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All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

ORGANIC RANKINE CYCLE TURBINE AND HEAT EXCHANGER SIZING FOR LIQUID AIR COMBINED CYCLE

Cryogenic energy storage offers several opportunities to design turbomachinery and other equipment for novel cycles. This paper presents the design and analysis of turbomachinery and heat exchangers for an Organic Rankine Cycle (ORC) subsystem for a hybrid energy storage concept. The Liquid Air Combined Cycle is an energy storage system that stores air at cryogenic conditions at times with high variable renewable energy to be dispatched along with a gas turbine to recover the exhaust heat. In order to re-vaporize the air, the liquid air is coupled with an ORC as an additional bottoming cycle. The ORC turbine is expected to expand the fluid with a pressure ratio of nearly 30 and a flow rate of approximately 45 kg/s. Sizing calculations for both a radial and axial turbine solution were performed over a range of speeds and stages to determine the optimal design point. The results show that either an axial (8- or 9-stage) or radial (four stages at two shaft speeds) turbine are capable of handling the pressure ratios. Further trades of the two configurations would be required to determine the best option. The ORC system also incorporates five heat exchangers to distribute heat, vaporize the liquid air, or recover exhaust heat from the gas turbine. Three heat exchangers were analyzed to understand the size of heat exchangers and pressure drop for the overall system. Different types of heat exchangers were explored for the different purposes, including plate-fin heat exchangers, gasketed plate heat exchangers and shell-in-tube heat exchangers. It was determined that the ORC recuperator, liquid-air vaporizer, and vaporized air pre-heater would be counter-flow heat exchangers using a gasketed plate design. Keywords: Energy Storage, Liquid Air Energy Storage, Organic Rankine Cycle

Pryor, Owen↗
DOE OSTI2021

Towards replacing physical testing of granular materials with a Topology-based Model

In the study of packed granular materials, the performance of a sample (e.g., the detonation of a high-energy explosive) often correlates to measurements of a fluid flowing through it. The “effective surface area,” the surface area accessible to the airflow, is typically measured using a permeametry apparatus that relates the flow conductance to the permeable surface area via the Carman-Kozeny equation. This equation allows calculating the flow rate of a fluid flowing through the granules packed in the sample for a given pressure drop. However, Carman-Kozeny makes inherent assumptions about tunnel shapes and flow paths that may not accurately hold in situations where the particles possess a wide distribution in shapes, sizes, and aspect ratios, as is true with many powdered systems of technological and commercial interest. To address this challenge, we replicate these measurements virtually on micro-CT images of the powdered material, introducing a new Pore Network Model based on the skeleton of the Morse-Smale complex. Pores are identified as basins of the complex, their incidence encodes adjacency, and the conductivity of the capillary between them is computed from the cross-section at their interface. We build and solve a resistive network to compute an approximate laminar fluid flow through the pore structure. Here, we provide two means of estimating flow-permeable surface area: (i) by direct computation of conductivity, and (ii) by identifying dead-ends in the flow coupled with isosurface extraction and the application of the Carman-Kozeny equation, with the aim of establishing consistency over a range of particle shapes, sizes, porosity levels, and void distribution patterns.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Fog particle concentrations

ngVIPS metadata and caveats: Column 1: Time, seconds after midnight UTC of beginning of time period. (60 sec values) Column 2: Relative concentration. Notes: Concentration values are observed values and assume ~4.0 m/s wind speed. (#/cm^3) Caveats: Observed particles with a very few exceptions were on the order of a few pixels (less than 10 microns). The size differential was insufficient to determine a particle size distribution. As the temperatures were relatively warm (and it can be 10C warmer inside the instrument) it is anticipated that some solid ice particles could have melted and some smaller liquid drops could have evaporated. A cyclic nature to the high resolution data was observed and determined to be caused by a focus issue. Each 60 second period included ~25 images. Particles are collected on a glass disk, but the disk isn't perfectly centered causing small particles to be slightly out of focus during part of the rotation. For that reason, we chose to report the average of the five highest values in a time period rather the average of all values. Higher resolution can be made available upon request, but a conversation with the PI would be important to fully understand the planned usage and if the higher resolution data were to be useful for the task. Please contact instrument PI, Carl Schmitt (naturalsystemsresearch@gmail.com) with any questions about these data.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

High-Energy X-ray Diffraction Microscopy for Nuclear Forensics FY2022 Project Report

Morphological information on nuclear material has been identified using visible light and scanning electron microscopy. These identify qualitative differences in particle morphology. Three-dimensional imaging of materials through alternating scanning electron microscopy imaging and focused ion beam milling has also been used. Unfortunately, these techniques are time- and labor-intensive, with significant sample preparation required and lengthy analysis times. Further, the resulting 3D images are qualitative, require manual identification, and do not capture statistically-representative populations. High energy X-ray 3D imaging using a direct-beam or diffracted-beam (High-Energy Diffraction Microscopy) have been developed at the Advanced Photon Source and can produce quantitative information on grains (phase, location, etc.) and pores (size distribution, sphericity) in a material. These techniques require only minutes to characterize a sample volume and are non-destructive, thus suitable for a wide range of existing samples and for confirmatory analyses to be carried out using conventional microscopy techniques. In this first year of the project, all uranium oxide samples were synthesized and characterized using conventional analyses by the analytical chemistry laboratory. Conventional analysis methods included powder x-ray diffraction, scanning electron microscopy, impurity analysis via inductively coupled plasma mass spectrometry, and infrared spectroscopy. Impurity analysis shows a drop in boron content from UO 3 to the lowest U 3 O 8 calcination temperature, but otherwise no appreciable difference in any sample. Analysis of diffraction data shows a flip of peaks from UO 3 dominated for the 600 °C calcined sample to U 3 O 8 dominated at 700 °C and 800 °C. Analysis of scanning electron microscopy images shows that with increased calcination temperature the size distribution of particles seems to increase and broaden. Both of these last findings are in line with previously published data, though this work used significantly fewer particles to simply show similar trends instead of getting truly quantitative particle analysis. Infrared analysis similarly shows ingrowth of U 3 O 8 as calcination temperature is increased, along with depression of peaks associated with UO 3 and water. Samples were prepared for analysis at the Advanced Photon Source at beamline 1-ID. It is anticipated that analysis will occur in November of 2022. AI/ML techniques to de-noise data coming out of 1-ID during the analyses was also developed during this time using previously gathered data. Preliminary results using a self-supervision technique called Noise2Selfshow good de-noising of data. Once the uranium oxide samples are analyzed, real data will be used to test the de-noising and other AI/ML techniques that may be developed in the second year of the project.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2024

Insights into the broadband emission of the TeV blazar Mrk 501 during the first X-ray polarization measurements

Aims.We present the first multiwavelength study of Mrk 501 that contains simultaneous very-high-energy (VHE)γ-ray observations and X-ray polarization measurements from the Imaging X-ray Polarimetry Explorer (IXPE). Methods.We used radio-to-VHE data from a multiwavelength campaign carried out between March 1, 2022, and July 19, 2022 (MJD 59639 to MJD 59779). The observations were performed by MAGIC,Fermi-LAT,NuSTAR,Swift(XRT and UVOT), and several other instruments that cover the optical and radio bands to complement the IXPE pointings. We characterized the dynamics of the broadband emission around the X-ray polarization measurements through its multiband fractional variability and correlations, and compared changes observed in the polarization degree to changes seen in the broadband emission using a multi-zone leptonic scenario. Results.During the IXPE pointings, the VHE state is close to the average behavior, with a 0.2–1 TeV flux of 20%–50% of the emission of the Crab Nebula. Additionally, it shows low variability and a hint of correlation between VHEγ-rays and X-rays. Despite the average VHE activity, an extreme X-ray behavior is measured for the first two IXPE pointings, taken in March 2022 (MJD 59646 to 59648 and MJD 59665 to 59667), with a synchrotron peak frequency > 1 keV. For the third IXPE pointing, in July 2022 (MJD 59769 to 59772), the synchrotron peak shifts toward lower energies and the optical/X-ray polarization degrees drop. All three IXPE epochs show an atypically low Compton dominance in theγ-rays. The X-ray polarization is systematically higher than at lower energies, suggesting an energy stratification of the jet. While during the IXPE epochs the polarization angles in the X-ray, optical, and radio bands align well, we find a clear discrepancy in the optical and radio polarization angles in the middle of the campaign. Such results further support the hypothesis of an energy-stratified jet. We modeled broadband spectra taken simultaneous to the IXPE pointings, assuming a compact zone that dominates in the X-rays and the VHE band, and an extended zone stretching farther downstream in the jet that dominates the emission at lower energies.NuSTARdata allow us to precisely constrain the synchrotron peak and therefore the underlying electron distribution. The change between the different states observed in the three IXPE pointings can be explained by a change in the magnetization and/or the emission region size, which directly connects the shift in the synchrotron peak to lower energies with the drop in the polarization degree.

Astronomy & Astrophysics↗
DOE OSTI2020

Detection Limits and Near‐Field Ground Motions of Fast and Slow Earthquakes

Abstract We investigate theoretical limits to detection of fast and slow seismic events, and spatial variations of ground motion expected from M 6 earthquakes at short epicentral distances. The analyses are based on synthetic velocity seismograms calculated with the discrete wavenumber method assuming seismic velocities and attenuation properties of the crust in Southern California. The examined source properties include different magnitudes ( M −1.0 to M 6.0), static stress drops (0.1–10 MPa), and slow and fast ruptures (0.1–0.9 of shear wave velocity). For the M 6 events we also consider variations in rise times producing crack‐ and pulse‐type events and different rupture directivities. Slow events produce ground motion with considerably lower amplitude than corresponding regular fast earthquakes with the same magnitude, and hence are significantly more difficult to detect. The static stress drop and slip rise time also affect the maximum radiated seismic motion, and hence event detectability. Apart from geometrical factors, the saturation and depletion of seismic ground motion at short epicentral distances stem from radiation pattern, earthquake size (magnitude, stress drop), and rupture directivity. The rupture velocity, rise time, and directivity affect significantly the spatial pattern of the ground motions. The results can help optimizing detection of slow and fast small earthquakes and understand the spatial distribution of ground motion generated by large events.

58 GEOSCIENCES↗
DOE OSTI2022

Toward Better Understanding of Microphysical Processes and Resulting Precipitation Physics: A Merger Of Observations and Cloud Models (Final Report)

A large database of global disdrometer observations and a diverse set of simulations from the Regional Atmospheric Modeling System (RAMS) were compared using principal component analysis (PCA) in order to better understand warm and ice-based precipitation processes. The analysis demonstrated that six distinct precipitation groups (PGs) with common characteristics were revealed in both the observations and model simulations. These PGs were defined by similar co-variability of rain parameters. However, the model showed large concentrations of drops with a given size compared to the observations. Detailed investigations determined the parameterization of drop breakup was forcing the drops to an equilibrium size with a stronger constraint than was indicated by the observations. A series of sensitivity studies permutating the rain shape parameter in the RAMS 2-moment bulk scheme demonstrated how the assumed shape of the rain distribution influences the microphysics as well as precipitation characteristics. Results were compared to the same simulations performed with a bin microphysics scheme where the rain shape parameter is freely evolving. In the bin scheme, a wide range of shape parameters were found, with horizontal and vertical variability, in addition to being a function of storm lifetime. The results of this study highlight the limitations of a fixed assumed rain distribution in 2-moment microphysics schemes. Model simulations were used to probe the microphysical origins of the six PGs. Rain budgets from a supercell case showed that several of the groups hypothesized to be associated with strong ice microphysical processes had significant contributions from melting hail, but the complexity of ice and warm-rain processes in the supercell precluded definitive determination of microphysical origins for many of the PGs. A case study from the Mid-latitude Continental Convection and Clouds Experiment (MC3E) compared the model PGs to disdrometer observations, and demonstrated the spatial and temporal variability of the PGs which is not possible from disdrometer point measurements alone. The analyses performed during this project demonstrated how statistical analysis can provide a robust method for comparing model simulations and observations, which ultimately resulted identification of limitations in some current microphysics parameterizations as well as insights into precipitation variability which ultimately benefits both observations and modeling efforts.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

Compact galaxies and the size–mass galaxy distribution from a colour-selected sample at 0.04 < z < 0.15 supplemented by ugrizYJHK photometric redshifts

ABSTRACT The size–mass galaxy distribution is a key diagnostic for galaxy evolution. Massive compact galaxies are potential surviving relics of a high-redshift phase of star formation. Some of these could be nearly unresolved in Sloan Digital Sky Survey (SDSS) imaging and thus not included in galaxy samples. To overcome this, a sample was selected from the combination of SDSS and UKIRT Infrared Deep Sky Survey (UKIDSS) photometry to r < 17.8. This was done using colour–colour selection, and then by obtaining accurate photometric redshifts (photo-z) using scaled flux matching (SFM). Compared to spectroscopic redshifts (spec-z), SFM obtained a 1σ scatter of 0.0125 with only 0.3 per cent outliers (|Δln (1 + z)| > 0.06). A sample of 163 186 galaxies was obtained with 0.04 < z < 0.15 over $2300\, {\rm deg}^2$ using a combination of spec-z and photo-z. Following Barro et al. log Σ1.5 = log M* − 1.5log r50, maj was used to define compactness. The spectroscopic completeness was 76 per cent for compact galaxies (log Σ1.5 > 10.5) compared to 92 per cent for normal-sized galaxies. This difference is primarily attributed to SDSS ‘fibre collisions’ and not the completeness of the main galaxy sample selection. Using environmental overdensities, this confirms that compact quiescent galaxies are significantly more likely to be found in high-density environments compared to normal-sized galaxies. By comparison with a high-redshift sample from 3D-HST, log Σ1.5 distribution functions show significant evolution, with this being a compelling way to compare with simulations such as EAGLE. The number density of compact quiescent galaxies drops by a factor of about 30 from z ∼ 2 to log (n/Mpc−3) = − 5.3 ± 0.4 in the SDSS–UKIDSS sample. The uncertainty is dominated by the steep cut off in log Σ1.5, which is demonstrated conclusively using this complete sample.

Baldry, Ivan K.↗
DOE OSTI2020

Geomancy: Automated Performance Enhancement through Data Layout Optimization

Large distributed storage systems such as high- performance computing (HPC) systems used by national or international laboratories require sufficient performance and scale for demanding scientific workloads and must handle shifting workloads with ease. Ideally, data is placed in locations to optimize performance, but the size and complexity of large storage systems inhibit rapid effective restructuring of data layouts to maintain performance as workloads shift. To address these issues, we have developed Geomancy, a tool that models the placement of data within a distributed storage system and reacts to drops in performance. Using a combination of machine learning techniques suitable for temporal modeling, Geomancy determines when and where a bottleneck may happen due to changing workloads and suggests changes in the layout that mitigate or prevent them. Our approach to optimizing throughput offers benefits for storage systems such as avoiding potential bottlenecks and increasing overall I/O throughput from 11% to 30%.

Bel, Oceane M.↗
DOE OSTI2020

High Speed In-situ X-ray Imaging of 3D Freeze Printing of Aerogels

3D freeze printing (3DFP) combines drop-on-demand (DOD) inkjet printing with freeze casting to fabricate lightweight and multifunctional aerogels with customized geometries. Freeze casting is an efficient and easily implemented method capable of fabricating porous, sponge-like structures for many different applications. This process enables tailoring the microstructure of the final product (i.e., pore morphology, alignment, average size distribution, etc.) by controlling the fabrication conditions and freezing kinetics. Furthermore, its combination with DOD printing provides the capability of engineering the macrostructure without relying on a mold as reported for 3D freeze-printed aerogels made from graphene, silver nanowires, and other nanocomposites. In this paper, we performed in-situ X-ray imaging to understand the inside process dynamics in 3DFP using a commercially available colloidal silica ink. We investigated the 3DFP process with the following hierarchy: first, single droplets; then, uniform lines obtained from coalescence of droplets; and finally, three consecutive lines deposited layer by layer. With the help of X-ray imaging, the importance of the balance between material deposition and freezing rates was shown in-situ by the observation inside of the freeze front following the tip of the printed line. The effects of the substrate temperature on the elimination of undesired interfacial boundaries were also shown by the observed ice crystals penetrating from lower to upper layer.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Experimental study of sprays with isooctane, hexane, ethanol and their binary mixtures under different flash boiling intensities

Here, this work investigated the behaviors of single-component and multi-component sprays under different flash boiling intensities. Isooctane, hexane, ethanol, isooctane-hexane, and isooctane-ethanol mixtures were used as test fuels. The pressure ratio of the ambient pressure over saturation pressure (R p ) was used as the indicator of the superheated degree. Macroscopic characteristics of the sprays under 10 different R p (1.1 to 0.14) were investigated using Diffused Backlight Imaging (DBI). Droplet size, which revealed the spray microscopic characteristics, was also investigated using Phase Doppler Anemometry (PDA). The results showed that ambient pressure reduction led to higher flash boiling intensity, significant spray morphology changes, and smaller droplet size, regardless of the fuel type and blend ratios. Spray collapse was observed for all sprays once R p dropped below 0.3. Spray collapse dramatically increased the liquid penetration length, expanded the near-field spray angle, and reduced the far-field angle for sprays with all the fuels. Fuel properties, such as the latent heat of vaporization, had significant impacts on droplet size distributions, thus altering the liquid penetration length and spray angles. Under similar R p , sprays with high ethanol content generally had larger droplet sizes compared with other fuel sprays due to the high latent heat of vaporization. The larger droplets caused the high ethanol content sprays to be more resistant to the plume trajectory change, resulting in a smaller far-field spray angle variations with changing R p . At R p of 0.28, sprays with the similar latent heat of vaporization have similar droplet sizes, and droplet sizes at different radial locations were also similar to each other.

42 ENGINEERING↗
DOE OSTI2023

What Are the Similarities and Differences in Marine Boundary Layer Cloud and Drizzle Microphysical Properties During the ACE–ENA and MARCUS Field Campaigns?

This study compares macrophysical and microphysical properties of single-layered, liquid-dominant MBL clouds from the Measurements of Aerosols, Radiation, and Clouds over the Southern Ocean (MARCUS) (above 60°S) and the ARM East North Atlantic (ENA) site during the Aerosol and Cloud Experiments in Eastern North Atlantic (ACE-ENA) field campaign. A total of 1,136 (16.5% of clouds) and 6,034 5-min cloud samples are selected from MARCUS and ARM ENA in this study. MARCUS clouds have higher cloud-top heights, thicker cloud layers, larger liquid water path, and colder cloud temperatures than ENA. Thinner, warmer MBL clouds at ENA can contain higher layer-mean liquid water content due to higher cloud and ocean surface temperatures along with greater precipitable water vapor (PWV). MARCUS has a higher drizzle frequency rate (71.8%) than ENA (45.1%). Retrieved cloud and drizzle microphysical properties from each field campaign show key differences. MARCUS clouds feature smaller cloud droplets, whereas ENA clouds have larger cloud droplets, especially at the upper region of the cloud. From cloud top to cloud base, drizzle drop sizes increase while number concentrations decrease. Drizzle drop radius and number concentration decrease from cloud base to drizzle base due to net evaporation, and MARCUS' lower specific humidity leads to a higher drizzle base than ENA. In conclusion, the broader surface pressure and lower tropospheric stability (LTS) distributions during MARCUS have demonstrated that there are different synoptic patterns for selected cases during MARCUS with less PWV, while ENA is dominated by high pressure systems with nearly doubled PWV.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Evaluation of thermal processing and properties of 422 martensitic stainless steel for replacement of 4140 steel in diesel engine pistons

The thermal and mechanical properties of martensitic stainless steel 422 were evaluated for suitability as a drop-in replacement for 4140 steel in next generation heavy-duty diesel engine (HDDE) pistons. The time and temperature of the austenitization and tempering steps were studied to achieve optimum materials performance in piston applications, including the balance of thermal and mechanical properties and resistance to long-term thermal aging. Reducing the tempering temperature from 700 to 600 °C caused a substantial increase in elevated temperature strength from 25 to 600 °C, but had no significant influence on thermal conductivity, suggesting that thermal conductivity in 422 is dominated largely by composition and distribution of alloying elements and mostly independent of the sub-grain structure size and precipitate size. Compared to the current HDDE piston alloy 4140, 422 exhibits substantially higher elevated temperature strength and lower thermal conductivity, the latter which will cause 422 to operate at higher temperatures in pistons, possibly requiring a piston redesign to take advantage of the improved high temperature strength of 422. Piston material selection and alloy design strategies with potential to mitigate some of the shortcomings of martensitic stainless steels, such as 422, as drop-in replacements are discussed.

36 MATERIALS SCIENCE↗
DOE OSTI2020

The Role of Ice Splintering on Microphysics of Deep Convective Clouds Forming Under Different Aerosol Conditions: Simulations Using the Model With Spectral Bin Microphysics

Observations during the Ice in Clouds Experiment-Tropical (ICE-T) field experiment show that the ice particles concentration in a developing deep convective clouds at the level of T = -15 °C reached about 500 L -1 , that is, many orders higher than that of ice-nucleating particle. To simulate microphysics of these clouds, the 2-D Hebrew University Cloud model (HUCM) with spectral bin microphysics was used in which two main types of ice multiplication mechanisms were included in addition to the Hallet-Mossop mechanism. In the first ice multiplication mechanism ice splinters form by drop freezing and drop-ice collisions. Ice multiplication of this type dominates during developing stage of cloud evolution, when liquid water content is significant. At later stage when clouds become nearly glaciated, ice crystals are produced largely by ice splintering during ice-ice collisions (the second ice multiplication mechanism). Simulations show that droplet size distributions, as well as size distributions of ice particles, agree well with the measurements during ICE-T. Simulations with different cloud condensation nuclei concentrations show the existence of the “optimum” cloud condensation nuclei concentration (or droplet concentration), at which concentration of ice splinters reaches maximum. In these simulations ice nucleation caused by the direct formation of ice crystals upon ice-nucleating particles, as well as the Hallett-Mossop process, has a negligible contribution to the ice crystal concentration.

54 ENVIRONMENTAL SCIENCES↗
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↗