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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 415 records · Page 23
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

Modeling Water Electrolysis in Bipolar Membranes

Bipolar membranes (BPMs) have proven useful in numerous electrochemical energy conversion and storage applications, including fuel cells and electrolyzers. However, water dissociation in bipolar membrane electrolysis cells (BPMECs) is a complicated phenomenon that occurs via several different pathways. In this work, we develop a model based on the Poisson-Nernst-Planck system that includes a multistep water-dissociation mechanism to observe the fundamental processes that contribute to BPMEC performance. The model, which is validated to in-house experimental data, demonstrates that the junction potential is the most significant contributor to the total electrolysis voltage. We investigated the effects of water-dissociation catalysts and found that the optimal catalyst $p{K}_{a}$ depends on how the catalyst is integrated into the BPM (although values near 7 are typically best, in accordance with conventional wisdom). We also simulated the water content across the BPM and found that dry-out is not a significant issue when the membrane is in contact with liquid water on both sides. The species conservation approach taken here leads to a physical understanding of the system without using any fitting parameters.

25 ENERGY STORAGE↗
DOE OSTI2019

Modeling Coupled Durability and Performance in Polymer-Electrolyte Fuel Cells: Membrane Effects

During operation, proton-exchange-membrane fuel cells (PEMFCs) are subjected to mechanical and chemical stressors that contribute to membrane degradation, performance loss, and eventual failure. Together, synergistic effects between mechanical and chemical degradation mechanisms lead to accelerated degradation. A physics-based model is developed to understand the synergistic effects of chemical and mechanical degradation and the coupled nature of performance and durability in PEMFCs. The model includes pinhole existence and growth in the membrane, which increases crossover of reactant gases as well as subsequent formation of chemical degradation agents that impact both transport and mechanical properties of the membrane. The fuel-cell model is fully coupled with a mechanical model to determine the stresses on the membrane and subsequent growth of pinholes during transient operation. Simulation results demonstrate pinhole growth under relative-humidity cycling and the resultant increased gas-crossover fluxes and decrease in polarization performance. Furthermore, the model results highlight nonlinearities and the importance of coupling mechanical and chemical degradation models in order to explain membrane degradation under various cycles, and serves as a foundation for examining coupled durability and performance.

25 ENERGY STORAGE↗
DOE OSTI2015

Computer model for characterizing, screening, and optimizing electrolyte systems

Electrolyte systems in contemporary batteries are tasked with operating under increasing performance requirements. All battery operation is in some way tied to the electrolyte and how it interacts with various regions within the cell environment. Seeing the electrolyte plays a crucial role in battery performance and longevity, it is imperative that accurate, physics-based models be developed that will characterize key electrolyte properties while keeping pace with the increasing complexity of these liquid systems. Advanced models are needed since laboratory measurements require significant resources to carry out for even a modest experimental matrix. The Advanced Electrolyte Model (AEM) developed at the INL is a proven capability designed to explore molecular-to-macroscale level aspects of electrolyte behavior, and can be used to drastically reduce the time required to characterize and optimize electrolytes. Although it is applied most frequently to lithium-ion battery systems, it is general in its theory and can be used toward numerous other targets and intended applications. This capability is unique, powerful, relevant to present and future electrolyte development, and without peer. It redefines electrolyte modeling for highly-complex contemporary systems, wherein significant steps have been taken to capture the reality of electrolyte behavior in the electrochemical cell environment. This capability can have a very positive impact on accelerating domestic battery development to support aggressive vehicle and energy goals in the 21st century.

Gering, KevinL.↗
DOE OSTI2022

Planning decentralized urban renewable energy systems using algal cultivation for closed-loop and resilient communities

To tackle climate challenges, communities need to harvest renewable energy and resources on site locally to close the loops for enhancing the resilience of communities facing unpredictable and uncertain future changes. A decentralization planning of urban renewable energy systems is proposed by treating urban waste streams and producing biomass through applying algal biotechnology. When applying algal technology as a renewable and decentralized energy source in urban systems, the overall performance can vary by levels of urban nutrients, solar and CO 2 resources, and the transportation cost when considering its application to different urban densities, urban form, and the spatial scale of urban settings. This research explores three potential impacts on the algal system’s energy performance: (1) urban density, (2) urban form in different contexts, and (3) spatial scale. The research examines the impacts by testing urban settings given in actual contexts in Atlanta, Georgia, USA. Four neighborhoods representing the high-density urban, mid-density urban, mixed suburban, and typical suburban areas are investigated. The density-scale–performance relationships are explored through testing different urban forms of neighborhoods in both hypothetical and actual neighborhood settings. A GIS-based model is developed to estimate the overall energy performance of the decentralized renewable energy system in urban environments. Results show that the energy performance is positive mainly for high-density urban neighborhoods with small-to-medium scales, up to 0.36 MJ per ton of municipal solid wastes for actual settings and 0.37 MJ for hypothetical cases. Neighborhoods with higher density have higher energy performance while up scaling has negative effects on the energy performance with a low degree of significance. Optimal scales are found as a 1-km radius in real test beds and 1.3 km in hypothetical settings, in which the results show trade-offs between scaling effects in the system efficiency gain and the transportation cost increase.

Quan, Steven Jige↗
DOE OSTI2021

Light transport with weak angular dependence in fog

Random scattering and absorption of light by tiny particles in aerosols, like fog, reduce situational awareness and cause unacceptable down-time for critical systems or operations. Computationally efficient light transport models are desired for computational imaging to improve remote sensing capabilities in degraded optical environments. To this end, we have developed a model based on a weak angular dependence approximation to the Boltzmann or radiative transfer equation that appears to be applicable in both the moderate and highly scattering regimes, thereby covering the applicability domain of both the small angle and diffusion approximations. An analytic solution was derived and validated using experimental data acquired at the Sandia National Laboratory Fog Chamber facility. The evolution of the fog particle density and size distribution were measured and used to determine macroscopic absorption and scattering properties using Mie theory. A three-band (0.532, 1.55, and 9.68 µ m) transmissometer with lock-in amplifiers enabled changes in fog density of over an order of magnitude to be measured due to the increased transmission at higher wavelengths, covering both the moderate and highly scattering regimes. The meteorological optical range parameter is shown to be about 0.6 times the transport mean free path length, suggesting an improved physical interpretation of this parameter.

74 ATOMIC AND MOLECULAR PHYSICS↗
DOE OSTI2012

Areas of Weakly Anomalous to Anomalous Surface Temperature in Routt County, Colorado, as Identified from ASTER Thermal Data

Note: This "Weakly Anomalous to Anomalous Surface Temperature" dataset differs from the "Anomalous Surface Temperature" dataset for this county (another remotely sensed CIRES product) by showing areas of modeled temperatures between 1o and 2o above the mean, as opposed to the greater than 2o temperatures contained in the "Anomalous Surface Temperature" dataset. This layer contains areas of anomalous surface temperature in Routt County identified from ASTER thermal data and spatial based insolation model. The temperature is calculated using the Emissivity Normalization Algorithm that separate temperature from emissivity. The incoming solar radiation was calculated using spatial based insolation model developed by Fu and Rich (1999). Then the temperature due to solar radiation was calculated using emissivity derived from ASTER data. The residual temperature, i.e. temperature due to solar radiation subtracted from ASTER temperature was used to identify thermally anomalous areas. Areas that had temperature between 1o and 2o were considered ASTER modeled warm surface exposures (thermal anomalies). Note: 'o' is used in this description to represent lowercase sigma.

15 GEOTHERMAL ENERGY↗
DOE OSTI2012

Areas of Weakly Anomalous to Anomalous Surface Temperature in Garfield County, Colorado, as Identified from ASTER Thermal Data

Note: This "Weakly Anomalous to Anomalous Surface Temperature" dataset differs from the "Anomalous Surface Temperature" dataset for this county (another remotely sensed CIRES product) by showing areas of modeled temperatures between 1o and 2o above the mean, as opposed to the greater than 2o temperatures contained in the "Anomalous Surface Temperature" dataset. This layer contains areas of anomalous surface temperature in Garfield County identified from ASTER thermal data and spatial based insolation model. The temperature is calculated using the Emissivity Normalization Algorithm that separate temperature from emissivity. The incoming solar radiation was calculated using spatial based insolation model developed by Fu and Rich (1999). Then the temperature due to solar radiation was calculated using emissivity derived from ASTER data. The residual temperature, i.e. temperature due to solar radiation subtracted from ASTER temperature was used to identify thermally anomalous areas. Areas that had temperature between 1o and 2o were considered ASTER modeled warm surface exposures (thermal anomalies) Note: 'o' is used in this description to represent lowercase sigma.

15 GEOTHERMAL ENERGY↗
DOE OSTI2012

Areas of Weakly Anomalous to Anomalous Surface Temperature in Dolores County, Colorado, as Identified from ASTER Thermal Data

Note: This "Weakly Anomalous to Anomalous Surface Temperature" dataset differs from the "Anomalous Surface Temperature" dataset for this county (another remotely sensed CIRES product) by showing areas of modeled temperatures between 1o and 2o above the mean, as opposed to the greater than 2o temperatures contained in the "Anomalous Surface Temperature" dataset. This layer contains areas of anomalous surface temperature in Dolores County identified from ASTER thermal data and spatial based insolation model. The temperature is calculated using the Emissivity Normalization Algorithm that separate temperature from emissivity. The incoming solar radiation was calculated using spatial based insolation model developed by Fu and Rich (1999). Then the temperature due to solar radiation was calculated using emissivity derived from ASTER data. The residual temperature, i.e. temperature due to solar radiation subtracted from ASTER temperature was used to identify thermally anomalous areas. Areas that had temperature greater than 2o were considered ASTER modeled very warm surface exposures (thermal anomalies) Note: 'o' is used in this description to represent lowercase sigma.

15 GEOTHERMAL ENERGY↗
DOE OSTI2012

Areas of Weakly Anomalous to Anomalous Surface Temperature in Chaffee County, Colorado, as Identified from ASTER Thermal Data

Note: This "Weakly Anomalous to Anomalous Surface Temperature" dataset differs from the "Anomalous Surface Temperature" dataset for this county (another remotely sensed CIRES product) by showing areas of modeled temperatures between 1o and 2o above the mean, as opposed to the greater than 2o temperatures contained in the "Anomalous Surface Temperature" dataset. This layer contains areas of anomalous surface temperature in Chaffee County identified from ASTER thermal data and spatial based insolation model. The temperature is calculated using the Emissivity Normalization Algorithm that separate temperature from emissivity. The incoming solar radiation was calculated using spatial based insolation model developed by Fu and Rich (1999). Then the temperature due to solar radiation was calculated using emissivity derived from ASTER data. The residual temperature, i.e. temperature due to solar radiation subtracted from ASTER temperature was used to identify thermally anomalous areas. Areas that had temperature greater than 2o were considered ASTER modeled very warm surface exposures (thermal anomalies). Note: 'o' is used in this description to represent lowercase sigma.

15 GEOTHERMAL ENERGY↗
DOE OSTI2012

Areas of Weakly Anomalous to Anomalous Surface Temperature in Archuleta County, Colorado, as Identified from ASTER Thermal Data

Note: This "Weakly Anomalous to Anomalous Surface Temperature" dataset differs from the "Anomalous Surface Temperature" dataset for this county (another remotely sensed CIRES product) by showing areas of modeled temperatures between 1o and 2o above the mean, as opposed to the greater than 2o temperatures contained in the "Anomalous Surface Temperature" dataset. This layer contains areas of anomalous surface temperature in Archuleta County identified from ASTER thermal data and spatial based insolation model. The temperature is calculated using the Emissivity Normalization Algorithm that separate temperature from emissivity. The incoming solar radiation was calculated using spatial based insolation model developed by Fu and Rich (1999). Then the temperature due to solar radiation was calculated using emissivity derived from ASTER data. The residual temperature, i.e. temperature due to solar radiation subtracted from ASTER temperature was used to identify thermally anomalous areas. Areas that had temperature between 1o and 2o were considered ASTER modeled warm surface exposures (thermal anomalies). Note: 'o' is used in this description to represent lowercase sigma.

15 GEOTHERMAL ENERGY↗
DOE OSTI2012

Areas of Anomalous Surface Temperature in Routt County, Colorado, as Identified from ASTER Thermal Data

This layer contains areas of anomalous surface temperature in Routt County identified from ASTER thermal data and spatial based insolation model. The temperature is calculated using the Emissivity Normalization Algorithm that separate temperature from emissivity. The incoming solar radiation was calculated using spatial based insolation model developed by Fu and Rich (1999). Then the temperature due to solar radiation was calculated using emissivity derived from ASTER data. The residual temperature, i.e. temperature due to solar radiation subtracted from ASTER temperature was used to identify thermally anomalous areas. Areas that had temperature greater than 2o were considered ASTER modeled very warm surface exposures (thermal anomalies). Note: 'o' is used in this description to represent lowercase sigma.

15 GEOTHERMAL ENERGY↗
DOE OSTI2012

Areas of Anomalous Surface Temperature in Garfield County, Colorado, as Identified from ASTER Thermal Data

This layer contains areas of anomalous surface temperature in Garfield County identified from ASTER thermal data and spatial based insolation model. The temperature is calculated using the Emissivity Normalization Algorithm that separate temperature from emissivity. The incoming solar radiation was calculated using spatial based insolation model developed by Fu and Rich (1999). Then the temperature due to solar radiation was calculated using emissivity derived from ASTER data. The residual temperature, i.e. temperature due to solar radiation subtracted from ASTER temperature was used to identify thermally anomalous areas. Areas that had temperature greater than 2o were considered ASTER modeled very warm surface exposures (thermal anomalies). Note: 'o' is used in this description to represent lowercase sigma.

15 GEOTHERMAL ENERGY↗
DOE OSTI2012

Areas of Anomalous Surface Temperature in Dolores County, Colorado, as Identified from ASTER Thermal Data

This layer contains areas of anomalous surface temperature in Dolores County identified from ASTER thermal data and spatial based insolation model. The temperature is calculated using the Emissivity Normalization Algorithm that separate temperature from emissivity. The incoming solar radiation was calculated using spatial based insolation model developed by Fu and Rich (1999). Then the temperature due to solar radiation was calculated using emissivity derived from ASTER data. The residual temperature, i.e. temperature due to solar radiation subtracted from ASTER temperature was used to identify thermally anomalous areas. Areas that had temperature greater than 2o were considered ASTER modeled very warm surface exposures (thermal anomalies). Note: 'o' is used in this description to represent lowercase sigma

15 GEOTHERMAL ENERGY↗
DOE OSTI2012

Areas of Anomalous Surface Temperature in Chaffee County, Colorado, as Identified from ASTER Thermal Data

This layer contains areas of anomalous surface temperature in Chaffee County identified from ASTER thermal data and spatial based insolation model. The temperature is calculated using the Emissivity Normalization Algorithm that separate temperature from emissivity. The incoming solar radiation was calculated using spatial based insolation model developed by Fu and Rich (1999). Then the temperature due to solar radiation was calculated using emissivity derived from ASTER data. The residual temperature, i.e. temperature due to solar radiation subtracted from ASTER temperature was used to identify thermally anomalous areas. Areas that had temperature greater than 2o were considered ASTER modeled very warm surface exposures (thermal anomalies). Note: 'o' is used in this description to represent lowercase sigma

15 GEOTHERMAL ENERGY↗
DOE OSTI2012

Areas of Anomalous Surface Temperature in Archuleta County, Colorado, as Identified from ASTER Thermal Data

This layer contains areas of anomalous surface temperature in Archuleta County identified from ASTER thermal data and spatial based insolation model. The temperature is calculated using the Emissivity Normalization Algorithm that separate temperature from emissivity. The incoming solar radiation was calculated using spatial based insolation model developed by Fu and Rich (1999). Then the temperature due to solar radiation was calculated using emissivity derived from ASTER data. The residual temperature, i.e. temperature due to solar radiation subtracted from ASTER temperature was used to identify thermally anomalous areas. Areas that had temperature greater than 2o were considered ASTER modeled very warm surface exposures (thermal anomalies). Note: 'o' is used in this description to represent lowercase sigma

15 GEOTHERMAL ENERGY↗
DOE OSTI2012

Areas of Anomalous Surface Temperature in Alamosa and Saguache Counties, Colorado, as Identified from ASTER Thermal Data

This layer contains areas of anomalous surface temperature in Alamosa and Saguache Counties identified from ASTER thermal data and spatial based insolation model. The temperature is calculated using the Emissivity Normalization Algorithm that separate temperature from emissivity. The incoming solar radiation was calculated using spatial based insolation model developed by Fu and Rich (1999). Then the temperature due to solar radiation was calculated using emissivity derived from ASTER data. The residual temperature, i.e. temperature due to solar radiation subtracted from ASTER temperature was used to identify thermally anomalous areas. Areas that had temperature greater than 2o were considered ASTER modeled very warm surface exposures (thermal anomalies) Note: 'o' is used in this description to represent lowercase sigma.

15 GEOTHERMAL ENERGY↗
DOE OSTI2012

Surface Temperature Anomalies Derived from Night Time ASTER Data Corrected for Solar and Topographic Effects, Alamosa and Saguache Counties, Colorado

This map shows areas of anomalous surface temperature in Alamosa and Saguache Counties identified from ASTER thermal data and spatial based insolation model. The temperature is calculated using the Emissivity Normalization Algorithm that separate temperature from emissivity. The incoming solar radiation was calculated using spatial based insolation model developed by Fu and Rich (1999). Then the temperature due to solar radiation was calculated using emissivity derived from ASTER data. The residual temperature, i.e. temperature due to solar radiation subtracted from ASTER temperature was used to identify thermally anomalous areas. Areas that had temperature greater than 2o were considered ASTER modeled "very warm modeled surface temperature" are shown in red on the map. Areas that had temperatures between 1o and 2o were considered ASTER modeled "warm modeled surface temperature" are shown in yellow on the map. This map also includes the locations of shallow temperature survey points, locations of springs or wells with favorable geochemistry, faults, transmission lines, and areas of modeled basement weakness "fairways." Note: 'o' is used in this description to represent lowercase sigma.

15 GEOTHERMAL ENERGY↗
DOE OSTI2012

Surface Temperature Anomalies Derived from Night Time ASTER Data Corrected for Solar and Topographic Effects, Archuleta County

This map shows areas of anomalous surface temperature in Alamosa and Saguache Counties identified from ASTER thermal data and spatial based insolation model. The temperature is calculated using the Emissivity Normalization Algorithm that separate temperature from emissivity. The incoming solar radiation was calculated using spatial based insolation model developed by Fu and Rich (1999). Then the temperature due to solar radiation was calculated using emissivity derived from ASTER data. The residual temperature, i.e. temperature due to solar radiation subtracted from ASTER temperature was used to identify thermally anomalous areas. Areas that had temperature greater than 2o were considered ASTER modeled "very warm modeled surface temperature" are shown in red on the map. Areas that had temperatures between 1o and 2o were considered ASTER modeled "warm modeled surface temperature" are shown in yellow on the map. This map also includes the locations of shallow temperature survey points, locations of springs or wells with favorable geochemistry, faults, transmission lines, and areas of modeled basement weakness "fairways." Note: 'o' is used in this description to represent lowercase sigma.

15 GEOTHERMAL ENERGY↗