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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 973 records · Page 54

Polymer-Based Thermally Stable Chemiresistive Sensor for Real-Time Monitoring of NO 2 Gas Emission

Here, we present a thermally stable, mechanically compliant, and sensitive polymer-based NO 2 gas sensor design. Interconnected nanoscale morphology driven from spinodal decomposition between conjugated polymers tethered with polar side chains and thermally stable matrix polymers offers judicious design of NO 2 -sensitive and thermally tolerant thin films. The resulting chemiresitive sensors exhibit stable NO 2 sensing even at 170 °C over 6 h. Controlling the density of polar side chains along conjugated polymer backbone enables optimal design for coupling high NO 2 sensitivity, selectivity, and thermal stability of polymer sensors. Lastly, thermally stable films are used to implement chemiresistive sensors onto flexible and heat-resistant substrates and demonstrate a reliable gas sensing response even after 500 bending cycles at 170 °C. Such unprecedented sensor performance as well as environmental stability are promising for real-time monitoring of gas emission from vehicles and industrial chemical processes.

47 OTHER INSTRUMENTATION↗

Life Cycle Greenhouse Gas Emissions of Biogas Upgrading for Fuel Production

Waste-to-Renewable Natural Gas (RNG) offers a promising solution to alleviating waste management challenges by converting waste into renewable fuels. Here, this process can significantly reduce greenhouse gas (GHG) emissions, as demonstrated through a comprehensive life cycle analysis. Biogas upgrading is essential to enhance the methane concentration, though it could be energy-intensive and susceptible to methane slippage. Four commonly adopted biogas upgrading technologies, including pressure swing adsorption, membrane separation, chemical absorption, and water scrubbing, are considered. Our study evaluates the life cycle GHG emissions of RNG production from major sources of waste in the U.S. including wastewater sludge, food waste, landfill gas, dairy cow manure, and swine manure. Meta-analysis was conducted to assess methane slippage and energy consumption of biogas upgrading and associated GHG emissions, while accounting for potential avoided emissions from conventional waste management, which vary widely (ranging from −481.0 to 101.8 g CO 2 -eq/MJ). Under default upstream assumptions, representative carbon intensity of RNG varies from about −125 g of CO 2 -eq/MJ (dairy cow manure) to about 41 g of CO 2 -eq/MJ (wastewater sludge). We also explored RNG applications in producing hydrogen, ammonia, and compressed/liquefied forms. These findings highlight the potential of RNG and RNG-derived fuels to reduce GHG emissions and bolster the U.S. energy supply.

Biogas upgrades↗

Discriminating Underground Nuclear Explosions Leading To Late-Time Radionuclide Gas Seeps

Utilizing historical data from the U.S. nuclear test program and freely available barometric pressure data, we performed an analytical barometric-pumping efficiency analysis to determine factors resulting in late-time radionuclide gas seeps from underground nuclear explosions. In this work, we considered 16 underground nuclear explosions with similar geology and test setup, of which five resulted in the measurement of late-time radionuclide gas concentrations at the ground surface. Additionally, the factors we considered include barometric frequency and amplitude, depth of burial, air-filled porosity, intact-rock permeability, fracture aperture, and fracture spacing. The analysis indicates that the best discriminators of late-time radionuclide gas seeps for these explosions are barometric frequency and amplitude and air-filled porosity. While geologic information on fracture aperture and spacing is not available for these explosions, the sensitivity of barometric-pumping efficiency to fracture aperture indicates that it would likely also be a good discriminator.

58 GEOSCIENCES↗

Investigation of flow regimes in arc plasma–gas interactions using a two-temperature arc in crossflow model

The perpendicular impingement of a gas stream on an electric arc, a configuration known as the arc in crossflow, is of primary relevance in the study of plasma-gas interactions, as well as in industrial applications such as circuit breakers and wire-arc spraying. The flow dynamics in the arc in crossflow are the result of coupled fluid-thermal-electromagnetic phenomena accompanied by large property gradients, which can produce significant deviations from Local Thermodynamic Equilibrium (LTE) among electrons and gas species. These characteristics can lead to the establishment of distinct flow regimes depending on the relative values of the controlling parameters of the system, such as inflow velocity, arc current, and inter-electrode spacing. A two-temperature non-LTE (NLTE) model is used to investigate the arc dynamics and the establishment of flow regimes in the arc in crossflow. The plasma flow model is implemented within a nonlinear Variational Multiscale (VMS) numerical discretization approach that is less dissipative, and hence better suited to capture unstable behavior, than traditional VMS methods commonly used in computational fluid dynamics simulations. The Reynolds and the Enthalpy dimensionless numbers, characterizing the relative flow strength and arc strength, respectively, are chosen as the controlling parameters of the system. Simulation results reveal the onset of dynamic behavior and the establishment of steady, periodic, quasi-periodic, and chaotic or potentially turbulent, regimes, as identified by distinct spatiotemporal fluctuations. The computational findings reveal the role of increasing the relative arc strength on enhancing flow stability by delaying the growth of fluctuating and unstable flow behavior.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

Direct measurement of thermal Knudsen forces in rarefied gas environments

At micro- and nanoscales, momentum transfer between surfaces is influenced by various physical mechanisms, including quantum fluctuations, electromagnetic interactions, electric charges, and the dynamics of (rarefied) gases. Under non-isothermal conditions, rarefied gases give rise to thermal Knudsen forces whose magnitudes strongly depend on the gas species and surface characteristics. Knudsen forces are particularly relevant in nanotechnology, optical manipulation, and aerospace systems, where gas rarefaction occurs due to highly confined geometries, sub-micrometer length scales, and reduced particle densities. Despite their significance, predictive modeling of Knudsen forces is limited by a lack of comprehensive experimental data across diverse materials and surface morphologies. Here, in this work, we present a highly sensitive and adaptable measurement platform capable of directly quantifying Knudsen forces using a suspended, interchangeable micro-cantilever within controlled rarefied helium and nitrogen environments. The system integrates optical fiber interferometry to precisely capture out-of-plane displacements at sub-micrometer resolution, driven by Knudsen forces. From the empirical data, we derive a robust correlation linking the magnitudes of Knudsen forces to energy accommodation coefficients, offering deeper insights into the underlying gas–surface interaction mechanisms.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

RELAP5-3D Capabilities for High-Temperature Gas-Cooled Reactor Analysis

RELAP5-3D is the premier systems thermal-hydraulic code in the world. The code has been used significantly for high-temperature gas-cooled reactor (HTGR) analyses over the last 10 years, but there has not been a comprehensive summary of RELAP5-3D capabilities and methods for HTGR modeling. In this paper, we provide a guide for how novice users can build models for prismatic HTGRs. This guide includes best practices for developing heat structures and setting up radiation and conduction heat transfer. We also provide a summary of relevant validation studies. These studies are divided into three categories: separate effects tests, integral effects tests, and nuclear reactor studies. We present a comprehensive review of RELAP5-3D HTGR applications for reactor modeling over the last 10 years. These applications include work for the Modular High-Temperature Gas-Cooled Reactor benchmark, design activities for a prismatic HTGR as part of the U.S. Department of Energy’s Advanced Demonstration and Test Reactor Options Study, and studies performed to design gas-cooled microreactors. Following the comprehensive literature review, we present a gap analysis highlighting capabilities of RELAP5-3D that are available and missing for a complete analysis of HTGR thermal hydraulics. We also discuss the limitations of existing code capabilities and potential improvements that could improve analyses. Current research efforts have yet to fully validate RELAP5-3D for a prismatic HTGR analysis; however, some of this is attributable to confounding factors in the available experimental data as opposed to code capabilities. In conclusion, this paper presents the most comprehensive review to date on RELAP5-3D capabilities, limitations, and applications for HTGR analysis.

22 - GENERAL STUDIES OF NUCLEAR REACTORS↗

Impurity gas detection for SNF canisters using probabilistic deep learning and acoustic sensing *

Abstract Monitoring impurity gases in spent nuclear fuel (SNF) canisters is a novel structural health monitoring approach for SNF in dry storage. The SNF canisters are sealed containers that do not facilitate visual access to the inside. Acoustic sensing can be deployed by taking advantage of the pathways unobstructed by internal hardware. Although the ultrasonic time-of-flight measurement can provide valuable information, it is limited in its ability to discern the concentration of only one impurity gas. As such, deep learning algorithms, particularly convolutional neural networks (CNNs), offer a promising solution. In this study, CNN-based probabilistic deep learning models were implemented to detect and quantify multiple impurity gases in helium. An experimental platform was established to simulate canister conditions, and ultrasonic test data were collected. The presence of argon and air in helium at concentrations ranging from 0% to 1.2% at increments of 0.05% was considered. The multi-layer perceptron, decision tree, and logistic regression classifiers achieved high accuracies when distinguishing pure helium from helium with impurities. CNN with dropout layers and CNN using maximum likelihood estimation showed a similar performance, indicating their ability to capture uncertainties. The ensemble CNN model exhibited improved predictions and the ability to balance individual gas concentration by integrating 1D- and 2D-CNN models. These findings contribute probabilistic deep learning solutions for impurity gas detection and analysis within SNF canisters, thus ensuring safe storage and management of SNFs.

47 OTHER INSTRUMENTATION↗

Monitoring natural gas storage using Synthetic Aperture Radar: are the residuals informative?

Estimates of line-of-sight (LOS) displacements from Interferometric Synthetic Aperture Radar (InSAR) observations serve as the basis of the long-term monitoring of an operating natural gas storage site at Honor Rancho in California. Here, an inversion algorithm is used to estimate the portion of the signal that is attributable to deformation within the gas storage reservoir, located at a depth of around 3 km. Removing this contribution produces residuals that are used to characterize the background variation is surface deformation at the gas storage facility and to determine a threshold that can signify unusually large residuals. An application to almost 7 yr of InSAR data, from 2011 until 2018, indicates that there are intervals of heightened residuals as well as brief episodes of anomalously large misfits. An examination of the spatial distributions of the individual residual LOS displacements indicates larger displacements in an alluvial valley just south of the reservoir, with rapid spatial variations in sign, indicating a rather shallow origin. Furthermore, the two anomalous events also involve rapid spatial variations in the LOS displacement residuals directly above the storage facility. The results demonstrate that the technique of extracting residuals after removing the reservoir signal is a useful approach, even in the case of this deep reservoir, and is a promising method for long-term monitoring.

03 NATURAL GAS↗

Opportunities for Clean Energy in Natural Gas Well Operations

The oil and gas industry is increasingly seeking operational improvements to reduce both costs and emissions while improving resilience against electric grid outages. This study describes techno-economic analysis of opportunities for distributed energy generation and storage technologies to support companies’ energy cost savings, clean energy, and energy resiliency goals. Specifically, the analysis evaluates solar photovoltaics (PV), distributed wind energy, and battery energy storage at hypothetical upstream well sites in the Marcellus Shale in Pennsylvania, both grid-connected and off-grid. Results indicate opportunity for solar PV to reduce operational costs. Additionally, these technologies reduce the site’s consumption of grid electricity and natural gas and thus can help reduce Scope 1 and 2 emissions associated with electricity and natural gas consumption. For each emissions reduction scenario, a cost of avoided emissions was calculated; these values can be compared to internal organizational value placed on emissions reductions, compared to other emissions reduction strategies such as energy efficiency, reducing flaring, and direct carbon capture and sequestration, and compared to existing (albeit limited) U.S. carbon markets such as California’s Low Carbon Fuel Standard. Results indicate that the associated costs of emissions reductions via distributed renewables are competitive with these options and markets. The study also explores the ability of these electric clean energy technologies to support site resiliency against utility outages.

42 ENGINEERING↗

A Machine Learning-Based Vulnerability Analysis for Cascading Failures of Integrated Power-Gas Systems

This article proposes a cascading failure simulation (CFS) method and a hybrid machine learning method for vulnerability analysis of integrated power-gas systems (IPGSs). The CFS method is designed to study the propagating process of cascading failures between the two systems, generating data for machine learning with initial states randomly sampled. The proposed method considers generator and gas well ramping, transmission line and gas pipeline tripping, island issue handling and load shedding strategies. Then, a hybrid machine learning model with a combined random forest (RF) classification and regression algorithms is proposed to investigate the impact of random initial states on the vulnerability metrics of IPGSs. Extensive case studies are carried out on three test IPGSs to verify the proposed models and algorithms. Simulation results show that the proposed models and algorithms can achieve high accuracy for the vulnerability analysis of IPGSs.

24 POWER TRANSMISSION AND DISTRIBUTION↗

Predictions of Cell-to-Cell Propagation and Vent Gas Production in the Thermal Runaway of Lithium-Ion Battery Stacks

This work presents the thermal runaway propagation model LIM1TR (Lithium-ion Modeling with 1-D Thermal Runaway) as an efficient tool to predict different cell-to-cell thermal runaway propagation scenarios. Here, we explored the vent gas volume production and reaction duration highlighting the relationship between these parameters and thermal runaway propagation due to convection by the vented gases. Two metrics based on gas production rate and heating rate are utilized as good indicators of the start and end of thermal runaway. LIM1TR results are compared with and validated by experiments from the literature for single-cell and multicell array experiments of 5 Ah and 10 Ah cells. By accounting for intraparticle diffusion of reacting species in the electrodes, we were able to capture the general dynamics of thermal runaway propagation and estimate acceptable reaction durations compared with the experimental values. Simulation results further demonstrated that varying heating modes lead to distinct reaction durations, consistent with experimental observations. Vent gas volume predictions indicate the need to consider both full and partial oxidation of the electrolyte. The outcomes of this work are building blocks for further investigations of module-to-module propagation by vented gases through convective heat transfer.

25 ENERGY STORAGE↗

Changuinola peat soil characteristics and gas emission raw data October 2019

This dataset comprises radiocarbon and geochemical measurements from peat and porewater samples collected across various depths at a site in Bocas del Toro, Panama. The study focuses on carbon cycling dynamics in tropical peatlands by examining carbon isotopic signatures (¹⁴C and ¹³C) and elemental compositions of bulk peat, dissolved organic carbon (DOC), carbon dioxide (CO₂), and methane (CH₄). Key parameters include radiocarbon ages and isotopic ratios (δ¹³C) of bulk peat, concentrations of carbon (%C) and nitrogen (%N), and radiocarbon content of porewater gases and dissolved organic carbon (DOC). The data provide insights into the vertical and spatial distribution of carbon sources and possible preservation and decomposition processes within tropical peat profiles, offering critical information for understanding carbon storage and greenhouse gas emissions in these ecosystems.This dataset is comprised of one main data folder containing (1) file-level metadata; (2) data dictionary; (3) field metadata; (4) carbon isotopic signatures (¹⁴C and ¹³C); (5) concentrations of carbon (%C) and nitrogen (%N); (6) radiocarbon content of porewater carbon dioxide (CO₂), and methane (CH₄) ; (7) porewater DOC; (8) bulk peat sampling protocol; (9) porewater sampling protocol; (10) porewater gas collection methods; and (11) gas extraction methods. All files are in .csv format and can be opened with any software that supports this file types.

54 ENVIRONMENTAL SCIENCES↗

2020 natural gas LCA appendices Rev1

This collection is the data-centric appendices for the report Life Cycle Analysis of Natural Gas Extraction and Power Generation: U.S. 2020 Emissions Profile. It consists of Appendix A: Additional Modeling Parameters [spreadsheet]; Appendix B: Water Burdens [spreadsheet]; Appendix D: Simulation of Liquids Unloading [python script and spreadsheet]; Appendix E: Detailed GHG Results for All Scenarios [spreadsheet]; Appendix F: Full Inventory Results [spreadsheet]; and Appendix I: Stage-Level Natural Gas Loss and Consumption Rates [spreadsheet]. These results have been updated from the previous version (https://edx.netl.doe.gov/dataset/2020-natural-gas-lca-data-appendices) to correct a modeling error where the same post-processing natural gas composition was used instead of the intended regional compositions.

Appendices↗

Characterizing Baselines and Change in Gas Hydrate Systems using EM Methods

The objective of this project was to advance our understanding of gas hydrate systems in nature by characterizing their electrical properties in the field and in the laboratory. In the laboratory measurements, methane hydrate was synthesized from pure water ice and flash frozen seawater, with varying amounts of sand or silt added. Electrical conductivity was determined by impedance spectroscopy, using equivalent circuit modeling to separate the effects of electrodes and to gain insight into conduction mechanisms. Silt and sand increase the conductivity of pure hydrate, inferred to be contaminant NaCl contributing to conduction in hydrate, to a peak conductivity in agreement with peak resistivities observed in well logs through massive hydrate (3,000--10,000 Ωm). The addition of silt and sand lowers the conductivity of hydrate synthesized from seawater, by an amount consistent with Archie's Law. All samples were characterized using cryogenic scanning electron microscopy and energy dispersive spectroscopy, which shows good connectivity of salt and brine phases. Electrical conductivity measurements of pure hydrate and hydrate mixed with silt during pressure-induced dissociation supports previous conclusions that sediment increases dissociation rate. In order to characterize gas hydrate systems in the field, we collected 360 line kilometers of controlled-source electromagnetic data on Walker Ridge 313, Orca Basin (WR100), Mad Dog (GC781), and Green Canyon 955 in the Gulf of Mexico, all areas with known or seismically inferred gas hydrate deposits and which have be drilled or targeted for future drilling. We deep-towed an EM transmitter that generates an alternating electric field which propagates through the seafloor geology. Data were recorded on 6 receivers towed behind the transmitter at distances between 550 and 1550 m. In the presence of conductive geology, the electric fields will be attenuated, and conversely, in resistive geology the fields will be preserved. Our data were inverted using a 2D inversion method that first optimizes the model-data misfit, then finds the smoothest model fitting the data. This ensures that resistivity structures present in the final model are likely necessary. At each of the proposed drilling sites we found increased resistivity, interpreted as increased hydrate concentrations. However, not only were the primary drilling sites not always more resistive than the alternate sites, at WR313 the strongest resistors were not at the locations targeted for drilling.

03 NATURAL GAS↗

Expanding the Knock/Emissions/Misfire Limits for the Realization of Ultra-Low Emissions, High Efficiency Heavy Duty Natural Gas Engines

The Vehicle Technologies Office funds early-stage, high-risk research to develop technology upon which industry can implement innovative transportation energy technologies that increase efficiency, decrease cost, and expand use of domestic energy sources, such as natural gas (NG). To improve the viability of medium and heavy-duty vehicles the natural gas engine efficiency must be closer to that of diesel. This work addresses fundamental limitations to achieve diesel-like efficiencies in heavy duty on-road natural gas engines. The primary goal of this research is to achieve diesel-like efficiencies in heavy duty on-road NG engines, focusing on the Cummins 15 liter engine platform (baseline diesel efficiency of 44% at peak torque). That goal has been achieved, though more work is needed to facilitate practical implementation of the developed technologies.

03 NATURAL GAS↗

Integrated TBC/EBC for SiC Fiber Reinforced SiC Matrix Composites for Next Generation Gas Turbines (Final Report)

In this ambitious project, a significant challenge preventing widespread use of ceramic matrix composites in hot section components of gas power generating turbines was addressed. Hot section components made from SiC fiber reinforced SiC matrix composites (SiC/SiC composites), have the potential to achieve a combined cycle energy efficiency of above 65%. To achieve this efficiency, the temperature of the combustion gases on the turbine component surface is expected to reach as high as ~1700ºC (3100ºF). However, SiC/SiC composites suffer from volatilization at high temperature under high velocity steam environment – typical in a turbine hot chamber. This integrated collaborative project, between two research groups at Clemson University and a team of engineers and scientists at GE Gas Power, focused on design, fabrication and evaluation of a new graded environmental barrier coating. Using the excellent compositional tailorability of polymer derived ceramics (PDCs), we developed an integrated EBC/BC that smoothly transitions from bottom SiC/SiC bond interface to hermetic sesquioxide (Y 2 O 3 ) top surface. This smooth compositional transition avoided the sharp CTE mismatch at interfaces to greatly reduce the thermal stress. The developed EBC is compatible with and bonds well with the bond coat that GE uses. The top hermetic oxide surface ensures low oxygen transportation and volatilization rates under the high velocity steam environment. The graded microstructure with increasing sesquioxide concentration also decreases the amount of Si and C elements near the top hermetic sesquioxide layer. Coatings made using a process suitable for manufacturing – atmospheric plasma spraying was demonstrated. The microstructure of coatings made using this process were fully characterized. Finally, the performance of these coatings was investigated under the most demanding conditions for this application. Specifically, high temperature, high velocity steam exposure; thermal shock test; and thermal cycling tests. The graded coatings performed exceptionally well in all these tests. Specific suggestions for future development in this area have also been identified. In addition to the excellent technical progress, this project helped initiate a robust collaboration between the research groups at Clemson and GE Gas Power. It also provided a high-quality learning and mentoring opportunity for the three post-doctoral research associates and two UG students at Clemson University who worked on this project. All proposed tasks, sub-tasks were completed, and all milestones achieved.

03 NATURAL GAS↗

Evaluating the Impact of Proprietary Oil & Gas Data on Machine Learning Model Performance Using a Quasi-Experimental Analytical Approach

This study implements a data-intensive supervised ML approach through a quasi-experimental framework with the objective of quantifying the impact of oil and gas operator-specific proprietary data on ML-based predictive model performance relative to using oil and gas datasets that may be more commonly publicly available. The models are designed to jointly predict daily oil, gas, and water production for horizontal wells as a function of bottom-hole pressure drawdown, spatial placement across the study domain, and well completion attributes. Model performance is quantified on holdout test data to evaluate how each dataset affects resulting model variant performance.

02 PETROLEUM↗

Techno-Economic Analysis of Natural Gas Fuel Cell Plant Configurations

The United States (U.S.) Department of Energy (DOE) Office of Fossil Energy and Carbon Management (FECM) and the National Energy Technology Laboratory (NETL) have been pursuing the development of solid oxide fuel cell (SOFC) technology to enable future power generation systems that are consistent with the cornerstones of the DOE mission—to ensure America’s security and prosperity by addressing its energy and environmental challenges through transformative science and technology solutions. The U.S. DOE FECM SOFC Program is currently focused on the development of low-cost, highly efficient, and reliable SOFC power systems. NETL’s SOFC technology development roadmap is aligned with near-term market opportunities in the distributed generation sector to validate and advance the technology while paving the way for utility-scale (> 50 MW) natural gas and coal-derived synthesis gas-fueled applications via progressively larger system demonstrations. The present study represents a part of a series of system evaluations being developed at NETL to aid in prioritizing technological advances along research pathways to the realization of utility-scale SOFC systems, a transformational goal of the fuel cell program. In particular, the system performance of utility-scale natural gas fuel cell (NGFC) systems with and without carbon dioxide (CO 2 ) capture is presented. The objective of the study is to provide targeted research and development (R&D) guidance to the FECM SOFC Program and SOFC commercial developers to accelerate technology deployment.

03 NATURAL GAS↗