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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 163 records · Page 9
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Using Financial Contracts to Facilitate Informal Leases Within a Western United States Water Market Based on Prior Appropriation

The ability to reallocate water to higher-value uses during drought is an increasingly important “soft-path” tool for managing water resources in an uncertain future. In most of the Western United States, state-level water market institutions that enable reallocation also impose substantial transaction costs on market participants related to regulatory approval and litigation. These transaction costs can be prohibitive for many participants in terms of both costs and lengthy approval periods, limiting transfers and reducing allocation efficiency, particularly during drought crises periods. This manuscript describes a mechanism to reduce transaction costs by adapting an existing form of informal leases to facilitate quicker and less expensive transfers among market participants. Instead of navigating the formal approval process to lease a water right, informal leases are financial contracts for conservation that enable more junior holders of existing rights to divert water during drought, thereby allowing the formal transfer approval process to be bypassed. The informal leasing approach is tested in the Upper Colorado River Basin, where drought and institutional barriers to transfers lead to frequent shortages for urban rights holders along Colorado's Front Range. Informal leases are facilitated via option contracts that include adaptive triggers and that define volumes of additional, compensatory, releases designed to mitigate impacts to instream flows and third parties. Results suggest that more rapid reallocation of water via informal leases could have resulted in up to $222 million in additional benefits for urban rights holders during the historical period 1950–2013.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2024

Project PARETO—DOE’s Produced Water Optimization Initiative

The Texas Produced Water Consortium invited Project PARETO to contribute a write-up about the project to an update report they are preparing for the Texas legislature. The article we have prepared presents a high-level overview of the PARETO produced water management framework, and includes sections dedicated to going into more detail about PARETO’s strategic model and water sharing portals (AquaShare and AquaTrade).

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

Management of the Three Mile Island Unit 2 Accident Corium and Severely Damaged Fuel Debris (Rev. 2)

The Three Mile Island, Unit Two (TMI-2) pressurized water reactor core underwent a significant meltdown in 1979 due to an untimely combination of maintenance problems that led to a loss of feedwater, followed by a series of operational misunderstandings and errors. Primary coolant discharging through a malfunctioning valve represented what was analyzed as a “small-break” loss-of-coolant accident (LOCA) and ultimately became a full core meltdown. The melted core recovery process required the development of a wide array of tools. After approximately three years of water management and other cleanup actions, the first views of the core revealed a much higher degree of damage than previously expected. Approximately 62 metric tons of the core had melted, leaving only 42 of the 177 fuel assemblies standing with fuel rods intact. The core to be recovered was composed of loose, gravel-like and granular particulate material and a central solidified mass of formerly molten fuel. Molten fuel had also penetrated some of the pressure vessel internals and resolidified below the main core support structure. Robotic tools that had been designed for the task were of limited effectiveness due to the range of material types and phases.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2022

Management of the Three Mile Island Unit 2 Accident Corium and Severely Damaged Fuel Debris (Rev. 2)

The Three Mile Island, Unit Two (TMI-2) pressurized water reactor core underwent a significant meltdown in 1979 due to an untimely combination of maintenance problems that led to a loss of feedwater, followed by a series of operational misunderstandings and errors. Primary coolant discharging through a malfunctioning valve represented what was analyzed as a “small-break” loss-of-coolant accident (LOCA) and ultimately became a full core meltdown. The melted core recovery process required the development of a wide array of tools. After approximately three years of water management and other cleanup actions, the first views of the core revealed a much higher degree of damage than previously expected. Approximately 62 metric tons of the core had melted, leaving only 42 of the 177 fuel assemblies standing with fuel rods intact. The core to be recovered was composed of loose, gravel-like and granular particulate material and a central solidified mass of formerly molten fuel. Molten fuel had also penetrated some of the pressure vessel internals and resolidified below the main core support structure. Robotic tools that had been designed for the task were of limited effectiveness due to the range of material types and phases.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2020

Asymmetric electrode ionomer for low relative humidity operation of anion exchange membrane fuel cells

The operation of fuel cells under low relative humidity (RH) conditions gives substantial cost and performance benefits. Nonetheless, it is not currently feasible to operate anion exchange membrane fuel cells (AEMFCs) at low RH conditions because current materials for membrane electrode assembly cannot provide sufficient water for the oxygen reduction reaction. In this work, we synthesized polyfluorene ionomers with different ammonium concentrations for anode and cathode to control water management. We designed several asymmetric electrodes that enable high performance under low RH conditions via not only conventional backward water diffusion (anode to cathode) but also forward diffusion (cathode to anode). The AEMFCs using optimized asymmetric electrodes exhibited high H 2 /CO 2 -free air performance (rated power density of circa 540 mW cm -2 at 90 °C under 75% (anode) and 50% RH (cathode) conditions), which is comparable to those of state-of-the-art AEMFCs under nearly water-saturated conditions. The durability of the AEMFCs is excellent, generating 0.6 A cm -2 for >900 h at 80 °C under 50% RH (cathode) conditions. This study demonstrates that high-performance and durable AEMFCs under low RH and high current generating conditions are possible.

25 ENERGY STORAGE↗
DOE OSTI2025

HydroForecast Long-term: Improving hydropower’s resilience to climate change through accurate climate-scale

With hydrologic patterns and water availability across the globe shifting due to climate change, advancements in hydrologic prediction systems can help significantly reduce the uncertainties that utilities and water supply entities have in their decision making. Understanding and estimating hydrology at the climate scale is critical for managing water resources under changing climate scenarios. This project focuses on integrating state-of-the-art neural network modeling with downscaled climate projections to deliver the reliable water supply projections decades into the future to meet an urgent need from hydropower operators and water utilities. In this Phase 1 DOE SBIR proposal, we developed and validated a theory-guided neural network model, HydroForecast Long-term, for climate-scale hydrology and implemented the model within existing HydroForecast infrastructure. HydroForecast Long-term combines the most accurate streamflow modeling system with a flexible and scalable data architecture to generate water supply projections out to the year 2100. This report illustrates that we have achieved our four objectives: 1) create a prototype of HydroForecast Long-term, building the neural network prediction model, 2) build an automated data input pipeline that processes large amounts of data from the latest global temperature and precipitation climate models; 3) benchmark the accuracy of the hydrologic model over the recent two decades over a large set of diverse basins, and 4) create a set of output visuals and summary metrics informed by customer feedback that connect the data to critical decision points. This work empowers water users to make data-informed decisions supporting a resilient, renewable-powered grid and water system. The results advance the Department of Energy’s mission by addressing critical gaps in water supply planning under climate change.

13 HYDRO ENERGY↗
DOE OSTI2025

Federal Energy Management Program

Fact sheet describes the Federal Energy Management Program and its mission, and outlines top priorities to achieve key federal agency statutory requirements for facility energy and water management.

Source record↗
DOE OSTI2025

Crop diversification improves water-use efficiency and regional water sustainability

As global water scarcity intensifies, identifying agricultural practices that enhance sustainable water management is critical. Temporal crop diversification-rotating multiple species over time-has been proposed to improve soil health and water retention based on field-scale experiments. However, widespread adoption remains limited on farms, in part due to unverified benefits at larger scales. Here, we assess the influence of crop diversification on agricultural water-use efficiency (WUE, ratio of gross primary productivity to evapotranspiration) along a spectrum of monoculture to complex species rotations in California. Leveraging new high-resolution remote sensing datasets, we show that crop diversification is a key driver of agricultural WUE, and increasing the number of species planted in the previous 6 years from two to four increases WUE by ∼20% after accounting for differences between crops. Our results provide spatially explicit, large-scale quantification of crop diversification’s improvements to WUE, with direct implications for climate adaptation. More broadly, our framework offers a tool to evaluate other sustainable practices and guide policy and farm-scale decision-making.

climate-resilient agriculture↗
DOE OSTI2021

Biogeochemistry of the Antrim Shale Natural Gas Reservoir

The Antrim Shale, located in the Michigan Basin, United States (U.S.), is a major U.S. shale play having produced over 2.5 Trillion Cubic Feet (Tcf) of unconventional shale natural gas as of 2010. The shallow nature of this formation sets it apart from other, more characterized unconventional shale gas plays. The depth of gas production of the Antrim ranges from approximately 150 to 600 m and it is typically vertically drilled, contrary to deeper, horizontally drilled shales. A thorough understanding of the biogeochemistry and microbiology of this complex system will be advantageous for improving well performance, produced water management, and potential biocidal treatment as microbial community composition can vary substantially even among closely spaced wells. In this study, we analyzed produced water collected from nine different wells in the Antrim Shale by investigating the geochemical and microbial community composition of the produced water to gain greater insight into the overall biogeochemistry of this unique shale system. The majority of the wells from this study had high total dissolved solids (TDS) primarily composed of chloride and sodium, averaging 86,804 mg/L with a maximum 116,223 mg/L; however, three of the wells sampled along the northern margin of the basin exhibited significantly lower TDS ranging from 4932 to 6496 mg/L. Overall, our microbial community analysis revealed relatively low abundance within our samples and high variability of the microbial community among the sampled wells. The majority of bacterial sequences were identified within Proteobacteria, Firmicutes, and Actinobacteria phyla and metagenomic sequencing revealed the low presence of Methanobacteriaceae within each sample. We also investigated potential microbial community drivers and found that TDS, sodium, chloride, iodide, bromide, ammonium, potassium, and strontium were significantly correlated with the observed microbial community. The varying geochemical conditions between wells demonstrate different subsurface environmental niches, potentially driving the heterogeneous microbial communities we observed from well to well. This analysis suggests an important relationship between both well location and geochemistry and the observed microbial community that can persist in the reservoir. Continued studies of the Antrim Shale will improve our understanding of the complex interdependencies of this ecosystem.

03 NATURAL GAS↗
DOE OSTI2022

Water Metering Best Practices

These best practices provide technical information to select the appropriate water meters on buildings that have been identified and to understand how data can be used and analyzed to improve water management. The guidance focuses on the installation of advanced meters and includes a process that directs agencies on how to prioritize their buildings for water meters. The process centers on metering water-intensive building types.

42 ENGINEERING↗
DOE OSTI2025

Machine Learning Prediction of Tritium‐Helium Groundwater Ages in the Central Valley, California, USA

Abstract Groundwater ages provides insight into recharge rates, flow velocities, and vulnerability to contaminants. The ability to predict groundwater ages based on more accessible parameters via Machine Learning (ML) would advance our ability to guide sustainable management of groundwater resources. In this study, ML models were trained and tested on a large data set of tritium concentrations and tritium‐helium groundwater ages from the California Central Valley, a large groundwater basin with complex land use, irrigation, and water management practices. The ML models were trained on 63 features, including location, well construction information, landscape characteristics, and climate variables, water chemistry, and stable isotopes. The Bagging regressor method can accurately classify (F1‐score = 0.91) groundwater samples as either modern or pre‐modern whereas the accuracy of the ML prediction of continuous tritium‐helium groundwater ages is limited and explains only of the variability in this data set. In general, ML groundwater age prediction relies mostly on features related to (a) the source of groundwater recharge, (b) contaminant history, (c) aquifer materials, (d) well construction, and (e) geochemical reactions along flow paths.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

HarDWR - Cumulative Water Rights Curves

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 Harmonized Water Rights Records v2.0 sourced from WestDAAT - Added "Unspecified" was a water source category, and files associated with this category 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 This product an updated version of the database used as input to the WBM model (Grogan et al. in review; Grogan et al. 2022), and is the result of the step creating cumulative water rights curves described in Lisk et al. (2024). This database contains 2,667 individual .csv files, three for each Water Management Area (WMA) in the 11-state region. File Naming WMA_[###]_[X]W.csv, where [###] is the unique identifier for each WMA, and [X] is either "S" for surface water rights, "G" for groundwater rights, or "U" for unspecified. Column headers in each file: Year: the priority date year CUML: the total cumulative water rights allocated up to this priority date year (ft3s-1) Irrigation: The percent of cumulative water rights allocated to the Irrigation category up to this priority date year (%) Domestic: The percent of cumulative water rights allocated to the Domestic category up to this priority date year (%) Livestock: The percent of cumulative water rights allocated to the Livestock category up to this priority date year (%) Fish: The percent of cumulative water rights allocated to the Fish category up to this priority date year (%) Industrial: The percent of cumulative water rights allocated to the Industrial category up to this priority date year (%) Environmental: The percent of cumulative water rights allocated to the Environmental category up to this priority date year (%) Other: The percent of cumulative water rights allocated to the Other category up to this priority date year (%) In addition to the database files, there is a companion .csv file, called stateWMAs_ID.csv. The main purpose of this file is to provide the means of translating between the [###] unique identifier and the various other id of the WMA the file is attached to. This translation file has five columns, which are: basinNum: The official state given alpha-numeric identifier of the WMA basinName: the state provided English name of the area, where applicable state: the state name uniID: a unique identifier we created by concatenating the state name, and underscore, and the state numerical ID ID: a unique numeric identifier we created as a requirement for the files to be used within WBM (Grogan et al. in review) For those whom may be interested in exploring our code more in depth, we are also making available an internal data file for convenience. The file is in .RData format and contains everything described above as well as some minor additional objects used within the code calculating the cumulative curves. For completeness, here is a detailed description of the various objects which can be found within the .RData file: states: A character vector containing the state names for those states in the study region. More importantly, the index of the state name is also the index in which that state's data can be found in the various following list objects. For example, if California is the second index in this object, the data for California will also be in the second index for each accompanying list. wmasRightsPersGrd: A list of nested lists of data frames. Each state has one entry in the top level list and consist of a list with one data frame per WMA. The data frames in this object are the same as the above described .csv files, specifically for groundwater. wmasRightsPersSur: A list of nested lists of data frames. Each state has one entry in the top level list and consist of a list with one data frame per WMA. The data frames in this object are the same as the above described .csv files, specifically for surface water. wmasRightsPersUnsp: A list of nested lists of data frames. Each state has one entry in the top level list and consist of a list with one data frame per WMA. The data frames in this object are the same as the above described .csv files, specifically for water from an unspecified source. wmasRightsTotsGrd: A list of nested lists of data frames. Each state has one entry in the top level list and consist of a list with one data frame per WMA. The values in the data frames of this object would be the equivalent to multiplying the CUML column by the sector percentage columns to create the sector total ft3s-1, specifically for groundwater. This object is provided for convenience to check out original total values, if desired. wmasRightsTotsSur: A list of nested lists of data frames. Each state has one entry in the top level list and consist of a list with one data frame per WMA. The values in the data frames of this object would be the equivalent to multiplying the CUML column by the sector percentage columns to create the sector total ft3s-1, specifically for surface water. This object is provided for convenience to check out original total values, if desired. wmasRightsTotsUnsp: A list of nested lists of data frames. Each state has one entry in the top level list and consist of a list with one data frame per WMA. The values in the data frames of this object would be the equivalent to multiplying the CUML column by the sector percentage columns to create the sector total ft3s-1, specifically for water from an unspecified source. This object is provided for convenience to check out original total values, if desired. wmaIDByState: A list of data frames which contain the by state allocation by WMA matrix. There is a matrix for each state in a different data frame within the object.

Economics↗
DOE OSTI2023

Tuning Alkaline Anion Exchange Membranes through Crosslinking: A Review of Synthetic Strategies and Property Relationships

Alkaline anion exchange membranes (AAEMs) are an enabling component for next-generation electrochemical devices, including alkaline fuel cells, water and CO2 electrolyzers, and flow batteries. While commercial systems, notably fuel cells, have traditionally relied on proton-exchange membranes, hydroxide-ion conducting AAEMs hold promise as a method to reduce cost-per-device by enabling the use of non-platinum group electrodes and cell components. AAEMs have undergone significant material development over the past two decades; however, challenges remain in the areas of durability, water management, high temperature performance, and selectivity. In this review, we survey crosslinking as a tool capable of tuning AAEM properties. While crosslinking implementations vary, they generally result in reduced water uptake and increased transport selectivity and alkaline stability. We survey synthetic methodologies for incorporating crosslinks during AAEM fabrication and highlight necessary precautions for each approach.

08 HYDROGEN↗
DOE OSTI2020

Using Resin to Reduce Silica in Borated Fluid - 20072

The Electric Power Research Institute (EPRI) strongly recommends the concentration of silica in the reactor coolant be limited, potential ingress be carefully controlled, and frequently monitored to assess potential silica deposits on fuel cladding surfaces. Boraflex is a product with silicone rubber encasing a neutron absorber for criticality control, and it was installed in the majority of nuclear power plant spent fuel pools. Over time and in the presence of radiation, the silicone rubber lining degrades into silica (SiO{sub 2}) and hydrogen gas. Silica is somewhat soluble in water and spent fuel pool water communicates with other plant systems during refueling outages, leading to a buildup of silica in the reactor coolant and connected systems. Due to the prevalence of Boraflex racks in spent fuel pools, the presence of silica is a chronic nuclear industry concern. To address this concern, many nuclear plants have installed reverse osmosis skid systems to remove the silica from their spent fuel pools and, by extension, from reactor coolant. These skids are expensive to install and maintain. Arcadis personnel demonstrated that an iron-impregnated resin could be used to remove silica from borated systems without impacting the water quality. The resin was readily available, inexpensive, and easy to install in existing plant equipment. During a nuclear power plant's refueling outage the iron-impregnated resin proved to be effective in reducing the need for feed and bleed operations, with concurrent cost savings and reduction in required outage water management activities. Thus, the following savings were realized: - Eliminated the need to purchase, install, and maintain a reverse osmosis system avoiding more than $10 million in up-front capital cost and ongoing operating and maintenance costs. - Removed 9.57 kilograms (21 pounds) of silica dioxide from the primary system fluid. - Reduced the volume of feed and bleed from 70,000 gallons (2.65 E+05 liters) to 35,000 gallons (1.32 E+05 liters). - Saved $380,000 in water production and disposal costs. (author)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2023

HarDWR - Cumulative Water Rights Curves

For a detailed description of the database of which this record is only one part, please see the HarDWR meta-record. This product is the dataset used as input to the WBM model (Grogan et al., in review; Grogan et al. 2022), and is the result of the step creating cumulative water rights curves described in Lisk et al. (submission pending). This database contains 1,744 individual .csv files, two for each Water Management Area (WMA; see here) in the 11-state region. File naming convention: WMA_[###]_[X]W.csv, where [###] is the unique identifier for each WMA, and [X] is either S for surface water rights, or G for groundwater rights. Column headers in each file: Year: the priority date year CUML: the total cumulative water rights allocated up to this priority date year (ft3s-1) Irrigation: The percent of cumulative water rights allocated to the Irrigation category up to this priority date year (%) Domestic: The percent of cumulative water rights allocated to the Domestic category up to this priority date year (%) Livestock: The percent of cumulative water rights allocated to the Livestock category up to this priority date year (%) Fish: The percent of cumulative water rights allocated to the Fish category up to this priority date year (%) Industrial: The percent of cumulative water rights allocated to the Industrial category up to this priority date year (%) Environmental: The percent of cumulative water rights allocated to the Environmental category up to this priority date year (%) Other: The percent of cumulative water rights allocated to the Other category up to this priority date year (%) In addition to the database files, there is a companion .csv file, called stateWMAs_ID.csv. The main purpose of this file is to provide the means of translating between the ### unique identifier and the various other id of the WMA the file is attached to. This translation file has five columns, which are: basinNum: The official state given alpha-numeric identifier of the WMA basinName: the state provided English name of the area, where applicable state: the state name uniID: a unique identifier we created by concatenating the state name, and underscore, and the state numerical ID ID: a unique numeric identifier we created as a requirement for the files to be used within WBM (Grogan et al., in review)

Economics↗
DOE OSTI2024

HarDWR - Cumulative Water Rights Curves

For a detailed description of the database of which this record is only one part, please see the HarDWR meta-record. This product is the dataset used as input to the WBM model (Grogan et al., in review; Grogan et al. 2022), and is the result of the step creating cumulative water rights curves described in Lisk et al. (submission pending). This database contains 1,744 individual .csv files, two for each Water Management Area (WMA; see here) in the 11-state region. File naming convention: WMA_[###]_[X]W.csv, where [###] is the unique identifier for each WMA, and [X] is either S for surface water rights, or G for groundwater rights. Column headers in each file: Year: the priority date year CUML: the total cumulative water rights allocated up to this priority date year (ft3s-1) Irrigation: The percent of cumulative water rights allocated to the Irrigation category up to this priority date year (%) Domestic: The percent of cumulative water rights allocated to the Domestic category up to this priority date year (%) Livestock: The percent of cumulative water rights allocated to the Livestock category up to this priority date year (%) Fish: The percent of cumulative water rights allocated to the Fish category up to this priority date year (%) Industrial: The percent of cumulative water rights allocated to the Industrial category up to this priority date year (%) Environmental: The percent of cumulative water rights allocated to the Environmental category up to this priority date year (%) Other: The percent of cumulative water rights allocated to the Other category up to this priority date year (%) In addition to the database files, there is a companion .csv file, called stateWMAs_ID.csv. The main purpose of this file is to provide the means of translating between the ### unique identifier and the various other id of the WMA the file is attached to. This translation file has five columns, which are: basinNum: The official state given alpha-numeric identifier of the WMA basinName: the state provided English name of the area, where applicable state: the state name uniID: a unique identifier we created by concatenating the state name, and underscore, and the state numerical ID ID: a unique numeric identifier we created as a requirement for the files to be used within WBM (Grogan et al., in review) The code related to the creation of this dataset can be viewed within HarDWR GitHub Repository/dataCumulationCurves.

Economics↗
DOE OSTI2020

Emerging technologies and radical collaboration to advance predictive understanding of watershed hydrobiogeochemistry

Increasing population and resource-intensive lifestyles are driving enhanced demands for clean water, food, and energy. In parallel, land-use change, climate change, and perturbations—including drought, floods, fires, and early snowmelt—are significantly reshaping interactions within watersheds throughout the world. While watersheds are the Earth's key functional unit for assessing and managing water resources, hydrological processes in watersheds also mediate biogeochemical interactions that support terrestrial life on Earth (Kaushal, Gold, Bernal, & Tank, 2018; National Research Council, 2012). Although society is dependent upon clean water availability, tractable prediction of watershed hydrobiogeochemical behavior, including watershed response to perturbations, remains a challenge. Central to the challenge are complex, multiscale interactions between plants, microorganisms, organic matter, minerals, dissolved constituents, and migrating fluids, which occur within and across bedrock-to-canopy compartments and along extensive lateral gradients of a watershed. Several recent community reports have synthesized formidable challenges associated with watershed science and technology (AGU, 2018; Blöschl et al., 2019). Here, we discuss emerging technologies and collaboration modes that are critical for developing generalizable insights about and predictive understanding of complex watershed hydrobiogeochemical behavior, which are important for underpinning optimized natural resource management.

54 ENVIRONMENTAL SCIENCES↗