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

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At least 91 records · Page 5

Electrolysis Propulsion Provides High-Performance, Inexpensive, Clean Spacecraft Propulsion

An electrolysis propulsion system consumes electrical energy to decompose water into hydrogen and oxygen. These gases are stored in separate tanks and used when needed in gaseous bipropellant thrusters for spacecraft propulsion. The propellant and combustion products are clean and nontoxic. As a result, costs associated with testing, handling, and launching can be an order of magnitude lower than for conventional propulsion systems, making electrolysis a cost-effective alternative to state-of-the-art systems. The electrical conversion efficiency is high (>85 percent), and maximum thrust-to-power ratios of 0.2 newtons per kilowatt (N/kW), a 370-sec specific impulse, can be obtained. A further advantage of the water rocket is its dual-mode potential. For relatively high thrust applications, the system can be used as a bipropellant engine. For low thrust levels and/or small impulse bit requirements, cold gas oxygen can be used alone. An added innovation is that the same hardware, with modest modifications, can be converted into an energy-storage and power-generation fuel cell, reducing the spacecraft power and propulsion system weight by an order of magnitude.

deGroot, Wim A.↗

Drop deployment system for crystal growth apparatus

This invention relates to a crystal growth apparatus (10) generally used for growing protein crystals wherein a vapor diffusion method is used for growing the crystals. In this apparatus, a precipitating solution and a solution containing dissolved crystalline material are stored in separate vials (12, 14), each having a resilient diaphragm (28) across one end and an opening (24) with a puncturable septum (26) thereacross at an opposite end. The vials are placed in receptacles (30) having a manifold (41) with a manifold diaphragm (42) in contact with the vial diaphragm at one end of the receptacle and a hollow needle (36) for puncturing the septum at the other end of the manifold. The needles of each vial communicate with a ball mixer (40) that mixes the precipitate and protein solutions and directs the mixed solution to a drop support (64) disposed in a crystal growth chamber (16), the drop support being a tube with an inner bevelled surface (66) that provides more support for the drop (68) than the tubes of the prior art. A sealable storage region (70) intermediate the drop support and mixer provides storage of the drop (68) and the grown crystals.

Rhodes, Percy H.↗

Characterize Human Forward Contamination Project

Let's face it: wherever we go, we will inevitably carry along the little critters that live in and on us. Conventional wisdom has long held that it's unlikely those critters could survive the space environment, but in 2007 microscopic animals called Tardigrades survived exposure to space and in 2008 Cyanobacteria lived for 548 days outside the International Space Station (ISS). But what about the organisms we might reasonably expect a crewed spacecraft to leak or vent? Do we even know what they are? How long might our tiny hitch-hikers survive in close proximity to a warm spacecraft that periodically leaks/vents water or oxygen-and how might they mutate with long-duration exposure? Unlike the Mars rovers that we cleaned once and sent on their way, crew members will provide a constantly regenerating contaminant source. Are we prepared to certify that we can meet forward contamination protocols as we search for life at new destinations? This project has four technical objectives: 1. TEST: Develop a test plan to leverage existing equipment (i.e. ISS) to characterize the kinds of organisms we can reasonably expect pressurized, crewed volumes to vent or leak overboard; as part of testing, we'll need to develop an Extravehicular Activity (EVA)-compatible tool that can withstand the pressure and temperature extremes of space, as well as collect, separate, and store multiple samples; 2. ANALYSIS: Develop an analysis plan to study those organisms in relevant destination environments, including spacecraft-induced conditions; 3. MODEL: Develop a modeling plan to model organism transport mechanisms in relevant destination environments; 4. SHARE: Develop a plan to disseminate findings and integrate recommendations into exploration requirements & ops. In short, we propose a system engineering approach to roadmap the necessary experiments, analysis, and modeling up front--rather than try to knit together disparate chunks of data into a sensible conclusion after the fact.

Rucker, Michelle↗

Microbial Monitoring of Astromaterials Curation Labs Reveals Inter-Lab Diversity

The Astromaterials Curation Division at NASA’s Johnson Space Center houses seven sample collections stored in separate clean rooms to avoid cross-contamination. Prior to receiving new sample collections from carbon rich asteroids, we instituted a monitoring program to characterize the microbial ecology of these labs and to understand how organisms could interact with and potentially contaminate current and future collections. Methods: Beginning in Oct. 2017 we sampled the Meteorite (ISO 7 equivalent) and Pristine Lunar (ISO 5 equivalent) labs on a monthly basis. Surface samples were collected using dry swabs. Air samples were collected using an impactor style air sampler. Cultivable organisms were identified and characterized. Aliquots of each sample were also preserved for DNA sequencing. For each sampling event recovery rate was calculated as the percentage of samples showing microbial growth1. Fungal colonies were selected for amino acid extraction and analysis via Ultra- Performance Liquid Chromatography with Fluorescence Detection and Mass Spectrometry.

Regberg, A. B.↗

CHESS 2025: Field-collected vegetation attributes and site photos

This dataset represents field observations of vegetation samples collected as part of the Colorado Headwaters Ecological Spectroscopy Study (CHESS) during June and July of 2025. Samples were collected in the field using tablet computers and digital forms, with target data differing by sample type (individual trees, individual shrubs, or 1-meter square plots of meadow and subshrub vegetation). Field samples were collected within 72 hours of airborne data collection using the National Ecological Observatory Network’s Aerial Observation Platform (NEON AOP). The NEON AOP collected waveform LiDAR (Light Detection and Ranging) and imaging spectrometer data in 426 spectral bands from the visible to shortwave infrared. Remote sensing data for the project is available on ESS-DIVE (DOI and citation to be added upon publication). Field data collected included canopy height and per-species horizontal proportional cover for meadow plots, species identity and height information for shrubs, as well as species identity, height, diameter at breast height, and health assessment information for trees. Photos of the focal site and surrounding landscape were taken for all sampling sites and are included in this archive. Green leaves or needles were collected for plant trait and foliar chemistry analysis. This data is archived separately (DOI and citation to be added upon publication). High-precision geospatial data for each sample (crown perimeter polygons for trees and shrubs, plot boundaries for meadow plots) is available here (Henderson et al., 2026). Field and remote sensing protocols largely followed those of a previous field and airborne imaging campaign performed in 2018 (described in Chadwick et al. 2020). Field data from the 2018 campaign can be found here (Chadwick et al., 2020 doi:10.15485/1618130). Because different field measurements were taken for meadow, shrub, and tree sites, data from these three sample types are archived as separate tables (chess_meadow_site_cleaned.csv, chess_shrub_site_cleaned.csv, chess_tree_site_cleaned.csv). Meadow proportional cover data is stored in a separate table (chess_meadow_cover_cleaned.csv). Taxonomy was treated identically between sample types, and the dataset shares a common set of voucher specimens (chess_voucher_IDs_cleaned.csv), as well as a single species list (chess_species_list_cleaned.csv). All taxonomic determinations were performed to the species level, and adhere to the Global Biodiversity Information Facility (GBIF) backbone taxonomy as of January 10th, 2026 (GBIF Secretariat 2023). CHESS Project Description: The Colorado Headwaters Ecological Spectroscopy Study (CHESS) comprised a multi-week airborne remote sensing and field observation campaign in the Upper Gunnison Basin, Colorado, conducted in June and July of 2025. Airborne remote sensing was conducted by the National Ecological Observatory Network Airborne Observation Platform (NEON AOP), concurrent with a field campaign run by the Rocky Mountain Biological Laboratory (RMBL), the Lawrence Berkeley National Laboratory (LBNL) and SLAC National Accelerator Laboratory Watershed Function Science Focus Area (SFA), and NASA-JPL (Jet Propulsion Laboratory) Earth Surface Mineral Dust Source Investigation (EMIT) program. Between June 10 and July 18, 2025, the NEON AOP flight team collected high-resolution aerial imaging spectroscopy and Light Detection and Ranging (LiDAR) data over three domains: the Upper East River (CRBU), Almont Triangle (ALMO), and the Upper Taylor Basin (UPTA). In coordination with the flights, a field campaign acquired ground-truth observations, including observations of vegetation composition, foliar traits, forest demography, and subsurface properties in 18 core sampling areas within the domains. Additional surface water observations were taken at over 380 point locations. All CHESS campaign datasets can be found within the CHESS ESS-DIVE data portal: https://data.ess-dive.lbl.gov/portals/chess. Funding Acknowledgment: Field and remote-sensing data acquisition was performed under a grant from the National Aeronautics and Space Administration (80NSSC24K1005). This work was also 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.

2018 NEON and 2025 CHESS Campaigns↗

Electricity Baseline 2022 Background Data and Log File

The ElectricityLCI v2 Python package (https://github.com/USEPA/ElectricityLCI/tree/v2.0) was used to generate the 2022 electricity baseline: a regionalized life cycle inventory model of U.S. electricity generation, consumption, and distribution using standardized facility and generation data. ElectricityLCI implements a local data store for downloading and accessing public data on an individual's computer. The data store follows the folder definition provided by USEPA's esupy Python package (https://github.com/USEPA/esupy), which utilized the appdirs Python dependency (https://pypi.org/project/appdirs/). This submission includes the background data used to generate the 2022 electricity baseline inventory. Each zip archive stores the source files as found in their data stores. Sub-folders in each of the data stores are archived separately. For example, stewi.zip contains the JSON files, while stewi.facility.zip is the 'facility' sub-folder of stewi data store that stores the parquet files. To reproduce the data store, extract each zip file and drag-and-drop sub-folders in to their appropriate root folders to recreate the data stores, then copy the root folders to your data store folder (as returned by running the following on the command line: `python -c "import appdirs; print(appdirs.user_data_dir())"`). The main five data stores include: 'electricitylci', 'facilitymatcher', 'fedelemflowlist', 'stewi', and 'stewicombo'. The log file generated by the 2022 model run is also included, which contains the statements at the DEBUG level and above.

Electricity; LCA; data inventory↗

Electricity Baseline 2021 Background Data and Log File

The ElectricityLCI v2 Python package (https://github.com/USEPA/ElectricityLCI/tree/v2.0) was used to generate the 2021 electricity baseline: a regionalized life cycle inventory model of U.S. electricity generation, consumption, and distribution using standardized facility and generation data. ElectricityLCI implements a local data store for downloading and accessing public data on an individual's computer. The data store follows the folder definition provided by USEPA's esupy Python package (https://github.com/USEPA/esupy), which utilizes the appdirs Python dependency (https://pypi.org/project/appdirs/). An overview of the ElectricityLCI data stores may be found on the README (https://github.com/USEPA/ElectricityLCI/blob/v2.0/README.md#data-store). This submission includes the background data used to generate the 2021 electricity baseline inventory. Each zip archive stores the source files as found in their data stores. Sub-folders in each of the data stores are archived separately. For example, stewi.zip contains the JSON files, while stewi.facility.zip is the 'facility' sub-folder of stewi data store that stores the parquet files. To reproduce the data store, extract each zip file and drag-and-drop sub-folders in to their appropriate root folders to recreate the data stores, then copy the root folders to your data store folder (as returned by running the following on the command line: python -c "import appdirs; print(appdirs.user_data_dir())"). The main five data stores include: 'electricitylci', 'facilitymatcher', 'fedelemflowlist', 'stewi', and 'stewicombo'. The log file generated by the 2021 model run is also included, which contains the statements at the DEBUG level and above.

Electricity; LCA; LCI; Life Cycle; data inventory↗

Electricity Baseline 2020 Background Data and Log File

The ElectricityLCI v2 Python package (https://github.com/USEPA/ElectricityLCI/tree/v2.0) was used to generate the 2020 electricity baseline: a regionalized life cycle inventory model of U.S. electricity generation, consumption, and distribution using standardized facility and generation data. ElectricityLCI implements a local data store for downloading and accessing public data on an individual's computer. The data store follows the folder definition provided by USEPA's esupy Python package (https://github.com/USEPA/esupy), which utilizes the appdirs Python dependency (https://pypi.org/project/appdirs/). An overview of the ElectricityLCI data stores may be found on the README (https://github.com/USEPA/ElectricityLCI/blob/v2.0/README.md#data-store). This submission includes the background data used to generate the 2020 electricity baseline inventory. Each zip archive stores the source files as found in their data stores. Sub-folders in each of the data stores are archived separately. For example, stewi.zip contains the JSON files, while stewi.facility.zip is the 'facility' sub-folder of stewi data store that stores the parquet files. To reproduce the data store, extract each zip file and drag-and-drop sub-folders in to their appropriate root folders to recreate the data stores, then copy the root folders to your data store folder (as returned by running the following on the command line: python -c "import appdirs; print(appdirs.user_data_dir())"). The main five data stores include: 'electricitylci', 'facilitymatcher', 'fedelemflowlist', 'stewi', and 'stewicombo'. The log file generated by the 2020 model run is also included, which contains the statements at the DEBUG level and above.

Electricity; LCA; LCI; Life Cycle; data inventory↗

New Hayabusa2 and OSIRIS REx Curation Facilities at Nasa Johnson Space Center

Asteroids are made up of rocky material that is left over from the formation of our solar system [1]. This material holds the key to unlocking information about the history of the planets and our sun. Hayabusa2 and OSIRIS-REx are two sample return missions that have collected rocky material from carbonaceous asteroids which are rich in water, carbon, and organic compounds [1]. Hayabusa2 is a spacecraft that collected samples from asteroid Ryugu launched by the Japan Aerospace Exploration Agency (JAXA). Samples were returned to Earth in December 2020 and 10 percent of the collected material is currently housed in a state-of-the-art laboratory at NASA Johnson Space Center (JSC) dedicated to Ryugu material [1]. OSIRIS-REx is a spacecraft launched by NASA that collected samples from asteroid Bennu. The samples are on their journey to Earth and will arrive in September 2023. They will be stored in a separate cleanroom dedicated to Bennu material. From these two missions, scientists will examine dust particles and rocky material to research clues on how the early solar system formed and how life began [2]. Because these samples are so precious and rare, it is important to keep them free from Earth contamination. In September of 2020, a massive construction project began to provide curation facilities to meet the needs of two incoming asteroid collections, Hayabusa2 and OSIRIS-REx. Despite numerous setbacks caused by the coronavirus pandemic, these facilities have been completed in the fall of 2021. Some of these setbacks included strict PPE requirements, keeping safe working distances as well as access to the facilities as NASA was operating under a Stage 3 condition in response to the pandemic. These facilities will provide curation laboratories to safely house the precious asteroid samples as well as facilities to manipulate samples, do sample preparation and process samples into and out of the labs for future studies by the scientific community. The lab suite consists of several different rooms including an initial anteroom (ISO 7), a staging area (ISO 7), two gowning areas (one for OSIRIS-REx and one for Hayabusa2; both ISO 6)), the Haybusa2 Curation Laboratory (ISO 5), the OSIRIS-REx Curation Laboratory (ISO 5), an ultramicrotomy laboratory (ISO 7) and a thin section laboratory (not a cleanroom). To maintain cleanliness levels necessary for these lab spaces quarterly to monthly Balazs organic and inorganic sampling has been conducted on a regular basis before, during and after the construction project to monitor the cleanliness levels in the new labs that will house the asteroid samples. These new facilities feature a Ruuvi (Grafana) monitoring system that measures temperature, humidity, and pressure of the lab suite. This is a small puck shaped sensor that sends signals to the Grafana cloud where the data can be plotted and set up on dashboards within the lab suite. These sensors are easy to set up and install and they run off a cell battery that is good for 3-5 years before needing replacement. This system can deliver text messages and emails to the appropriate personnel when values fall outside of set limits. In addition to the monitoring system, this new lab suite features viewing windows from several different locations and sliding motion censored doors. We have obtained some state-of-the-art equipment to outfit the lab spaces (desiccators, microscopes, ultramicrotome) and are in the process of outfitting the suite with additional items such as: nitrogen gloveboxes, additional desiccators, micromanipulators, etc. The Hayabusa2 samples provided to NASA by JAXA included 23 millimeter sized grains and 4 containers with finer material. OSIRIS-REx is expected to return as much as 400 grams of asteroid Bennu. Samples will be stored in a nitrogen glove box and separated into nitrogen sealed transfer containers. Small Bennu and Ryugu particles will be handled and prepared (e.g., ultra-thin sections for scanning electron microscopy (SEM) or transmission electron microscopy (TEM)) using an Axis Pro micromanipulator and Leica EM UC7 ultramicrotome. Analyzing samples at the atomic scale will provide greater insight to the origins of the solar system and life formation on Earth. All the curation preparation for these sample collections is essential to ongoing research and efforts to understand our solar system, both nowand for future generations.

Curation↗

Portable instant display and analysis reflectance spectrometer

A portable analysis spectrometer (10) for field mineral identification is coupled to a microprocessor (11) and memory (12) through a bus (13) and A/D converter (14) to display (16) a spectrum of reflected radiation in a band selected by an adjustable band spectrometer (20) and filter (23). A detector array (21) provides output signals at spaced frequencies within the selected spectrometer band which are simultaneously converted to digital form for display. The spectrum displayed is compared with a collection of spectra for known minerals. That collection is stored in memory and selectively displayed with the measured spectrum, or stored in a separate portfolio. In either case, visual comparison is made. Alternatively, the microprocessor may use an algorithm to make the comparisons in search for the best match of the measured spectrum with one of the stored spectra to identify the mineral in the target area.

Goetz, Alexander F. H.↗

Where's My Data - WMD

WMD provides a centralized interface to access data stored in the Mission Data Processing and Control System (MPCS) GDS (Ground Data Systems) databases during MSL (Mars Science Laboratory) Testbeds and ATLO (Assembly, Test, and Launch Operations) test sessions. The MSL project organizes its data based on venue (Testbed, ATLO, Ops), with each venue's data stored on a separate database, making it cumbersome for users to access data across the various venues. WMD allows sessions to be retrieved through a Web-based search using several criteria: host name, session start date, or session ID number. Sessions matching the search criteria will be displayed and users can then select a session to obtain and analyze the associated data. The uniqueness of this software comes from its collection of data retrieval and analysis features provided through a single interface. This allows users to obtain their data and perform the necessary analysis without having to worry about where and how to get the data, which may be stored in various locations. Additionally, this software is a Web application that only requires a standard browser without additional plug-ins, providing a cross-platform, lightweight solution for users to retrieve and analyze their data. This software solves the problem of efficiently and easily finding and retrieving data from thousands of MSL Testbed and ATLO sessions. WMD allows the user to retrieve their session in as little as one mouse click, and then to quickly retrieve additional data associated with the session.

Quach, William L.↗

HarDWR - Harmonized Water Rights Records

A dataset within the Harmonized Database of Western U.S. Water Rights (HarDWR). For a detailed description of the database, please see the meta-record v2.0. Changelog v2.0 - Recalculated based on data sourced from WestDAAT - Changed using a Site ID column to identify unique records to using aa combination of Site ID and Allocation ID - Removed the Water Management Area (WMA) column from the harmonized records. The replacement is a separate file which stores the relationship between allocations and WMAs. This allows for allocations to contribute to water right amounts to multiple WMAs during the subsequent cumulative process. - Added a column describing a water rights legal status - Added "Unspecified" was a water source category - Added an acre-foot (AF) column - Added a column for the classification of the right's owner v1.02 - Added a .RData file to the dataset as a convenience for anyone exploring our code. This is an internal file, and the one referenced in analysis scripts as the data objects are already in R data objects. v1.01 - Updated the names of each file with an ID number less than 3 digits to include leading 0s v1.0 - Initial public release Description Here we present an updated database of Western U.S. water right records. This database provides consistent unique identifiers for each water right record, and a consistent categorization scheme that puts each water right record into one of seven broad use categories. These data were instrumental in conducting a study of the multi-sector dynamics of inter-sectoral water allocation changes though water markets (Grogan et al., *in review*). Specifically, the data were formatted for use as input to a process-based hydrologic model, Water Balance Model (WBM), with a water rights module (Grogan et al., *in review*). While this specific study motivated the development of the database presented here, water management in the U.S. West is a rich area of study (e.g., Anderson and Woosly, 2005; Tidwell, 2014; Null and Prudencio, 2016; Carney et al., 2021) so releasing this database publicly with documentation and usage notes will enable other researchers to do further work on water management in the U.S. West. We produced the water rights database presented here in four main steps: (1) data collection, (2) data quality control, (3) data harmonization, and (4) generation of cumulative water rights curves. Each of steps (1)-(3) had to be completed in order to produce (4), the final product that was used in the modeling exercise in Grogan et al. (*in review*). All data in each step is associated with a spatial unit called a Water Management Area (WMA), which is the unit of water right administration utilized by the state in which the right came from. Steps (2) and (3) required use to make assumptions and interpretation, and to remove records from the raw data collection. We describe each of these assumptions and interpretations below so that other researchers can choose to implement alternative assumptions an interpretation as fits their research aims. Motivation for Changing Data Sources The most significant change has been a switch from collecting the raw water rights directly from each state to using the water rights records presented in WestDAAT, a product of the Water Data Exchange (WaDE) Program under the Western States Water Council (WSWC). One of the main reasons for this is that each state of interest is a member of the WSWC, meaning that WaDE is partially funded by these states, as well as many universities. As WestDAAT is also a database with consistent categorization, it has allowed us to spend less time on data collection and quality control and more time on answering research questions. This has included records from water right sources we had previously not known about when creating v1.0 of this database. The only major downside to utilizing the WestDAAT records as our raw data is that further updates are tied to when WestDAAT is updated, as some states update their public water right records daily. However, as our focus is on cumulative water amounts at the regional scale, it is unlikely most records updates would have a significant effect on our results. The structure of WestDAAT led to several important changes to how HarWR is formatted. The most significant change is that WaDE has calculated a field known as `SiteUUID`, which is a unique identifier for the Point of Diversion (POD), or where the water is drawn from. This separate from `AllocationNativeID`, which is the identifier for the allocation of water, or the amount of water associated with the water right. It should be noted that it is possible for a single site to have multiple allocations associated with it and for an allocation to be able to be extracted from multiple sites. The site-allocation structure has allowed us to adapt a more consistent, and hopefully more realistic, approach in organizing the water right records than we had with HarDWR v1.0. This was incredibly helpful as the raw data from many states had multiple water uses within a single field within a single row of their raw data, and it was not always clear if the first water use was the most important, or simply first alphabetically. WestDAAT has already addressed this data quality issue. Furthermore, with v1.0, when there were multiple records with the same water right ID, we selected the largest volume or flow amount and disregarded the rest. As WestDAAT was already a common structure for disparate data formats, we were better able to identify sites with multiple allocations and, perhaps more importantly, allocations with multiple sites. This is particularly helpful when an allocation has sites which cross WMA boundaries, instead of just assigning the full water amount to a single WMA we are now able to divide the amount of water between the number of relevant WMAs. As it is now possible to identify allocations with water used in multiple WMAs, it is no longer practical to store this information within a single column. Instead the stAllocationToWMATab.csv file was created, which is an allocation by WMA matrix containing the percent Place of Use area overlap with each WMA. We then use this percentage to divide the allocation's flow amount between the given WMAs during the cumulation process to hopefully provide more realistic totals of water use in each area. However, not every state provides areas of water use, so like HarDWR v1.0, a hierarchical decision tree was used to assign each allocation to a WMA. First, if a WMA could be identified based on the allocation ID, then that WMA was used; typically, when available, this applied to the entire state and no further steps were needed. Second was the spatial analysis of Place of Use to WMAs. Third was a spatial analysis of the POD locations to WMAs, with the assumption that allocation's POD is within the WMA it should belong to; if an allocation still had multiple WMAs based on its POD locations, then the allocation's flow amount would be divided equally between all WMAs. The fourth, and final, process was to include water allocations which spatially fell outside of the state WMA boundaries. This could be due to several reasons, such as coordinate errors / imprecision in the POD location, imprecision in the WMA boundaries, or rights attached with features, such as a reservoir, which crosses state boundaries. To include these records, we decided for any POD which was within one kilometer of the state's edge would be assigned to the nearest WMA. Other Changes WestDAAT has Allowed In addition to a more nuanced and consistent method of assigning water right's data to WMAs, there are other benefits gained from using the WestDAAT dataset. Among those is a consistent categorization of a water right's legal status. In HarDWR v1.0, legal status was effectively ignored, which led to many valid concerns about the quality of the database related to the amounts of water the rights allowed to be claimed. The main issue was that rights with legal status' such as "application withdrawn", "non-active", or "cancelled" were included within HarDWR v1.0. These, and other water rights status' which were deemed to not be in use have been removed from this version of the database. Another major change has been the addition of the "unspecified water source category. This is water that can come from either surface water or groundwater, or the source of which is unknown. The addition of this source category brings the total number of categories to three. Due to reviewer feedback, we decided to add the acre-foot (AF) column so that the data may be more applicable to a wider audience. We added the ownerClassification column so that the data may be more applicable to a wider audience. File Descriptions The dataset is a series of various files organized by state sub-directories. In addition, each file begins with the state's name, in case the file is separate from its sub-directory for some reason. After the state name is the text which describes the contents of the file. Here is each file described in detail. Note that st is a placeholder for the state's name. stFullRecords_HarmonizedRights.csv: A file of the complete water records for each state. The column headers for each of this type of file are: state - The name of the state to which the allocations belong to. FIPS - The two digit numeric state ID code. siteID - The site location ID for POD locations. A site may have multiple allocations, which are the actual amount of water which can be drawn. In a simplified hypothetical, a farm stead may have an allocation for "irrigation" and an allocation for "domestic" water use, but the water is drawn from the same pumping equipment. It should be noted that many of the site ID appear to have been added by WaDE, and therefore may not be recognized by a given state's water rights database. allocationID - The allocation ID for the water right. For most states this is the water right ID, and what is recommended to use should a right be looked up on a given state's water rights database. The water amounts associated with these IDs tend to be finer scaled than those associated with siteID. It should be noted that some allocations may be extracted from multiple sites, particularly for larger Places of Use. ownerClassification - A classification of the types of owners for water rights. The most common is `Private` which incorporates a wide range of entities. Several classifications would be grouped into a government category, most of which are for the U.S. Federal Government. These allocations could be listed as "Federal", "United States of America", or as the names of any number of federal agencies. The last major grouping of entities is for "Native American"s. priorityDate - The date we use as the water right priority date for our modeling analysis. This is the legal priority date when it is available. However, for some rights, specifically from California and New Mexico, we used a pseudo priority date (e.g. well completion date or start of well drilling date) when a legal priority date was not available. The most questionable dates come from New Mexico, where the only date associated with certain water right records was the date the allocation was recorded in the database. As the allocation record creation tended to be within a few months of the filing of the application of the water right, from manually double checking the water rights, and our analysis focuses on aggregating water rights on the timescale of years, we determined it was acceptable to use such dates to include as many records as possible. primaryBeneficialUse - From the numerous state water use categories, WaDE categorized them into 21 categories WestDAAT. This column is the original WaDE category for the primary water use at the PoD site. allocationBeneficialUse - From the numerous state water use categories, WaDE categorized them into 21 categories for WestDAAT. This column is the original WaDE category

Economics↗

Quality-Controlled Meteorological Data from the Flood Control District of Maricopa County (FCDMC) Network, Phoenix, Arizona (1987-2024)

This dataset contains 15- or 30-minute interval meteorological data from the Flood Control District of Maricopa County (FCDMC), Arizona, USA, covering eight key variables across multiple sensor stations between 1987 and 2024. Each variable is stored as a separate CSV file, containing time-series data that have undergone rigorous quality control (QC) procedures and, where appropriate, short-gap interpolation for consistency. The quality control (QC) pipeline consisted of four sequential tests: (1) a range test to ensure all values fall within physically realistic limits, (2) a step test to identify abrupt and implausible changes between consecutive records, (3) a proximity test that validates flagged values from step test using data from nearby stations and exceedance probability thresholds, and (4) a persistence test to detect and remove periods of unrealistically constant readings. These thresholds were calibrated to Arizona’s environmental conditions and sensor specifications. After QC, short gaps (≤2 hours) were linearly interpolated to ensure consistent temporal resolution, except for wind variables. Due to a major upgrade in FCDMC’s data transmission system, only ALERT-2 protocol data (2016–2024) for wind variables are included; earlier ALERT-1 data were excluded because of irregular sampling and high missing rates. This dataset supports regional climate and infrastructure resilience studies by providing standardized, high-resolution meteorological data for the greater Phoenix metropolitan area.

54 ENVIRONMENTAL SCIENCES↗

Lessons from commissioning and operations of the ICARUS cryogenic system at Fermilab

Results and lessons from three years of commissioning and operations of the cryogenic and purificationsystems of the ICARUS neutrino detector installed at the Fermilab are described. The detector uses liquid argon stored in two separate containers of about 275 m 3 each. The liquid argon must be kept in very stable thermal conditions, and the contamination of electronegative impurities must be consistently kept at small fractions of parts per billion. Since its fill in early 2020, the detector has been commissioned and then operated in neutrino beams at Fermilab for the search of sterile neutrinos and measurements of neutrino-argon cross sections. The results of operating the cryogenic and purification systems and lessons learned, including challenges in reaching the design electron lifetime in argon bath, operating and servicing liquid nitrogen and argon circulation pumps, argon condensers and purifiers, are described. Plans for future operations and list of recommendations are presented.

43 PARTICLE ACCELERATORS↗

High-Resolution Regional Atmosphere–Ocean–Wave Coupled Simulations of Hurricane Henri (2021)

To explore the integrated effects of ocean and ocean surface wave related physical processes on tropical cyclone simulations, a set of three model simulations is performed. * In experiment 'A', the event is modeled using the Weather Research Forecasting (WRF) model alone with prescribed Sea Surface Temperature (SST) at 6-hour intervals. * In experiment ‘AO,’ WRF is coupled with the Finite Volume Community Ocean Model (FVCOM), enabling variable exchange between atmosphere and ocean, but without considering ocean surface wave-related physical processes. * In experiment ‘AOW’, WRF, FVCOM, and Simulating WAves Nearshore (SWAN) exchange variables with each other every hour through the OASIS3-MCT Coupler to allow direct and indirect atmosphere-ocean-wave interactions. * Observational data are also included in this dataset (Dropsonde, HRD-Radar, NDBC_wave). All simulations are initialized at 18:00 UTC on August 19, 2021, within a domain encompassing the western North Atlantic Ocean. The atmospheric domain features a horizontal resolution of 3 km. The ocean domain, which covers a substantial portion of the WRF ocean domain, employs an unstructured triangular grid with resolutions ranging from 3 km near the coast to 9 km in the open ocean, effectively resolving the complex coastline of the U.S. Northeast Coast. Initial and boundary conditions for the atmosphere model are obtained from the 6-hourly 0.25° NCEP (National Centers for Environmental Prediction) Global Forecast System (GFS; NCEP, 2015) data. These CSV files are derived from the NetCDF files in the c0 dataset. Unlike the original format, where geographic coordinates were stored in a separate file, each CSV now embeds the corresponding latitude and longitude values alongside the measured or simulated variables.

17 WIND ENERGY↗

Automatically activated, 300 ampere-hour silver-zinc cell

A prototype silver zinc cell is reported for which the electrolyte is being stored in a separate tank; the cell is being activated when additional power is required by collapsing the neoprene bellows container and thus forcing the electrolyte into cell through a plastic connection. A solar array is proposed as main power source for the flow actuator.

Hennigan, T. J.↗

Transonic aerodynamics; Transonic Perspective Symposium, Moffett Field, CA, February 18-20, 1981, Technical Papers

After an historical account of the development of transonic aerodynamics before 1940 and in the period 1945-1975, attention is given to the design for cruise performance efficiency of commercial transport aircraft, with emphasis on transonic wing design, the practical aerodynamic problems of military aircraft, with emphasis on store carriage and separation, experimental testing in transonic wind tunnels capable of Reynolds number simulation and aerodynamic flow visualization, and such mathematical techniques as potential equation methods for flow prediction and Reynolds-averaged Navier-Stokes computations of transonic flows. Also considered are transonic design using computational aerodynamics, the application of computation methods to transonic wing design, computational experience with advanced tactical aircraft, the evaluation of full potential flow methods, and the application of a shock-turbulent boundary layer interaction theory to transonic flowfield analysis.

Nixon, D.↗

BIRP - A way to search through image engineering data

Several hundred thousand images have been returned by planetary missions and by earth-orbiting satellites. Locating specific images for study based on engineering data can be cumbersome and time consuming if done manually. BIRP, a FORTRAN program for small computers, is designed to quickly search through catalogs of image engineering data. A description of the desired images is entered and the BIRP program finds the images meeting the requirements. A videodisk player or microfiche reader may be controlled by BIRP so that images can be viewed immediately after a search. A search through 50,000 images on a single parameter takes less than 1 min on a minicomputer. Data for each parameter is stored in a separate file so that the number of files and thus the storage space required may be varied to fit computers with different storage capacities. Most modern minicomputers with FORTRAN compilers and an 8-bit byte variable type may use BIRP with minor modifications to the program.

Bolef, L. K.↗