Search NASA⌕ Search

SEARCH · Search NASA

Results for “task 4”

Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 109 records · Page 6
All sourcesNASA NTRSDOE OSTI
NASA NTRS2021

Planetary and Lunar Environment Thermal Toolbox Elements (PALETTE) Project Year One Results

This paper summarizes the technology development progress made during year one of the three-year JPL PALETTE project, which is funded by the NASA STMD Game Changing Development (GCD) Program. The project goal is to ensure that a full “palette” of flight-ready (high TRL) thermal “toolbox” elements is available so that engineers can create passive, ultra-isolative thermal designs for science instruments on a variety of carriers in lunar/planetary extreme environments. PALETTE is structured to meet the need via four design/build/test tasks and four analysis/study tasks. This paper focuses on Tasks 1-4, the four design/build/test tasks. Task 1 involves the development of nested thermally-switched enclosures featuring a reverse-operation DTE thermal switch (ROD-TSW) in series with a propylene miniaturized loop heat pipe (mini-LHP). Task 2 involves the development of a parabolic reflector radiator (PRR) for low latitude lunar sites. Task 3 involves the development of a low effective emissivity (e*) multilayer insulation (MLI) known as “spacerless” MLI. Finally, Task 4 involves the development of low conductance (G) thermal isolators. Available test results for all four tasks will be summarized in the paper, as will the plans for the remainder of the PALETTE project.

Britton, Shawn R↗
DOE OSTI2025

WHOLESCALE - Water & Hole Observations Leverage Effective Stress Calculations And Lessen Expenses (Final Technical Report 2020 - 2024)

The WHOLESCALE acronym stands for Water & Hole Observations Leverage Effective Stress Calculations and Lessen Expenses. The goal of the WHOLESCALE project is to simulate the spatial distribution and temporal evolution of stress in the geothermal system at San Emidio in Nevada, United States. To reach this goal, the WHOLESCALE team has developed a methodology to incorporate and interpret data from four methods of measurement into a multi-physics model that couples thermal, hydrological, and mechanical (T H-M) processes. The WHOLESCALE team has applied this methodology at the San Emidio geothermal field, located ~100 km north of Reno, Nevada in the northwestern Basin and Range province. The WHOLESCALE team includes 30 individuals working at two universities, two national laboratories, and one industry partner. Two master-degree students and five post-doctoral researchers have gained professional experience and earned partial financial support via the WHOLESCALE project. The WHOLESCALE team has taken advantage of the perturbations created by changes in pumping operations during planned shutdowns in 2016, 2021, and 2022 to infer temporal changes in the state of stress in the geothermal system at San Emidio, Nevada, U.S. The WHOLESCALE results support the working hypothesis that increasing pore-fluid pressure reduces the effective normal stress acting across fault zones. During normal operations, pumping in deep production wells decreases fluid pressures and thus increases the effective normal stresses on faults, reducing microseismicity. During planned shutdowns, the cessation of production increases pore-fluid pressure and reduces effective normal stress. The WHOLESCALE products generated during the 4-year period between 2020 and 2024 include: three articles published in the open-access, peer-reviewed scientific literature, two master’s theses, 20 presentations or papers at scientific conferences, and 17 data sets available on public repositories. The WHOLESCALE project has been completed in two phases that included three performance periods separated by two Go/No-go Stage Gate Reviews. Tasks were classified by data type (i.e., Geologic Structure, Borehole, Geodesy, Hydrology, Seismology, and Modeling). The first phase of the project started July 31, 2020 and included ongoing project coordination (Task 1), a project kickoff (Task 2), analysis of existing data (Task 3), development of the initial stress model & deployment design (Task 4), and Go/No-go Decision Point #1 (Task 5). Phase II began with implementing the 2022 deployment (Task 6), followed by Go/No-go Decision Point #2 (Task 7) The remainder of Phase II consisted of analyzing data collected during deployment (Task 8), calibration of the stress model on all observations (Task 9), and the Final Review (August 23, 2024) & Reporting (Task 10).

15 GEOTHERMAL ENERGY↗
NASA NTRS1976

Development of an improved coating for polybenzimidazole foam

An improved coating system was developed for Polybenzimidazole (PBI) foam to provide coating stability, ruggedness, moisture resistance, and to satisfy optical property requirements (alpha sub (s/epsilon) or = 0.4 and epsilon 0.8) for the space shuttle. The effort was performed in five tasks: Task 1 to establish material and process specifications for the PBI foam, and material specifications for the coatings; Task 2 to identify and evaluate promising coatings; Task 3 to establish mechanical and thermophysical properties of the tile components; Task 4 to determine by systems analysis the potential weight trade-offs associated with a coated PBI TPS; and Task 5 to establish a preliminary quality assurance program. The coated PBI tile was, through screening tests, determined to satisfy the design objectives with a reduced system weight over the baseline shuttle silica LRSI TPS. The developed tile provides a thermally stable, extremely rugged, low thermal conductivity insulator with a well characterized optical coating.

Neuner, G. J.↗
DOE OSTI2025

Solar Decarbonization of Paraffin Dehydrogenation Through Particle Heat Carriers (Final Technical Report)

This project focuses on solutions to decarbonize high-temperature catalytic processes using solar thermal heat. The primary project goal is to show the validity of a moving packed bed reactor for propane dehydrogenation using catalyst particles as the heat carrier for the reaction, which can be heated by concentrated solar energy in a particle receiver. This concept, if further developed, may provide a cost-effective pathway for converting lower value gases to important chemical precursors for industrial materials using only renewable energy. The project was divided into six tasks. In Task 1, DFT calculations were performed to understand the role of Pt and Sn in the catalytic dehydrogenation reaction. In Task 2 chemical kinetics measurements were made for several catalyst formulations at high temperatures. In Task 3, the solar absorptance of catalyst particles was compared to the absorptance of commonly used materials in particle receivers. In Task 4, numerical models were developed which could predict performance of the complete system and predict specific temperatures in the system. In Task 5, a prototype system was designed, fabricated, and tested to show the validity of the concept. Task 6 concerned project management activities. Experiments with the prototype showed repeatable thermal performance at temperatures targeted for the reaction. A limited set of tests were done with active catalyst and propane dehydrogenation, showing conversion of propane to propylene with a range of conversions and selectivities. The results are promising, and the prototype designed was reliable during testing, and the team expects that further development of the prototype would yield improved results. A numerical model framework based on coupled fluid and particle mechanics was developed with high computational efficiency using GPU calculations. The model may prove highly useful for evaluating other high-temperature particle systems. However, it was determined that simpler porous media models were good fits for the needs of the current moving packed bed concept. Data showing strong solar absorption of the particles validates the plan of using existing solar particle receivers with only a change in the particle type. Catalyst investigation showed that Pt 1 Sn 3 is the most viable candidate for developing PtSn catalysts for high temperature propane dehydrogenation, considering the balance of activity, selectivity, and deactivation. This project completed an initial study of various factors needed to incorporate a moving bed catalytic reactor for propane dehydrogenation into a concentrated solar thermal particle system. Future developments may allow this technology to be scaled up and help to use solar thermal energy to decarbonize not only the propane dehydrogenation reaction, but other gas-solid catalytic reactions at similar temperatures.

14 SOLAR ENERGY↗
NASA NTRS1992

PROS: An IRAF based system for analysis of x ray data

PROS is an IRAF based software package for the reduction and analysis of x-ray data. The use of a standard, portable, integrated environment provides for both multi-frequency and multi-mission analysis. The analysis of x-ray data differs from optical analysis due to the nature of the x-ray data and its acquisition during constantly varying conditions. The scarcity of data, the low signal-to-noise ratio and the large gaps in exposure time make data screening and masking an important part of the analysis. PROS was developed to support the analysis of data from the ROSAT and Einstein missions but many of the tasks have been used on data from other missions. IRAF/PROS provides a complete end-to-end system for x-ray data analysis: (1) a set of tools for importing and exporting data via FITS format -- in particular, IRAF provides a specialized event-list format, QPOE, that is compatible with its IMAGE (2-D array) format; (2) a powerful set of IRAF system capabilities for both temporal and spatial event filtering; (3) full set of imaging and graphics tasks; (4) specialized packages for scientific analysis such as spatial, spectral and timing analysis -- these consist of both general and mission specific tasks; and (5) complete system support including ftp and magnetic tape releases, electronic and conventional mail hotline support, electronic mail distribution of solutions to frequently asked questions and current known bugs. We will discuss the philosophy, architecture and development environment used by PROS to generate a portable, multimission software environment. PROS is available on all platforms that support IRAF, including Sun/Unix, VAX/VMS, HP, and Decstations. It is available on request at no charge.

Conroy, M. A.↗
DOE OSTI2026

Large Load Integration - Task List and Overview

Large Load Integration Tasks: Task 1 – Workshops Support stakeholder engagement across industry to promote collaboration and identify solutions to challenges that will guide other work Task 2 – Ancillary Services Characterize different types of large loads to assess under what conditions they may be utilized to provide grid stability services Task 3 – Communications Explore the cybersecurity and communications infrastructure required to enable large loads to interface with grid operations to provide ancillary services Task 4 – Nuclear Integration Explore risks and methods for supporting large load energy needs with SMRs and incorporating them into the wider power system Task 5 – Decision Support and TA Provide support to stakeholders through the creation of planning tools and direct technical assistance.

24 - POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2024

The Grain Boundary Relaxation (GBR) Approach for Manufacturing High Strength Nanocrystalline Lightweight Metals

The overarching goal of the project was to conduct research and development work as proposed in the Statement of Project Objective (SOPO) of the award document DE-FE-0009116. The project had 4 tasks and 12 milestones. All the milestone deliverables were completed. The accomplishments of the project objectives and technical discussions are described in Sections 3 and 4, respectively. The modeling and simulation work indicated that to increase the strength and stability of nanocrystalline aluminum (Al), selection of dopants, such as Mg, is necessary. It was predicted that the crystallite size should be less than 50 nm to give high strength. On the basis of modeling, cryo-milling of Al was conducted with the addition of Mg as a function of different times. The crystallite size of the cryo-milled powders was determined by XRD and TEM. Both measurements showed that the actual crystallite size of the grain was <40 nm. The thermal stability of the grain size was established as a function of temperature. It was established that the grain size was < 50 nm up to 500C. The crystallite size of the bulk sample prepared by spark plasma sintering (SPS) and cold spray (CS) additive manufacturing was less than <40 nm. The mechanical properties of the bulk samples prepared by SPS and CS, showed excellent microhardness, good tensile properties (>200 MPa) with moderate ductility and improved fatigue performance. Adding yttria stabilized zirconia (YSZ) improved the build thick of the CS sample, however the YSZ was getting embedded into the sample. A highly dense SPS samples sent for 3rd party testing to the Innovation Testing Services showed a minimum hardness of 180 HV with an average tensile strength of 512.5 MPa. The high cycle fatigue tests also showed an endurance limit of 179.5 MPa. The Energy cost evaluations showed an overall energy cost of around $\$$17.05 for the cryomilling and SPS processes and the total manufacturing cost calculations of $\$$78.14 for 1 kg of sample. The energy cost to prepare a Kg of CS sample is $\$$17.60 and the overall manufacturing cost is $\$$86.85.

36 MATERIALS SCIENCE↗
NASA NTRS1995

Vaccum Gas Tungsten Arc Welding, phase 1

This two year program will investigate Vacuum Gas Tungsten Arc Welding (VGTAW) as a method to modify or improve the weldability of normally difficult-to-weld materials. VGTAW appears to offer a significant improvement in weldability because of the clean environment and lower heat input needed. The overall objective of the program is to develop the VGTAW technology and implement it into a manufacturing environment that will result in lower cost, better quality and higher reliability aerospace components for the space shuttle and other NASA space systems. Phase 1 of this program was aimed at demonstrating the process's ability to weld normally difficult-to-weld materials. Phase 2 will focus on further evaluation, a hardware demonstration and a plan to implement VGTAW technology into a manufacturing environment. During Phase 1, the following tasks were performed: (1) Task 11000 Facility Modification - an existing vacuum chamber was modified and adapted to a GTAW power supply; (2) Task 12000 Materials Selection - four difficult-to-weld materials typically used in the construction of aerospace hardware were chosen for study; (3) Task 13000 VGTAW Experiments - welding experiments were conducted under vacuum using the hollow tungsten electrode and evaluation. As a result of this effort, two materials, NARloy Z and Incoloy 903, were downselected for further characterization in Phase 2; and (4) Task 13100 Aluminum-Lithium Weld Studies - this task was added to the original work statement to investigate the effects of vacuum welding and weld pool vibration on aluminum-lithium alloys.

Weeks, J. L.↗
NASA NTRS1982

Comparative analysis of operational forecasts versus actual weather conditions in airline flight planning: Summary report

The impact of more timely and accurate weather data on airline flight planning with the emphasis on fuel savings is studied. This summary report discusses the results of each of the four major tasks of the study. Task 1 compared airline flight plans based on operational forecasts to plans based on the verifying analyses and found that average fuel savings of 1.2 to 2.5 percent are possible with improved forecasts. Task 2 consisted of similar comparisons but used a model developed for the FAA by SRI International that simulated the impact of ATc diversions on the flight plans. While parts of Task 2 confirm the Task I findings, inconsistency with other data and the known impact of ATC suggests that other Task 2 findings are the result of errors in the model. Task 3 compares segment weather data from operational flight plans with the weather actually observed by the aircraft and finds the average error could result in fuel burn penalties (or savings) of up to 3.6 percent for the average 8747 flight. In Task 4 an in-depth analysis of the weather forecast for the 33 days included in the study finds that significant errors exist on 15 days. Wind speeds in the area of maximum winds are underestimated by 20 to 50 kts., a finding confirmed in the other three tasks.

Keitz, J. F.↗
DOE OSTI2025

Southwest Regional Partnership on Carbon Sequestration: Phase III (Final Scientific/Technical Report)

The Southwest Regional Partnership on Carbon Sequestration (SWP) is one of 7 regional partnerships formed in 2003 under the U.S. Department of Energy’s (DOE) Regional Carbon Sequestration Partnerships (RCSPs) initiative. The overall purpose of the initiative was to help determine and implement the technology, infrastructure, and regulations most appropriate to promote carbon storage in different regions of the country. Covering Arizona, Colorado, New Mexico, Oklahoma, Utah, and parts of Texas, Wyoming, and Kansas, the SWP evaluated regional carbon storage and utilization potential and focused on technologies and sites that could complement the region’s strong position in energy production. The project progressed through three phases: • Phase I (2003–2005): Characterized regional geologic formations and CO 2 sources, assessed sequestration potential, and identified pilot test sites. • Phase II (2005–2013): Conducted small-scale field tests to validate sequestration methods, including geologic and terrestrial projects. • Phase III (2008–2022): Demonstrated large-scale CO 2 injection at a commercial oil field to test monitoring, verification, and long-term storage strategies. This report covers Phase III. The final project site, the Farnsworth Unit (FWU) in Texas, provided real-world testing of reservoir characterization, monitoring, and risk evaluation tools and processes that could be used in any commercial scale carbon capture, utilization, and storage (CCUS) project. Extensive data collection and analysis helped refine best practices for reservoir characterization, injection monitoring, and storage verification. The SWP contributed to national databases, DOE best practice manuals, and regional geological assessments to support future sequestration efforts. Key lessons learned include the importance of robust data management, strategic site selection, regulatory navigation, and effective industry collaboration. The project’s findings will inform ongoing and future carbon storage initiatives. Task 1 (Regional Characterization) • The SWP continued to participate in national outreach efforts and NATCARB. • The SWP evaluated multiple potential sites before selecting the FWU as the primary field test location. Task 2 (Public Outreach and Education) • The SWP contributed to national databases, DOE best practice manuals, and regional geological assessments to support future sequestration efforts. Task 3 (Permitting and Regulatory Compliance) • The SWP ensured compliance with federal and state regulations, including National Environmental Policy Act (NEPA) requirements. • The SWP obtained all necessary permits for drilling, injection, and monitoring activities. Task 4 (Site Characterization and Planning) • The SWP developed work plans for four key activities: characterization, simulation, monitoring and verification, and risk evaluation. • The SWP collected and synthesized legacy data from multiple sources to build initial static geological models and dynamic reservoir models demonstrating project feasibility. • The SWP conducted an initial risk evaluation and developed mitigation plans. Task 5 (Field Operations and Data Collection) • The SWP drilled, logged, and cored three characterization wells to gather critical subsurface data. • The SWP conducted multiple geophysical surveys, including 3D seismic, crosswell seismic, and vertical seismic profiling, to improve reservoir characterization. Task 6 (Monitoring and Verification) • The SWP performed extensive geological characterization using data from characterization wells and seismic surveys. • The SWP established a surface monitoring network to track CO 2 flux in soil gas, groundwater chemistry, and near-surface atmospheric CO 2 levels. • The SWP built and refined reservoir models to study the effects of relative permeability on simulation behavior and improve calibration with experimental data. Task 7 (Risk Assessment and Model Refinement) • The SWP conducted multiple studies to evaluate reservoir integrity, predict CO 2 plume behavior and improve predictive modeling capabilities. • The SWP refined geological models and used them to enhance the accuracy of simulation models. • The SWP continued quantitative risk assessment of top-ranked risks and strengthened the link between qualitative and quantitative risk methodologies.

02 PETROLEUM↗
NASA NTRS1972

Study of structural active cooling and heat sink systems for space shuttle

This technology investigation was conducted to evaluate the feasibility of a number of thermal protection systems (TPS) concepts which are alternate candidates to the space shuttle baseline TPS. Four independent tasks were performed. Task 1 consisted of an in-depth evaluation of active structural cooling of the space shuttle orbiter. In Task 2, heat sink concepts for the booster were studied to identify and postulate solutions for design problems unique to heat sink TPS. Task 3 consisted of a feasibility demonstration test of a phase change material (PCM) incorporated into a reusable surface insulation (RSI) thermal protection system for the shuttle orbiter. In Task 4 the feasibility of heat pipes for stagnation region cooling was studied for the booster and the orbiter. Designs were developed for the orbiter leading edge and used in trade studies of leading edge concepts. At the time this program was initiated, a 2-stage fully reusable shuttle system was envisioned; therefore, the majority of the tasks were focused on the fully reusable system environments. Subsequently, a number of alternate shuttle system approaches, with potential for reduced shuttle system development funding requirements, were proposed. Where practicable, appropriate shifts in emphasis and task scoping were made to reflect these changes.

Source record↗
DOE OSTI2026

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

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

EARTH SCIENCE > LAND SURFACE > SOILS↗
DOE OSTI2026

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

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

EARTH SCIENCE > LAND SURFACE > SOILS↗
DOE OSTI2026

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

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

EARTH SCIENCE > LAND SURFACE > SOILS↗
DOE OSTI2026

High-Torque Heavy-Rare-Earth-Free Electric Motor Thermal Management

This project is part of a multi-lab Next-Generation Reliable Electric Drive Systems for Medium and Heavy-Duty Vehicles (NEXT-DRIVE) project led by Oak Ridge National Laboratory (ORNL), and including NREL, Sandia National Laboratories (SNL), and Ames Laboratory that leverages research expertise and facilities of these national labs to develop tools and approaches for reducing the design and development time of new electric drive technologies for medium and heavy-duty vehicles (MHDVs) and their associated costs while increasing reliability and asset utilization. The Next-Drive project aligns with the DOE's goals by introducing high-fidelity multi-physics and AI/ML-based modeling to design low-cost, highly reliable, and longer-lifetime drivetrains, aiming to achieve 25 years of progress in 5 years. The efforts of this project will focus on NEXT-DRIVE Task 4 (led by ORNL, NREL, and AMES) - developing high-fidelity modeling framework and identifying technologies enabling heavy-rare-earth-free electric motors for medium- and heavy-duty vehicles to achieve 1 million miles of operation. Contrary to conventional approaches that optimize the motor for power density, the focus will be to identify motor designs that achieve the best trade-off between motor power density and durable operation. NREL tasks include development of high-fidelity motor thermal models incorporating rotor windage losses and identification, evaluation and measurement of motor interface materials in key thermal pathways. The poster summarizes NREL's accomplishments for the first half of FY 2025 and outlines future plans.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2023

Development of Corrosion Resistant Coatings for Structural Materials for Liquid Fueled Molten Salts Reactors Applications (Final Report)

Current structural alloys, code certified for the MSRs temperature ranges, contain high levels of chromium, making them highly susceptible to molten halide salt corrosion. One potential solution to circumvent the need for code certification of novel alloys, which is expensive and time consuming, is to design claddings that will protect the underlying code certified materials from corrosion damage during operation. In this work, we examined the corrosion of Ni and Cu electroplated, Ni and CuNi weld overlay, Mo-laser clad, and carburized claddings on SS316H for use in molten salt reactor environments. After characterization of the cladded materials (Task 1), static corrosion tests were used to assess corrosion resistance of the claddings in typical molten fluoride salts (Task 2). The corrosion tests were performed in molten FLiNaK at 700°C up to 1000 hours. Pre- and post-corrosion Scanning Electron Microscopy (SEM), energy-dispersive-spectroscopy (EDS), Transmission Electron Microscopy (TEM), X-ray Diffraction (XRD) and glow-discharge-optical-emission-spectroscopy (GDOES) were performed on cladding cross-sections and surfaces to evaluate degradation. The Cu and Ni electroplated samples, as well as the carburized samples, showed excellent corrosion resistance relative to the bare SS316H. To assess high temperature cladding stability, ageing experiments were performed at temperatures up to 900 C in inert atmosphere for the electroplated and carburized samples and a diffusion model was developed to predict long term cladding behavior (Task 3). It was found that the Cu cladding was basically insensitive to the high temperature ageing, except for small secondary phases forming at the interface. On the other hand, the Ni electroplated cladding experienced significant interdiffusion. Nevertheless, the gain in corrosion resistance for a 100μm Ni electroplated cladding is phenomenal, with more than 50% reduction in chromium dissolution from the substrate material for the first 15 years of salt exposure at 700°C. To assess radiation resistance and phase stability, high-temperature 4MeV Ni heavy ion irradiation was performed across the cladding/substrate interface up to 50 displacement-per-atom (DPA) at 500°C and 700°C to assess the phase stability and irradiation behaviors of the electroplated systems (Task 4). The Ni and Cu electroplated systems did not experience void swelling at the contrary to the SS316H substrate due to their nanocrystalline nature. Some level of recrystallization was observed in the cladding, as well as radiation induced segregation and enhanced diffusion. The interface acted as a potent sink for point defects with the presence of a void denuded zone. The mechanical properties of the claddings were assessed using thermal shock, micro-indentation, nano-indentation, and four-point bend testing experiments (Task 5). These experiments were performed on the claddings in as-received, corroded, irradiated, and thermally aged states to determine the effects of typical molten salt reactor environments on cladding integrity. No significant mass loss was observed after repeated thermal shocks. While the electroplated samples softened after high-temperature ageing, irradiation hardening compensate this effect, such that there is little different with the as-received materials. The results of these experiments suggest that the electroplated (copper/nickel) show the greatest promise for application in molten salt reactor development. These claddings prevented any chromium dissolution from occurring during the static corrosion experiments. Additionally, they demonstrated favorable properties for high temperature diffusion, phase stability, interfacial mechanical properties, and irradiation resistance. Weld-overlay cladding are also of great interest and could reach properties similar to the electroplated samples upon optimization (especially with multiple weld passes). The carburized SS316H showed excellent behavior as well, but more work needs to be done to assess their long-term stability. Finally, the Mo-laser clad system was not pursued further due to the manufacturing challenges. It is also worth noting that the Ni cladding systems should behave relatively well in terms of weldability since the Ni-weld overlay microstructure and associated corrosion rate are sound. While the NiCu weld-overlay has even lower corrosion rates than the Ni-weld overlay, more studies on welding of Cu-electroplated systems are necessary since Cu clusters are known to embrittle steels.

36 MATERIALS SCIENCE↗
NASA NTRS1994

Test Plans. Lightweight Durable TPS: Tasks 1,2,4,5, and 6

The objective of this task is to develop the fluted core flexible blankets, also referred to as the Tailorable Advanced Blanket Insulation (TABI), to a technology readiness level (TRL) of 6. This task is one of the six tasks under TA 3, Lightweight Durable TPS study, of the Single Stage to Orbit (SSTO) program. The purpose of this task is to develop a durable and low maintenance flexible TPS blanket material to be implemented on the SSTO vehicle.

Greenberg, H. S.↗
NASA NTRS2009

Effect of Changing the Center of Gravity on Human Performance in Simulated Lunar Gravity

Subjective measures of physical exertion, compensation, and controllability while performing tasks in simulated reduced gravity can be affected by changing the center of gravity (CG) of the overall system. The CG of the overall system is defined as the combined CG of the subject, the spacesuit, and the equipment required to change the CG. PURPOSE: To determine if changing the CG affects subjective ratings of suited human performance in simulated lunar gravity. METHODS: A custom weight support structure interfaced with the lunar prototype spacesuit, allowing manipulation of the CG. Weight locations to alter CG were based on a reference subject (81.6 kg, 182.9 cm). Six subjects (80.0 +/- 10.6 kg, 182.3 +/- 6.2 cm) completed 4 tasks (walking, kneel/stand, rock pickup, and shoveling) with system CG at 3 different locations (B=4.8/1.0, C=7.6/14.4, and P=11.2/20.1 cm aft/above the standard subject?s CG). Lunar gravity (0.17-g) was simulated by parabolic flight. Suited testing was performed at 29.6 kPa with a combined suit and structure mass of 181 kg. In all conditions, subjects provided ratings of perceived exertion (RPE) and the gravity compensation and performance scale (GCPS) upon completion of each task. RESULTS: Mean RPE and GCPS were highest at P for all tasks. Variability was greatest at B and lowest at C, and large variations between subjects at the same CG existed for both RPE and GCPS. These trends were not consistent with results from unsuited CG studies performed in other underwater and overhead suspension lunar gravity simulations. CONCLUSION: Modifying CG during suited testing at lunar gravity seems to affect subjective human performance. However, variation in subjective ratings at a given CG location indicates that further study is needed to determine the interactions among lunar gravity simulation, system CG, system mass, and subject characteristics such as anthropometry, strength, and fitness.

Chappell, Steven P.↗