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All sourcesNASA NTRSDOE OSTI
NASA NTRS2011

Packing Optimization of Sorbent Bed Containing Dissimilar and Irregular Shaped Media

The Fire Cartridge is a packed bed air filter with two different and separate layers of media designed to provide respiratory protection from combustion products after a fire event on the International Space Station (ISS). The first layer of media is a carbon monoxide catalyst and the second layer of media is universal carbon. During development of Fire Cartridge prototypes, the two media beds were noticed to have shifted inside the cartridge. The movement of media within the cartridge can cause mixing of the bed layers, air voids, and channeling, which could cause preferential air flow and allow contaminants to pass through without removal. An optimally packed bed mitigates these risks and ensures effective removal of contaminants from the air. In order to optimally pack each layer, vertical, horizontal, and orbital agitations were investigated and a packed bulk density was calculated for each method. Packed bulk density must be calculated for each media type to accommodate variations in particle size, shape, and density. Additionally, the optimal vibration parameters must be re-evaluated for each batch of media due to variations in particle size distribution between batches. For this application it was determined that orbital vibrations achieve an optimal pack density and the two media layers can be packed by the same method. Another finding was media with a larger size distribution of particles achieve an optimal bed pack easier than media with a smaller size distribution of particles.

Holland, Nathan↗
NASA NTRS1973

The relative density of lunar soil

The specific gravity and minimum and maximum bulk densities were determined for three one-gram lunar soil samples containing particles less than 1 mm in diameter: 14163,148; 14259,3; and 15601,82. The specific gravity varied from 2.90 to 2.93 for the Apollo 14 samples to 3.24 for the Apollo 15 sample. The difference is attributed to the higher proportion of agglutinates and breccias and fewer mineral fragments and basalts in the Apollo 14 soils. The minimum bulk densities varied from 0.87 to 1.10 g/cu cm, while the maximum densities varied from 1.51 to 1.89 g/cu cm. The ranges in values were due to differences in the specific gravity, re-entrant intragranular voids, particle shape, surface texture, and grain arrangements. The in situ lunar soil in the plains areas of the moon can have a low to medium relative density at the surface, increasing rapidly to a very high relative density at depths greater than 10 to 20 cm.

Carrier, W. D., III↗
NASA NTRS1994

The intact capture of hypervelocity dust particles using underdense foams

The impact of a hypervelocity projectile (greater than 3 km/s) is a process that subjects both the impactor and the impacted material to a large transient pressure distribution. The resultant stresses cause a large degree of fragmentation, melting, vaporization, and ionization (for normal densities). The pressure regime magnitude, however, is directly related to the density relationship between the projectile and target materials. As a consequence, a high-density impactor on a low-density target will experience the lowest level of damage. Historically, there have been three different approaches toward achieving the lowest possible target density. The first employs a projectile impinging on a foil or film of moderate density, but whose thickness is much less than the particle diameter. This results in the particle experiencing a pressure transient with both a short duration and a greatly reduced destructive effect. A succession of these films, spaced to allow nondestructive energy dissipation between impacts, will reduce the impactor's kinetic energy without allowing its internal energy to rise to the point where destruction of the projectile mass will occur. An added advantage to this method is that it yields the possibility of regions within the captured particle where a minimum of thermal modification has taken place. Polymer foams have been employed as the primary method of capturing particles with minimum degradation. The manufacture of extremely low bulk density materials is usually achieved by the introduction of voids into the material base. It must be noted, however, that a foam structure only has a true bulk density of the mixture at sizes much larger than the cell size, since for impact processes this is of paramount importance. The scale at which the bulk density must still be close to that of the mixture is approximately equal to the impactor. When this density criterion is met, shock pressures during impact are minimized, which in turn maximizes the probability of survival for the impacting particle. The primary objectives of the experiment are to (1) Examine the morphology of primary and secondary hypervelocity impact craters. Primary attention will be paid to craters caused by ejecta during hypervelocity impacts of different substrates. (2) Determine the size distribution of ejecta by means of witness plates and collect fragments of ejecta from craters by means of momentum-sensitive mcropore foam. (3) Assess the directionality of the flux by means of penetration-hole alignment of thin films placed above the cells. (4) Capture intact the particles that perforated the thin film and entered the cell. Capture media consisted of both previously flight-tested micropore foams and aerogel. The foams had different latent heats of fusion and, accordingly, will capture particles over a range of momenta. Aerogel was incorporated into the cells to determine the minimum diameter than can be captured intact.

Maag, Carl R.↗
DOE OSTI2026

Characterization of ceramic fuel powder packing fractions to support INFLUX

Triply periodic minimal surface (TPMS)-based structures show marked potential in novel nuclear reactor fuel designs, as their high surface area-to-volume ratio increases the efficiency of heat transfer out of the fuel, enabling safer, more innovative reactor designs. This milestone report addresses the role of dUO 2 powder processing route on the fill behavior of TPMS-based cladding shells to understand and advance the feasibility of manufacturing TPMS-based nuclear fuel forms. dUO 2 powder was processed through either a dry granulation route, varying consolidation pressure, or through milling, varying milling time, milling method and milled size distribution. The lowest tapped bulk densities (TBD), but best powder flowabilities, were obtained when testing unprocessed dUO 2 powder which was prone to self-agglomeration and formed low-density spheroids. The highest TBD and lowest flowabilities were obtained when using powder produced by hammer-milling dUO 2 powder to pass through a 200-mesh sieve, which led to particles with angular morphologies. Powder produced by dry granulation exhibited TBD that varied according to the consolidation pressure used to form the initial pellets and exhibited improved flowabilities when compared to hammer-milled material. Because of the large span of granule sizes formed as well as the irregular shape associated with the granules, a packing fraction of 0.69 was achieved, exceeding the analytical solution for random close packing of mono-sized spheres. TPMS polymer shells were loaded with unprocessed, granulated, and hammer-milled dUO 2 powders, and their qualitative packing behaviors were analyzed using x-ray computed tomography (xCT). TBDs calculated after loading TPMS polymer shells were 10-20% lower when compared to tapped bulk density measurements taken in a glass graduated cylinder, indicating a non-trivial impact on the tapped bulk density of either the TPMS channel size, TPMS channel surface material, powder cohesiveness, or a combination of the two parameters. A metallic zircaloy-4 TPMS shell will be loaded with hammer-milled dUO 2 powder upon receipt of the shell from Oak Ridge National Laboratory (ORNL) and shipped to Idaho National Labs (INL) for subsequent hot isostatic pressing (HIP) densification experiments.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
NASA NTRS1972

Bearing strength of the lunar soil

A discussion is presented on the bearing strength and bearing load-penetration relations in lunar soil. These were measured in air as a function of bulk density. It was found that the relation between bulk density and the logarithm of the bearing capacity is about linear. Shapes of the load vs penetration curves were observed to be similar to those obtained with particulate material of terrestrial origin.

Jaffe, L. D.↗
NASA NTRS1984

A reexamination of soil textural effects on microwave emission and backscattering

Microwave frequency measurements of moist soil dielectric properties are noted to challenge the validity of percent-of-field-capacity as a moisture indicator that is independent of soil texture in terms of microwave sensitivity. In arriving at this view, gravimetric, volumetric, and percent-of-field-capacity were tested for their ability to reduce dielectric behavior divergence between soil textures at 1.4 and 5.0 GHz. The most congruent dielectric behavior between soil textures is found to occur when soil moisture is expressed on a volumetric basis that is proportional to the number of water dipoles/unit volume. An inadequate characterization of soil bulk density in the field, combined with the dependency of bulk density on water retention at field capacity, offers the most plausible explanation for the earlier conclusions.

Dobson, M. C.↗
NASA NTRS1988

Microwave dielectric spectrum of rocks

A combination of several measurement techniques was used to investigate the dielectric properties of 80 rock samples in the microwave region. The real part of the dielectric constant, epsilon', was measured in 0.1 GHz steps from 0.5 to 18 GHz, and the imaginary part, epsilon'', was measured at five frequencies extending between 1.6 and 16 GHz. In addition to the dielectric measurements, the bulk density was measured for all the samples and the bulk chemical composition was determined for 56 of the samples. The study shows that epsilon' is frequency-dependent over the 0.5 to 18 GHz range for all rock samples, and that the bulk density rho accounts for about 50 percent of the observed variance of epsilon'. For individual rock types (by genesis), about 90 percent of the observed variance may be explained by the combination of density and the fractional contents of SiO2, Fe2O3, MgO, and TiO2. For the loss factor epsilon'', it was not possible to establish statistically significant relationships between it and the measured properties of the rock samples (density and chemical composition).

Ulaby, F. T.↗
NASA NTRS1985

Effects of soil tillage on the microwave emission of soils

In order to understand the interactions of soil properties and microwave emission better, a series of field experiments were conducted in 1984. Small plots were measured with a truck-mounted passive microwave radiometer operating at 1.4 GHz. These data were collected concurrent with ground observations of soil moisture and bulk density. Treatment effects studied included different soil moisture contents and bulk densities. Evaluations of the data have shown that commonly used models of the dielectric properties of wet soils do not explain the observations obtained in these experiments. This conclusion was based on the fact that the roughness parameters determined through optimization were significantly larger than those observed in similar investigations. These discrepancies are most likely due to the soil structure. Commonly used models assume a homogeneous three phase mixture of soil solids, air and water. Under tilled conditions the soil is actually a two phase mixture of aggregates and voids. Appropriate dielectric models for this tilled condition were evaluated and found to explain the observations. These results indicate that previous conclusions concerning the effects of surface roughness in tilled fields may be incorrect, and they may explain some of the inconsistencies encountered in roughness modeling.

Jackson, T. J.↗
NASA NTRS1988

Dynamic porosity variations in ceramics

A silicon carbide disk was sintered from 2090 to 2190 C in 25 C steps. After each sintering step the disk was examined using a precision acoustic scanning system to determine acoustic attenuation and velocity. The bulk density was found to vary nonmonotonically with sintering temperature. During the sintering process, the density varied as much as 10 pct from its value at 2090 C. Local density fluctuations were observed to occur in an organized and history-dependent way. These local density fluctuations varied up to + or - 7 pct of the bulk density and were made visible by acoustic attenuation and velocity mapping.

Generazio, Edward R.↗
NASA NTRS1989

Ultrasonic imaging of porosity variations produced during sintering

A silicon carbide disk was sintered from 2090 to 2190 C in 25-C steps. After each sintering step, the disk was examined using a precision acoustic scanning system to determine acoustic attenuation and velocity. The bulk density was found to vary nonmonotonically with sintering temperature. The density varied as much as 10 percent from its value at 2090 C during the sintering process. Local density fluctuations occurred in an organized and history-dependent way. These local density fluctuations varied up to + or - 7 percent of the bulk density and were made visible by acoustic attenuation and velocity mapping.

Generazio, Edward R.↗
DOE OSTI2024

Absence of bulk charge density wave order in the normal state of UTe 2

A spatially modulated superconducting state, known as pair density wave (PDW), is a tantalizing state of matter with unique properties. Recent scanning tunneling microscopy (STM) studies revealed that spin-triplet superconductor UTe 2 hosts an unprecedented spin-triplet, multi-component PDW whose three wavevectors are indistinguishable from a preceding charge-density wave (CDW) order that survives to temperatures well above the superconducting critical temperature, T c . Whether the PDW is the mother or a subordinate order remains unsettled. Here, based on a systematic search for bulk charge order above T c using resonant elastic X-ray scattering (REXS), we show that the structure factor of charge order previously identified by STM is absent in the bulk within the sensitivity of REXS. Our results invite two scenarios: either the density-wave orders condense simultaneously at T c in the bulk, in which case PDW order is likely the mother phase, or the charge modulations are restricted to the surface.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
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↗
NASA NTRSDate not supplied

Parameters Impacting Columnated Granular Soil Pneumatic Seal Performance

The ability of a column of loose granular soil to form a pneumatic seal was investigated by varying the diameter of the soil column, the effective column height, and the level of compaction in the soil. The soil column diameter was tested at three levels using pipes with inner diameters measuring 5.08, 10.16, and 15.24 centimeters (2, 4, and 6 inches). Soil was filled in each pipe to form 15, 30, and 45 centimeter (6, 12, and 18 inch) tall soil columns. Each diameter/height configuration was also tested at three levels of soil compaction, compared by calculating the bulk density of the soil with mass and volume measurements. GRC-1a simulant was used, with approximate low/medium/high bulk densities of 1.6, 1.75, and 1.9 g/cc achieved with a combination of vibration and tamping. The order of tests for a given column diameter was randomized and repeated three times. With the top of the soil open to atmosphere at room temperature, compressed air was injected through a small diffuser at the column base with several small downward-facing holes. The number and size of these holes was scaled such that a constant total inlet orifice area to column cross sectional area ratio was maintained for each column diameter. Inlet air pressure was slowly increased via a precision regulator to preserve quasi-static equilibrium in the soil column to minimize the impact of dynamics. Air pressure was increased all the way through the static and bubbling regimes until slugging or turbulent behavior was observed in the soil to ensure that the entire static regime had been captured during data collection. A three-factor, three-level analysis of variance (ANOVA) statistical analysis was performed on the resulting data to determine the extent to which each physical parameter impacted the soil column seal performance. It was concluded that there is statistically significant evidence that column height, and the interaction between column height and diameter impact soil seal performance. In all other cases there was insufficient data to identify a statistically significant causal relationship. Additionally, plots were generated comparing experimental data to the predictive formula developed by Ogino et al. for fluidized beds in 1993. Because this model was developed for industrial spouted fluidized beds, the accuracy of its output prior to fluidization in the static seal ‘edge case’ is unknown, especially considering in this application the working gas was diffused across the column base rather than being injected through a spout. Further, the fidelity of the model had not yet been tested with lunar soil simulants. Plotting the Ogino et al. model alongside test data allows for a more intuitive sense of the impact of test parameters on soil seal performance, as well as providing a quick means to further tune this predictive model for more accurate use with static seals across granular lunar soil simulants. The model provided by Ogino et al. was further tuned using test data to determine the degree to which a spouted bed model could be applied to a slightly modified set of testing conditions: lunar soil simulants and a more diffuse, homogeneous application of pneumatic pressure. A Matlab script was created to test different values for the leading coefficient and exponents in Ogino’s formula. The script swept preset ranges, then iterated with higher resolutions across narrower ranges to converge on the optimal value for each parameter. The resulting adjusted model was compared to the original, as well as test data with noticeable improvements across the entire test domain.

Jack Stewart↗
NASA NTRS1976

The densities and bulk compositions of Ceres and Vesta

Present radiometric and polarimetric measurements of the sizes and albedos of asteroids 1 Ceres and 4 Vesta, although uncertain in absolute calibration, determine the ratio of diameters with sufficient precision (Ceres/Vesta = 1.87 plus or minus 0.03) to permit meaningful discussion of their relative densities. These measurements combined with the known masses yield a ratio of densities Vesta/Ceres = 1.33 plus or minus 0.17, thus demonstrating that: (1) the bulk compositions are likely different, and (2) the difference is consistent with indications from remotely-determined surface composition that Vesta is differentiated while Ceres may be of undifferentiated carbonaceous chondritic composition.

Morrison, D.↗
NASA NTRS1987

High frequency ultrasonic characterization of sintered SiC

High frequency (60 to 160 MHz) ultrasonic nondestructive evaluation was used to characterize variations in density and microstructural constituents of sintered SiC bars. Ultrasonic characterization methods included longitudinal velocity, reflection coefficient, and precise attenuation measurements. The SiC bars were tailored to provide bulk densities ranging from 90 to 98 percent of theoretical, average grain sizes ranging from 3.0 to 12.0 microns, and average pore sizes ranging from 1.5 to 4.0 microns. Velocity correlated with specimen bulk density irrespective of specimen average grain size, average pore size, and average pore orientation. Attenuation coefficient was found to be sensitive to both density and average pore size variations, but was not affected by large differences in average grain size.

Baaklini, George Y.↗
NASA NTRS1989

High-frequency ultrasonic characterization of sintered silicon carbide

High-frequency 60- to 160-MHz ultrasonic nondestructive evaluation was used to characterize variations in density and microstructural constituents of sintered SiC bars. Ultrasonic characterization methods included longitudinal velocity, reflection coefficient, and precise attenuation measurements. The SiC bars were tailored to provide bulk densities ranging from 90 to 98 percent of theoretical, average grain sizes ranging from 3.0 to 12.0 microns, and average pore sizes ranging from 1.5 to 4.0 microns. Velocity correlated with specimen bulk density irrespective of specimen average grain size, average pore size, and average pore orientation. The attenuation coefficient was found to be sensitive to both density and average pore size variations, but was not affected by large differences in average grain size.

Baaklini, George Y.↗
NASA NTRS2014

Characteristics of the 2012 Geminids

The parent of the Geminids, 3200 Phaethon, is a unique body in that it is classified as an asteroid, however is responsible for one of the most prolific meteor showers of the year and has shown comet-like behavior in its past (Jewitt and Li 2010). The Geminid meteor shower is also anomalous as its rates have been increasing since it was first detected. Understanding the composition and properties of meteoroids that belong to this meteor shower is an important area of study and of interest to both theoreticians and experimentalists. Using the light curve and decelerations of ten double-station Geminids as seen in the Meteoroid Environment Office's widefield meteor cameras, densities were able to be approximated using a model of meteoroid ablation by Campbell-Brown et al (2013) which employs thermal disruption to model the release of grains during ablation. Bulk densities of Geminids give unique insight into the composition of Phaethon that would only be derived by going to the asteroid itself. The bulk densities of these ten Geminids were found to be between 2.6 and 3.0 g/cm(3), supporting results from Babadzhanov (2009) and Borovicka et al (2010) which prove Phaethon has a much lower porosity than most other meteor shower parents. NASA's Meteoroid Environment Office established these two wide-field meteor cameras to observe meteors in the milligram-mass-range. Each camera consists of a 17 mm focal length Schneider lens (f/0.95) on a Watec 902U2 Ultimate CCD video camera, producing a 21.7x15.5 degree field-of-view. This configuration sees meteors down to a magnitude of +6. Data from these cameras are currently being used to calculate daily automated meteor fluxes. On the first night of operation, December 13-14, 2012, 18 double-station and 53 unique single-station Geminids were detected. The Geminid flux results from this system will be presented as well as ZHR's over the peak of the Geminids. The average flux density over the night was 0.058, 0.052, and 0.062 meteors/km(2)/hour down to a limiting magnitude of +6.5, for the double-station results and each single-station's results. This equates to ZHR's of 113, 102, and 122 respectively. Included in the flux algorithm is a process to find the collecting area per height and a method to find the limiting meteor magnitude per 10 minute time period.

Blaauw, R.↗