The inclusion of the van-Genuchten-type models is due to their common application. If other retention models are required, users can access the data in data.csv for further data fitting. More information about the models can be found in the Methods section below. Fitting Tasks The model fitting can be categorized into three levels of tasks (specified by “fitting_task” column in the files). Level 1 (“fit_retention”) only includes retention data fitting (the only level available for the in-situ method). Level 2 (“fit_retention_conductivity”) includes both retention and hydraulic conductivity data fitting, and the saturated hydraulic conductivity (Ks, a parameter of the hydraulic conductivity functions) is fixed by the measurements from KSAT. Level 3 (“fit_retention_conductivity_Ks”) also includes both retention and hydraulic conductivity data fitting, but Ks is a fitted parameter without the constraints from KSAT measurements. Among the same retention models (e.g. vG_constrained models of the same sample), level 1 should produce the best retention data fitting. Level 2 should have the highest misfit of the retention and hydraulic conductivity data, because the retention and hydraulic conductivity functions share common model parameters, and the unsaturated hydraulic conductivity (HYPROP) data fitting is subject to Ks measured independently by KSAT. Level 3 should have mid-level misfits of the retention and hydraulic conductivity data. While level 3 fits the hydraulic conductivity data better than level 2, the fitted Ks value might be unreasonable due to the lack of constraints at the wet end of the measurements. General recommendation when using this data package: (1) Choice of sampling methods: Intact cores might suffer from sample gaps that would lead to overestimation of Ks (sample gaps can be inferred from the “soil_sample_volume” column in metadata.csv when the value is < 249). In-situ method has higher uncertainty in characterizing the wet end of the SWRC because of sensor limitations and the difficulty in reaching full saturation under natural conditions. (2) Choice of fitting tasks: When only retention data is needed, level 1 (“fit_retention”) should be prioritized. When both retention and hydraulic conductivity data are needed, level 2 (“fit_retention_conductivity”) could be prioritized. (3) Choice of models: This could depend on what the downstream models call for. If no specific model is required, model misfit could be used as a ranking criterion. Model misfit values in terms of RMSE can be found in model_parameters.csv. The following files are included in this data package: (1) metadata.csv – This file includes the general information of each sample, including location (description, geocoordinates, elevation), sampling and measurements details (method, depth, time or period, volume, instruments), and soil physical properties (bulk density, saturated hydraulic conductivity, only applicable to physical soil samples). (2) data.csv – This file includes soil water potential, volumetric water content, and unsaturated hydraulic conductivity data of each sample. Column “instrument” specifies the instrument (HYPROP, WP4C, or TEROS) used to perform the measurements. (3) model_fit.csv – This file includes soil water potential, volumetric water content, and unsaturated hydraulic conductivity fitted from the four models and three fitting tasks. Column “model_name” specifies the retention model used, and “fitting_task” specifies the level of data fitting. Missing values indicate that the variable does not apply to that fitting task. (4) model_parameters.csv – This file includes the fitted model parameters, model misfits, and conventional water content thresholds (field capacity and wilting point) from the four models and three fitting tasks. Column “model_name” specifies the retention model used, and “fitting_task” specifies the level of data fitting. Missing values indicate that the parameter does not apply to that model and/or that fitting task. (5) data_Ks.csv – This file includes the saturated hydraulic conductivity measurements from KSAT. (6) /figure/*.png – This folder includes three quick visualizations of the data, retention model fitting results and misfits, and hydraulic conductivity model fitting results, misfits, and parameters. The model fitting results are separated by samples and fitting tasks and colored by models. Zoom-in required. (7) /hyprop/*.bdhx – This folder includes proprietary hyprop files that require the free Labros SoilView-Analysis (METER) to open. Users can explore data fitting using other retention models (i.e. Brooks-Corey, Fredlund-Xing, Kosugi, bimodal models). Be aware that Ks value is pre-entered under “Fitting tab, Conductivity functions parameters” for level 2 fitting. If the value is lost, please refer to metadata.csv under “Ks” column. (8) Six file-level metadata that summarize file, header, column, and variable information of all files. This work was supported by the Watershed Function Science Focus Area at Lawrence Berkeley National Laboratory funded by the US Department of Energy, Office of Science, Biological and Environmental Research under Contract No. DE-AC02-05CH11231.

EARTH SCIENCE > LAND SURFACE > SOILS↗
DOE OSTI2018

Kinetics of D/H isotope fractionation between molecular hydrogen and water

At equilibrium, the D/H isotope fractionation factor between H 2 and H 2 O (α H 2 O-H 2 (eq) ) is a sensitive indicator of temperature, and has been used as a geothermometer for natural springs and gas discharges. However, δD H 2 measured in spring waters may underestimate subsurface temperatures of origin due to partial isotopic re-equilibration during ascent and cooling. In this work, we present new experimental data on the kinetics of D–H exchange for H 2 dissolved in liquid water at temperatures below 100 °C. Comparing these results with published exchange rates obtained from gas phase experiments (100–400 °C), we derive a consistent activation energy of 52 kJ/mol, and the following rate expressions; ln k =9.186 - 6298/ T and k 1 = 9764.61[H 2 O] e-6298/ T where T is absolute temperature (K), k is the universal rate constant ([L/mol]/hr), and k 1 is a pseudo-first-order constant (hr -1 ) applicable to water-dominated terrestrial systems by constraining [H 2 O] as the density of H 2 O (in mol/L) at the P-T of interest. The density-dependent rate constant accounts for the kinetic disparity of D–H exchange with H 2 when dissolved in liquid H 2 O relative to a gas/steam phase, exemplifed by 1/ k 1 at 100 °C of ~2 days in liquid, versus ~7 yrs in saturated steam. This difference may explain the high variability of δDH 2 observed in fumarolic gases. Fluids convecting in the crust frequently reach T > 225 °C, where isotopic equilibrium is rapidly attained (<1 hr). We compare fractionation factors measured in natural fluids (α OBS ) with values expected for equilibrium at the T of acquisition. Where these values differ, we use kinetic models to estimate cooling rates during upward advection that account for the observed disequilibrium. Models fit to fluids from Yellowstone Park and the Lost City (deep-sea) vent field, both recovered at ~90 °C, require respective transit times of ~7 hrs and ~11 days between higher temperature reaction zones and the surface. Using estimates of subsurface depths of origin, however, suggests similar mean fluid flow rates (10 s of meters/hr). Additional complications must be considered when interpreting the δDH 2 of lower-temperature effluent. When applied to data from deep-sea hydrothermal systems, our kinetic models indicate microbial catalysis accelerates D–H exchange once fluids cool below ~60 °C. The H 2 measured in both continental alkaline springs and fracture fluids from Precambrian shield rock is likely produced at T < 100 °C, through processes such as serpentinization. In these settings, δDH 2 values appear closer to equilibrium with H 2 O than those from geothermal systems. Considering kinetic isotope effects may yield H 2 that is out of equilibrium when generated at lower temperatures, we calculate maximum (isothermal) times to apparent isotopic equilibrium of 1.3 yrs at 50 °C, 9 yrs at 25 °C, and 35 yrs at 10 °C. A similar calculation applied to Antarctic brines (-13 °C), where measured δDH 2 is far from equilibrium, yields ~350 yrs. This time is shorter than the fluids have been isolated (2.8 ka), suggesting kinetic isotope effects associated with H 2 destruction or loss via diffusion may also be possible.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2016

PICS: SIMULATIONS OF STRONG GRAVITATIONAL LENSING IN GALAXY CLUSTERS

Gravitational lensing has become one of the most powerful tools available for investigating the “dark side” of the universe. Cosmological strong gravitational lensing, in particular, probes the properties of the dense cores of dark matter halos over decades in mass and offers the opportunity to study the distant universe at flux levels and spatial resolutions otherwise unavailable. Studies of strongly lensed variable sources offer even further scientific opportunities. One of the challenges in realizing the potential of strong lensing is to understand the statistical context of both the individual systems that receive extensive follow-up study, as well as that of the larger samples of strong lenses that are now emerging from survey efforts. Motivated by these challenges, we have developed an image simulation pipeline, Pipeline for Images of Cosmological Strong lensing (PICS), to generate realistic strong gravitational lensing signals from group- and cluster-scale lenses. PICS uses a low-noise and unbiased density estimator based on (resampled) Delaunay Tessellations to calculate the density field; lensed images are produced by ray-tracing images of actual galaxies from deep Hubble Space Telescope observations. Other galaxies, similarly sampled, are added to fill in the light cone. The pipeline further adds cluster member galaxies and foreground stars into the lensed images. The entire image ensemble is then observed using a realistic point-spread function that includes appropriate detector artifacts for bright stars. Noise is further added, including such non-Gaussian elements as noise window-paning from mosaiced observations, residual bad pixels, and cosmic rays. The aim is to produce simulated images that appear identical—to the eye (expert or otherwise)—to real observations in various imaging surveys.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2017

Phase diagram of boron carbide with variable carbon composition

Boron carbide exhibits intrinsic substitutional disorder over a broad composition range. The structure consists of 12-atom icosahedra placed at the vertices of a rhombohedral lattice, together with a 3-atom chain along the threefold axis. In the high-carbon limit, one or two carbon atoms can replace boron atoms on the icosahedra while the chains are primarily of type C-B-C. We fit an interatomic pair interaction model to density-functional-theory total energies to investigate the substitutional carbon disorder. Monte Carlo simulations with sampling improved by replica exchange and augmented by two-dimensional multiple histogram analysis predict three phases. The low-temperature, high-carbon-composition monoclinic Cm structure disorders through a pair of phase transitions, first via an Ising-like transition to a monoclinic centrosymmetric state with space group C2/m, then via a first-order three-state Potts-like transition to the experimentally observed rhombohedral R$\bar3$ m symmetry.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2018

Geometric Analysis Based Double Closed-loop Iterative Learning Control of Output PDF Shaping of Fiber Length Distribution in Refining Process

In order to improve the pulp quality and to reduce the energy consumption, the fiber length distribution (FLD) is generally employed as one of the important technological indexes in the refining process. Considering that the traditional mean and variance of fiber length are unable to adequately characterize the non-Gaussian distribution properties, this paper proposes a novel geometric analysis based double closed-loop iterative learning control (ILC) method for probability density function (PDF) shaping of output FLD in the refining process. Primarily, a RBF neural network (NN) with Gaussian-type is utilized to approximate the square root PDF in the inner loop, where the RBF basis function parameters (center and width) are tuned between any two adjacent batches by using an ILC law, and the subspace identification method can be applied to establish the state-space model of weight vector. Then, for the sake of accelerating the convergence rate of the closed-loop sysyem, a geometric analysis based ILC method is adopted in the outer loop. Lastly, both simulation and experiments demonstrate the effectiveness and practicability of the proposed approach.

42 ENGINEERING↗
DOE OSTI2016

Local structure in BaTi O 3 - BiSc O 3 dipole glasses

Local structures in cubic perovskite-type (Ba 0.6 Bi 0.4 )(Ti 0.6 Sc 0.4 )O 3 solid solutions that exhibit reentrant dipole glass behavior have been studied with variable-temperature x-ray/neutron total scattering, extended x-ray absorption fine structure, and electron diffraction methods. Simultaneous fitting of these data using a reverse Monte Carlo algorithm provided instantaneous atomic configurations, which have been used to extract local displacements of the constituent species. The smaller Bi and Ti atoms exhibit probability density distributions that consist of 14 and 8 split sites, respectively. In contrast, Ba and Sc feature single-site distributions. The multisite distributions arise from large and strongly anisotropic off-center displacements of Bi and Ti. The cation displacements are correlated over a short range, with a correlation length limited by chemical disorder. The magnitudes of these displacements and their anisotropy, which are largely determined by local chemistry, change relatively insignificantly on cooling from room temperature. The structure features a nonrandom distribution of local polarization with low-dimensional polar clusters that are several unit cells in size. In situ measurements of atomic pair-distribution function under applied electric field were used to study field-induced changes in the local structure; however, no significant effects besides lattice expansion in the direction of the field could be observed up to electric-field values of 4 kVmm -1 .

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2018

Part-per-million quantization and current-induced breakdown of the quantum anomalous Hall effect

In the quantum anomalous Hall effect, quantized Hall resistance and vanishing longitudinal resistivity are predicted to result from the presence of dissipationless, chiral edge states and an insulating two-dimensional bulk, without requiring an external magnetic field. Here, we explore the potential of this effect in magnetic topological insulator thin films for metrological applications. Using a cryogenic current comparator system, we measure quantization of the Hall resistance to within one part per million and, at lower current bias, longitudinal resistivity under 10 mΩ at zero magnetic field. Increasing the current density past a critical value leads to a breakdown of the quantized, low-dissipation state, which we attribute to electron heating in bulk current flow. Here, we further investigate the prebreakdown regime by measuring transport dependence on temperature, current, and geometry, and find evidence for bulk dissipation, including thermal activation and possible variable-range hopping.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2020

CHARACTERISTICS OF CYCLE-TO-CYCLE COMBUSTION VARIABILITY AT PARTIAL-BURN LIMITED AND MISFIRE LIMITED SPARK TIMING UNDER HIGHLY DILUTED CONDITIONS

Diluted combustion with exhaust gas recirculation (EGR) has been widely employed to improve the fuel economy of spark ignition engines. The combustion kinetics, however, are affected and the flame propagation speed is decreased. In order to compensate for this adverse effect, the spark timing needs to be recalibrated to achieve maximum brake torque (MBT). At high levels of EGR dilution, the spark timing is constrained by two ignition limits: 1) the partial-bum limit where the spark timing is retarded from MBT and 2) the misfire limit where the spark timing is too advanced. This work uses a probabilistic framework to capture the differences between both ignition limits. In particular, it introduces the concept of a nominal indicated mean effective pressure (IMEP) distribution based on the stochastic properties of the cycle-to-cycle variability (CCV) at nominal stable conditions. By defining a nominal band where fully burned cycles occur with high probability, we introduce a cycle classification method that can be used to 1) determine the level of randomness of misfire and partial-burn events, and 2) measure CCV. The new CCV metric based on the density of the nominal band is compared with the traditional coefficient of variation of IMEP (CoVIMEP). It is shown that the nominal band concept, together with the CoVIMEP, can help to discern between partial-burn limited and misfire limited conditions. Furthermore, the Kullback-Leibler divergence is used to demonstrate that the IMEP distribution is significantly different between nominal and partial-burn/misfire limited conditions. Experiments are carried at various EGR levels and spark timings while recoding in-cylinder pressure at steady state. Although the emphasis of this work is to characterize the differences of both ignition limits from a probabilistic point of view, similarities between partial-burn cycles at either limiting conditions are also discussed.

Maldonado, Bryan↗
DOE OSTI2020

Fine Structures in the Main Sequence Revealed by Gaia Data Release 2

The Hertzsprung–Russell diagram (HRD) is fully examined using the Fourier analysis. This work shows more stars are above the gap in the lower main sequence than below it, and this implies that stars spend more time above the gap while they undergo variability associated with the {sup 3}He instability. The enhanced HRD also shows the width of the gap is not linear and depends on the G{sub BP}−G{sub RP} color up until G{sub BP}−G{sub RP}=2.7. Beyond this color limit, the gap is hardly seen. Besides, a new low density region is revealed for the first time centered at M {sub G} ≈ 10.7 and G {sub BP} − G {sub RP} ≈ 2.8, which is below the lower right corner of the gap. This work also shows that the main sequence appears to have fine stripes where stellar densities are relatively low or high compared to their adjacent regions on the main sequence. These stripes can be seen throughout the main sequence of stars redder than G {sub BP} − G {sub RP} = 0.8 and are not limited to any specific color or spectral type. Slopes of these features are different from the main sequence, but are pretty consistent throughout the main sequence, with a few exceptions. We are perplexed by these new features, but the complexities of stellar atmospheric features and opacities of dwarfs may have caused these patterns.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2016

Observational evidence of dynamic star formation rate in Milky Way giant molecular clouds

Star formation on galactic scales is known to be a slow process, but whether it is slow on smaller scales is uncertain. We cross-correlate 5469 giant molecular clouds (GMCs) from a new all-sky catalog with 256 star-forming complexes (SFCs) to build a sample of 191 SFC-GMC complexes—collections of multiple clouds each matched to 191 SFCs. The total mass in stars harbored by these clouds is inferred from WMAP free–free fluxes. We measure the GMC mass, the virial parameter, the star formation efficiency ϵ and the star formation rate per freefall time ϵ {sub ff}. Both ϵ and ϵ {sub ff} range over 3–4 orders of magnitude. We find that 68.3% of the clouds fall within σ{sub logϵ}=0.79±0.22 dex and σ{sub logϵ{sub f{sub f}}}=0.91±0.22 dex about the median. Compared to these observed scatters, a simple model with a time-independent ϵ {sub ff} that depends on the host GMC properties predicts σ{sub logϵ{sub f{sub f}}}=0.12--0.24. Allowing for a time-variable ϵ {sub ff}, we can recover the large dispersion in the rate of star formation. This strongly suggests that star formation in the Milky Way is a dynamic process on GMC scales. We also show that the surface star formation rate profile of the Milky Way correlates well with the molecular gas surface density profile.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2015

Factors Affecting Seismic Velocity in Alluvium

Yucca Flat at the Nevada National Security Site has been selected as the Source Physics Experiment (SPE) Dry Alluvium Geology Phase II site. The alluvium in this part of Yucca Flat is typical of desert basin fill, with discontinuous beds that are highly variable in clast size and provenance. Detailed understanding of the subsurface geology will be needed for interpretation of the SPE seismic data. A 3D seismic velocity model, created for Yucca Flat using interval seismic velocity data, shows variations in velocity within alluvium near the SPE Phase II site beyond the usual gradual increase of density with depth due to compaction. In this study we examined borehole lithologic logs, geophysical logs, downhole videos, and laboratory analyses of sidewall core samples to understand which characteristics of the alluvium are related to these variations in seismic velocity. Seismic velocity of alluvium is generally related to its density, which can be affected by sediment provenance, clast size, gravel percentage, and matrix properties, in addition to compaction. This study presents a preliminary subdivision of the alluvial strata in the SPE Phase II area into mappable units expected to be significant to seismic modeling. Further refinements of the alluvial units may be possible when seismic data are obtained from SPE Phase II tests.

58 GEOSCIENCES↗
DOE OSTI2022

Understanding the Microphysical Control and Spatial‐Temporal Variability of Warm Rain Probability Using CloudSat and MODIS Observations

Abstract By combining measurements from MODIS and the CloudSat radar, we develop a parameterization scheme to quantify the combined microphysical controls by liquid water path (LWP) and cloud droplet number concentration (CDNC) of the probability of precipitation (PoP) in marine low cloud over tropical oceans. We demonstrate that the spatial‐temporal variation of grid‐mean in‐cloud can be largely explained by the variation of the joint probability density function of LWP and CDNC in the phase space specified by the bivariate PoP (LWP and CDNC) function. Through a series of sensitivity tests guided by this understanding, we find that in the Southeastern Pacific and Atlantic the stratocumulus to cumulus transition of the is mainly due to the variation of CDNC while the annual cycle is mainly due to the variation of LWP. The results of this study provide a viable way to diagnose the root cause of warm rain problems in global climate models.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2018

Rigidity of the Laughlin Liquid

We consider general N-particle wave functions that have the form of a product of the Laughlin state with filling factor 1/ℓ and an analytic function of the N variables. This is the most general form of a wave function that can arise through a perturbation of the Laughlin state by external potentials or impurities, while staying in the lowest Landau level and maintaining the strong correlations of the original state. We show that the perturbation can only shift or lower the 1-particle density but nowhere increase it above a maximum value. Consequences of this bound for the response of the Laughlin state to external fields are discussed.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗