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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 OSTI2015

Study of current-voltage characteristics of ferromagnetic α-Fe{sub 1.64}Ga{sub 0.36}O{sub 3} oxide under magnetic fields

We report the influence of magnetic field on I-V characteristics of α-Fe{sub 1.64}Ga{sub 0.36}O{sub 3} sample. Synchrotron X-ray diffraction pattern and Raman Spectroscopy have confirmed rhombohedral structure with space group R3C in the sample. The sample exhibits ferromagnetic feature at room temperature and non saturation of magnetization up to 7Tesla suggests the effect of non-collinear structure (canting) of the spins on the ferromagnetic properties. We have recorded I-V characteristics of the sample under magnetic field to study the effect of non-collinear spin structure on the electrical properties. Space charge limited current mechanism controlled the nature of non-linear I-V curves and the curves are significantly affected by magnetic field.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2017

Zonostrophic instability driven by discrete particle noise

The consequences of discrete particle noise for a system possessing a possibly unstable collective mode are discussed. It is argued that a zonostrophic instability (of homogeneous turbulence to the formation of zonal flows) occurs just below the threshold for linear instability. The scenario provides a new interpretation of the random forcing that is ubiquitously invoked in stochastic models such as the second-order cumulant expansion or stochastic structural instability theory; neither intrinsic turbulence nor coupling to extrinsic turbulence is required. A representative calculation of the zonostrophic neutral curve is made for a simple two-field model of toroidal ion-temperature-gradient-driven modes. To the extent that the damping of zonal flows is controlled by the ion-ion collision rate, the point of zonostrophic instability is independent of that rate. Published by AIP Publishing.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2017

Experimental and numerical analysis of the performance and wake of a scale–model horizontal axis marine hydrokinetic turbine

Here, this paper presents an experimental and numerical study of a scale-model Horizontal Axis Hydrokinetic Turbine (HAHT). The model turbine is based on the U.S. Department of Energy Reference Model 1 (RM1), with the blade geometry modified to reproduce the design C p –TSR performance curve of the RM1 at the flume scale Reynolds numbers (5 × 10 4 –10 × 10 5 ). The performance and wake structure of a 45:1 scale turbine were measured using a load cell (torque applied on shaft) and a magnetic angular encoder (rotor rpm), and by planar particle image velocimetry, respectively. The details of the rotor flow field and three-dimensional wake evolution are analyzed from the numerical solution of the RANS equations solved around a computational model of the turbine. The comparison of experimental and numerical results highlights the strengths and limitations of the experimental and numerical analyses in the characterization of HAHT. Useful guidelines for developing experimental flume scale data and using them for validating numerical tools, as well as for performing a similar type of analysis and design validation of full scale devices as pilot projects start to go in the water in the United States, are provided.

13 HYDRO ENERGY↗
DOE OSTI2019

Ab initio investigation of Br-3d core-excited states in HBr and HBr + toward XUV probing of photochemical dynamics

Ultrafast X-ray/XUV transient absorption spectroscopy is a powerful tool for real-time probing of chemical dynamics. Interpretation of the transient absorption spectra requires knowledge of core-excited potentials, which necessitates assistance from high-level electronic-structure computations. In this study, we investigate Br-3d core-excited electronic structures of hydrogen bromide (HBr) using spin-orbit general multiconfigurational quasidegenerate perturbation theory (SO-GMC-QDPT). Potential energy curves and transition dipole moments are calculated from the Franck-Condon region to the asymptotic limit and used to construct core-to-valence absorption strengths for five electronic states of HBr ( 1 Σ 0 +, 3 Π 1 , 1 Π 1 , 3 Π 0 +, 3 Σ 1 ) and two electronic states of HBr+ ( 2 Π 3/2 , 2 Σ 1/2 ). The results illustrate the capabilities of Br-3d edge probing to capture transitions of the electronic-state symmetry as well as nonadiabatic dissociation processes that evolve across avoided crossings. Furthermore, core-to-valence absorption spectra are simulated from the neutral 1 Σ 0 + state and the ionic 2 Π 1/2,3/2 states by numerically solving the time-dependent Schrödinger equation and exhibit excellent agreement with the experimental spectrum. The comprehensive and quantitative picture of the core-excited states obtained in this work allows for transparent analysis of the core-to-valence absorption signals, filling gaps in the theoretical understanding of the Br-3d transient absorption spectra.

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

Effect of roughness, deterministic and random errors in film thickness on the reflecting properties of aperiodic mirrors for the EUV range

By the example of three aperiodic multilayer Mo/Si mirrors (AMM) for the wavelength ranges 17–21 nm, 24–29 nm, and 28–33 nm we have studied numerically the effect of the linearly determinisctic and random fluctuations of the film thickness and the interlayer roughness on the spectral dependences of the reflection coefficient. The simulation results are used to solve the inverse problem of reconstructing the interlayer roughness and the thickness of individual films from the measured dependences of the extreme UV radiation reflection coefficients. It is shown that the 'asymmetry' of the boundaries affects the magnitude and slope of the reflection coefficient plateau. Random fluctuations of the film thickness with the variance of 1%–2% weakly influence the reflection characteristics of AMMs and allow reliable reconstruction of the thickness of individual films. The fluctuations with the variance 8%–10% allow the estimation of individual thicknesses, but the reflection curve in this case strongly differs from the desirable one. Larger fluctuations do not allow the reconstruction of the AMM structure. The basic criteria for high-quality AMM synthesis are formulated. (extreme laser radiation: physics and fundamental applications)

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2015

THE DEGENERACY OF M33 MASS MODELING AND ITS PHYSICAL IMPLICATIONS

The Local Group galaxy M33 exhibits a regular spiral structure and is close enough to permit high resolution analysis of its kinematics, making it an ideal candidate for rotation curve studies of its inner regions. Previous studies have claimed the galaxy has a dark matter halo with an Navarro-Frenk-White profile, based on statistical comparisons with a small number of other profiles. We apply a Bayesian method from our previous paper to place the dark matter density profile in the context of a continuous, and more general, parameter space. For a wide range of initial assumptions we find that models with inner log slope γ{sub in} < 0.9 are strongly excluded by the kinematics of the galaxy unless the mass-to-light ratio of the stellar components in the 3.6 μm band satisfies Y{sub 3.6} ≥ 2. Such a high Y{sub 3.6} is inconsistent with current modeling of the stellar population of M33. This suggests that M33 is a galaxy whose dark matter halo has not been significantly modified by feedback. We discuss possible explanations of this result, including ram pressure stripping during earlier interactions with M31.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2016

A consistent and unified picture for critical phenomena of f(R) AdS black holes

A consistent and unified picture for critical phenomena of charged AdS black holes in f ( R ) gravity is drawn in this paper. Firstly, we investigate the phase transition in canonical ensemble. We derive the explicit solutions corresponding to the divergence of C {sub Q} . The two solutions merge into one when the condition Q {sub c} =√(−1/3 R {sub 0}) is satisfied. The curve of specific heat for Q < Q {sub c} has two divergent points and can be divided into three regions. Both the large radius region and the small radius region are thermodynamically stable with positive specific heat while the medium radius region is unstable with negative specific heat. However, when Q > Q {sub c} , the specific heat is always positive, implying the black holes are locally stable and no phase transition will take place. Secondly, both the T − r {sub +} curve and T − S curve f ( R ) AdS black holes are investigated and they exhibit Van der Vaals like behavior as the P − v curve in the former research. Critical physical quantities are obtained and they are consistent with those derived from the specific heat analysis. We carry out numerical check of Maxwell equal area law for the cases Q =0.2 Q {sub c} , 0.4 Q {sub c} , 0.6 Q {sub c} , 0.8 Q {sub c} . The relative errors are amazingly small and can be negligible. So the Maxwell equal area law holds for T − S curve of f ( R ) black holes. Thirdly, we establish geometrothermodynamics for f ( R ) AdS black hole to examine the phase structure. It is shown that the Legendre invariant scalar curvature R would diverge exactly where the specific heat diverges. To summarize, the above three perspectives are consistent with each other, thus providing a unified picture which deepens the understanding of critical phenomena of f ( R ) AdS black holes.

79 ASTRONOMY AND ASTROPHYSICS↗
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 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 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 OSTI2017

Nuclear Forensics and Radiochemistry: Fission

Radiochemistry has been used to study fission since it’ discovery. Radiochemical methods are used to determine cumulative mass yields. These measurements have led to the two-mode fission hypothesis to model the neutron energy dependence of fission product yields. Fission product yields can be used for the nuclear forensics of nuclear explosions. The mass yield curve depends on both the fuel and the neutron spectrum of a device. Recent studies have shown that the nuclear structure of the compound nucleus can affect the mass yield distribution.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2017

Inhomogeneities and the Modeling of Radio Supernovae

Observations of radio supernovae (SNe) often exhibit characteristics not readily accounted for by a homogeneous, spherically symmetric synchrotron model; e.g., flat-topped spectra/light curves. It is shown that many of these deviations from the standard model can be attributed to an inhomogeneous source structure. When inhomogeneities are present, the deduced radius of the source and, hence, the shock velocity, is sensitive to the details of the modeling. As the inhomogeneities are likely to result from the same mechanism that amplify the magnetic field, a comparison between observations and the detailed numerical simulations now under way may prove mutually beneficial. It is argued that the radio emission in Type Ib/c SNe has a small volume filling factor and comes from a narrow region associated with the forward shock, while the radio emission region in SN 1993J (Type IIb) is determined by the extent of the Rayleigh–Taylor instability emanating from the contact discontinuity. Attention is also drawn to the similarities between radio SNe and the structural properties of SN remnants.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2023

Multidimensional Radiation Hydrodynamics Simulations of Pulsational Pair-instability Supernovae

Stars with masses of 80–130 M ⊙ can encounter pulsational pair-instability at the end of their lives, which triggers consecutive episodes of explosive burning that eject multiple massive shells. Collisions between these shells produce bright transients known as pulsational pair-instability supernovae (PPI SNe) that may explain some extreme supernovae. In this paper, we present the first 2D and 3D radiation hydrodynamics simulations of PPI SNe with the CASTRO code. Radiative cooling causes the collided shells to evolve into thin, dense structures with hot spots that can enhance the peak luminosity of the SN by factors of 2–3. The light curve peaks at 1.9–2.1 × 10 43 erg s -1 for 50 days and then plateaus at 2–3 × 10 42 erg s -1 for 200 days, depending on the viewing angle. The presence of 12 C and 16 O and the absence of 28 Si and 56 Fe in its spectra can uniquely identify this transient as a PPI SN in follow-up observations. Our models suggest that multidimensional radiation hydrodynamics is required to model the evolution and light curves of all shell-collision SNe, such as Type IIne, not just PPI SNe.

79 ASTRONOMY AND ASTROPHYSICS↗
NASA NTRS1975

Crawler Transporter Steering and Jel Systems

The transporter weighing 2.8 million kilograms (6.3 million pounds) was used to lift a 5.7-million-kilogram (12.6-million-pound) combination of mobile launcher and space vehicle, transfer this load approximately 5.6 kilometers (3.5 miles) from its point of assembly, negotiate curves of 152-meter (500-foot) mean radius, climb a 5-percent grade while maintaining the 122-meter (400-foot) structure in a vertical position within 10 minutes of arc, and smoothly position this huge structure to within plus or minus 5.1 centimeters (plus or minus 2 inches) on support pedestals at the launch pad. The crawler-transporter is described in detail.

Davis, V. L.↗
NASA NTRS1976

Orbital changes of the gaseous ring around B sub e stars

In order to understand the seemingly erratic V/R variations and radial velocity curves of emission edges and central absorption of some Be stars, a theory is advanced to explain the changes of structure of the gaseous ring around these stars. The theory is based on the interaction between the existing ring and the newly ejected matter from the star. It shows that the structural change of the ring is completely controlled by the angular momentum input factor and the dissipation factor. In light of this understanding, the observed results of beta Mon and Pi Aqr. are interpreted.

Huang, S. S.↗
NASA NTRS1978

Mutual phenomena of Jupiter's five inner satellites in 1979

In this paper, we first predict eclipses and occultations of the Galilean satellites in 1979 and find that, although circumstances are generally poor, about 75 events are observable. We have then made a special point of including 40 eclipses of J5 (Amalthea) by the Galilean satellites in the hope that both visual and far-infrared light curves can be obtained - the former giving accurate astrometric information for J5, and the latter possible bearing on its surface structure or composition.

Aksnes, K.↗
NASA NTRS1978

A comparison of synthetic and measured solar continuum intensities and limb darkening coefficients

The paper compares recent center-limb studies of optical and IR continuum intensities (over the wavelength range 0.3-2.4 microns, with the data tabulated in the form of second- and fifth-order polynomial fits to empirical limb darkening curves) by Pierce and Slaughter (1977) and by Pierce et al. (1977) with predictions based on several current photospheric thermal structure models, using calibrated specific intensity measurements by Labs and Neckel (1968, 1970) to set an absolute temperature scale. The thermal structure in best quantitative agreement with the measurements of Pierce and Slaughter and Pierce et al. is the semiempirical model of Vernazza, Avrett and Loeser (1976). The temperatures in this model must be scaled upward, however, by a factor of 1.015 plus or minus 0.0005 to be consistent with the Labs and Neckel absolute calibration of continuum high points in the 0.40-0.65 micron region.

Ayres, T. R.↗
NASA NTRS1987

The off-axis channel macroplate

High-gain microchannel plates (MCPs) which utilize curvature of the channel to inhibit ion feedback (C-plate MCRs) have demonstrated excellent performance characteristics. However, C-plate MCPs are at present costly to fabricate, and the shearing process used to curve the channels produces a low device yield. Described here is a totally new type of high-gain MCP structure in which each channel has an axially symmetric curvature. Initial tests of proof-of-concept units of these MCPs with 75-micron-diameter channels (macroplates) suggest that their performance characteristics have the potential to be equal to those of a C-plate MCP while the fabrication process is no more complex than that of a conventional straight-channel MCP.

Timothy, J. Gethyn↗