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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 145 records · Page 8
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Salt Diapir‐Driven Recycling of Gas Hydrate

Abstract By harnessing both hypothetical, synthetic basin and gas hydrate (GH) system models and real‐world models of well‐studied salt diapir‐associated GH sites at Green Canyon (Gulf of Mexico) and Blake Ridge (U.S. Atlantic coast), we propose and demonstrate salt movement (and in particular, diapirism) to be a new mechanism for the recycling of marine GH. At Green Canyon, for example, we show that by considering this newly proposed diapir‐driven recycling mechanism in conjunction with previously proposed lithological control on sandy‐reservoir‐hosted hydrate at the base of the GH stability zone (BGHSZ; ∼bottom‐simulating reflector, BSR), modeled GH saturations match drilling data. Overall, salt diapir movement‐induced GH recycling provides a temperature‐driven mechanism by which GH saturations at the BGHSZ may reach >90 vol. % and by which GH volumes near and free gas volumes beneath the BGHSZ may be increased significantly through time. Interestingly, comparison of salt diapir‐driven recycling and sediment burial‐driven recycling scenarios suggests notably higher rates of recycling via diapir‐driven versus burial‐driven processes. Our results suggest that GH and associated free gas accumulations above salt diapir crests represent particularly attractive targets for unconventional and conventional hydrocarbon resource exploration and for scientific and academic drilling expeditions aimed at exploiting GH systems. Salt basins containing GH systems—including passive margin basins of the Gulf of Mexico, southeastern Brazil, and southwestern Africa—are therefore compelling localities for studying salt‐driven GH recycling and for salt diapir‐associated natural gas exploration.

58 GEOSCIENCES↗
DOE OSTI2024

Decomposition characteristics of C4F7N-based SF6-alternative gas mixtures

C4F7N [2,3,3,3-tetrafluoro-2-(trifluoromethyl)propanenitrile]/CO2 gas mixtures are being developed as an eco-friendly electrical insulator to replace SF6, the most potent greenhouse industrial gaseous dielectric. However, recent studies have reported complicated and often conflicting decomposition pathways for C4F7N/CO2 gas mixtures, which has raised concerns. In this work, the decomposition characteristics of C4F7N/CO2 gas mixtures were studied comprehensively by both designed computations and experiments. Computations were performed starting from fundamental propositions of C4F7N/CO2 decompositions, which were further experimentally verified by pyrolysis, long-term thermal aging with/without catalytic materials (industrial-grade molecular sieves 4A), and electrical decomposition by spark discharge. The results of both computations and experiments suggest that in an ideal thermal decomposition, C4F7N is likely to decompose into C2F6 and small fluoronitriles first at high temperatures. The generation of C3F6 and C2N2 from C4F7N thermal decomposition at lower temperatures appears because of the catalytic effect of incompatible materials, for example, the industrial-grade molecular sieves 4A that we tested. The electron impact dissociation of C4F7N plays an important role in C4F7N electrical decomposition, leading to additional formation of distinctive small molecules of CF4 and C2N2 of low concentrations. It was pointed out based on a real arcing test in a load disconnector that the decomposition of C4F7N gas mixtures in real applications will be at a much moderate and manageable rate than what was obtained from the highly accelerated laboratory tests presented in this work. The signatures of decomposition products extracted in this study provide invaluable guidance for developing decomposition-based diagnosis and fixation of decomposition byproducts toward SF6-free power grids.

Physics↗
DOE OSTI2024

NUMERICAL MODELING OF A SOLID OXIDE FUEL CELL FOR USE IN REAL-TIME SIMULATION AND CYBER-PHYSICAL SYSTEMS

Cyber-physical systems provide a mechanism with which to investigate the physical phenomena and behavior of traditionally cost-prohibitive or otherwise fragile equipment. For the National Energy Technology Laboratory (NETL), this approach resulted in the Hybrid Performance (Hyper) facility which features a gas turbine-SOFC hybrid cycle utilizing real turbomachinery and a simulated SOFC stack. This allows for the investigation of combined cycle performance and control strategies, in an exhaustive manner, both without fear of destroying delicate state-of-the-art fuel cells, and with the full accuracy of real-world turbomachinery. Issues arose between the transient response of the SOFC model being limited to a sample time of 80 milliseconds, due to the calculation time of the SOFC model taking on average 40 milliseconds to calculate for a given timestep with spikes in calculation time reaching the 80 millisecond threshold. In order to be able to match the speed of transients from the turbomachinery and likewise better discern transient behavior, it was determined that the SOFC model must be optimized to operate at a sample time of 5 milliseconds. Therefore, it is necessary to optimize the SOFC model in order to decrease the calculation time from around 40 milliseconds, down to at the most 5 milliseconds. To do this, both the electrochemical algorithm and the thermal algorithm used to simulate the physical behavior of the SOFC are investigated to determine where improvements can be made. To this end the rootfinding numerical recipes of the electrochemical algorithm are investigated as the complex electrochemistry requires a highly iterative nested dual convergence loop to resolve the voltage-current relationship, and likewise the temporal discretization of the thermal algorithm is modified for the sake of higher accuracy and stability. Ultimately the new electrochemical algorithm featuring higher order rootfinding schemes proves to be efficient enough to reach the sub 5 millisecond target, signifying an order of magnitude reduction in calculation time, and when coupled with the new temporal discretization similar calculation time characteristics show that a fully implicit, higher order temporal discretization can also successfully be used if desired. Ultimately this result means that the cyber-physical simulation system can operate at higher sample rates, and resolve transient events at significantly higher resolution and fidelity.

Arias, Jesus↗
DOE OSTI2026

Preliminary proof-of-concept of real-time divertor heat flux control from infrared cameras with nitrogen injection in the DIII-D tokamak

In future tokamak reactors like ITER and the Fusion Pilot Plant (FPP), real-time feedback control of heat flux to the plasma-facing components (PFC) will be critical for steady-state operation. This work presents the first experimental demonstration of real-time divertor heat flux estimation with infrared thermography and feedback control with impurity seeding on the DIII-D tokamak. The flexible infrastructure of the Plasma Control System (PCS) on DIII-D makes this new capability possible. The PCS software runs on a gateway computer system, and five real-time compute nodes. An array of low latency streaming digitizers from D-TACQ Solutions connects to these real-time computers to collect and process data, and send commands to actuators during plasma discharges. This system handles the signal IO from the tokamak and allows the PCS to utilize the diagnostic data necessary to perform control in real-time. Feedback control on heat flux was accomplished by feeding infrared camera data from the “Infrared TV” (IRTV) camera to a custom-developed User Datagram Protocol (UDP) server. This server transmits infrared data to a newly developed PCS algorithm that estimates the heat flux to PFC. Here, a proportional integral derivative (PID) controller minimizes the error between a heat flux reference and the real-time estimate by injecting nitrogen gas into the divertor.

Algorithm development↗
DOE OSTI2023

Revealing quasi-excitations in the low-density homogeneous electron gas with model exchange–correlation kernels

Time-dependent density functional theory within the linear response regime provides a solid mathematical framework to capture excitations. The accuracy of the theory, however, largely depends on the approximations for the exchange–correlation (xc) kernels. Away from the long-wavelength (or q = 0 short wave-vector) and zero-frequency (ω = 0) limit, the correlation contribution to the kernel becomes more relevant and dominant over exchange. The dielectric function, in principle, can encompass xc effects relevant to describe low-density physics. Furthermore, besides collective plasmon excitations, the dielectric function can reveal collective electron–hole excitations, often dubbed “ghost excitons.” Besides collective excitons, the physics of the low-density regime is rich, as exemplified by a static charge-density wave that was recently found for r s > 69, and was shown to be associated with softening of the plasmon mode. These excitations are seen to be present in much higher density 2D homogeneous electron gases of r s ≳ 4. Here, in this work, we perform a thorough analysis with xc model kernels for excitations of various nature. The uniform electron gas, as a useful model of real metallic systems, is used as a platform for our analysis. We highlight the relevance of exact constraints as we display and explain screening and excitations in the low-density region.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Phase Identification in Real Distribution Networks with High PV Penetration Using Advanced Metering Infrastructure Data

Many distribution network monitoring and control applications - including state estimation, volt/VAR optimization, and network reconfiguration - rely on accurate network models; however, the network models maintained by utilities can become outdated because of restoration activities, network reconfiguration, and missing data. With the widespread deployment of advanced metering infrastructure (AMI), abundant measurement data from low-voltage secondary networks are available. The AMI measurement data can be used for phase identification to improve the network models. Although the existing phase identification techniques work well in passive distribution feeders that do not have photovoltaic (PV) generation, they can fail to accurately identify the phases in the presence of PV. This paper proposes a robust phase identification algorithm based on supervised machine learning that accurately identifies the AMI meter phase connectivity in the presence of significant PV generation. The proposed algorithm does not require network topology information or feeder head measurement data. The algorithm is validated using the AMI measurement data collected in the field and the field-validated phase connectivity database on two real distribution feeders from San Diego Gas & Electric Company that have significant PV generation.

advanced metering infrastructure↗
DOE OSTI2022

“Innervated” Pipelines: A New Technology Platform for In-Situ Repair and Embedded Intelligence

The overall vision pursued under the project would ultimately enable capability for real-time operational monitoring of natural gas (and other) pipeline infrastructures through the combination of in-situ repair and rehabilitation methods with embedded fiber optic sensing and associated data analytics methods and platform tools. Benefits to the public include more resilient and robust natural gas pipeline infrastructures with the potential to expand the applications of developed technologies under the program to other important areas of critical energy infrastructure in the future such as H2 pipelines and infrastructure, municipal and other civil infrastructure, as well as subsurface oil, gas, and geothermal infrastructure applications. The program has successfully demonstrated feasibility for all of the critical enabling elements and serves as a foundation for future technology maturation, deployment, and customization for other potential applications and high-priority needs into the future. Several underlying innovations have shown potential for commercial deployment and are the subject of continued development and technology transfer based upon patents submitted during the project duration.

02 PETROLEUM↗
DOE OSTI2022

Phase Identification in Real Distribution Networks with High PV Penetration Using Advanced Metering Infrastructure Data: Preprint

Many distribution network monitoring and control applications - including state estimation, volt/VAR optimization, and network reconfiguration - rely on accurate network models; however, the network models maintained by utilities can become outdated because of restoration activities, network reconfiguration, and missing data. With the widespread deployment of advanced metering infrastructure (AMI), abundant measurement data from low-voltage secondary networks are available. The AMI measurement data can be used for phase identification to improve the network models. Although the existing phase identification techniques work well in passive distribution feeders that do not have photovoltaic (PV) generation, they can fail to accurately identify the phases in the presence of PV. This paper proposes a robust phase identification algorithm based on supervised machine learning that accurately identifies the AMI meter phase connectivity in the presence of significant PV generation. The proposed algorithm does not require network topology information or feeder head measurement data. The algorithm is validated using the AMI measurement data collected in the field and the field-validated phase connectivity database on two real distribution feeders from San Diego Gas & Electric Company that have significant PV generation.

advanced metering infrastructure↗
DOE OSTI2022

Phase Identification in Real Distribution Networks with High PV Penetration Using Advanced Metering Infrastructure Data

Many distribution network monitoring and control applications - including state estimation, Volt/VAr optimization, and network reconfiguration - rely on accurate network models; however, the network models maintained by utilities can become outdated because of restoration activities, network reconfiguration, and missing data. With the widespread deployment of advanced metering infrastructure (AMI), abundant measurement data from low-voltage secondary networks are available. The AMI measurement data can be used for phase identification to improve the network models. Although the existing phase identification techniques work well in passive distribution feeders that do not have photovoltaic (PV) generation, they can fail to accurately identify the phases in the presence of PV. This paper proposes a robust phase identification algorithm based on supervised machine learning that accurately identifies the AMI meter phase connectivity in the presence of significant PV generation. The proposed algorithm does not require network topology information or feeder-head measurement data. The algorithm is validated using the AMI measurement data collected in the field and the field-validated phase connectivity database on two real distribution feeders from San Diego Gas & Electric Company that have significant PV generation.

advanced metering infrastructure (AMI)↗
DOE OSTI2022

Characterization of plasma catalytic decomposition of methane: role of atomic O and reaction mechanism

In this work, we investigated atmospheric pressure plasma jet (APPJ)-assisted methane oxidation over a Ni-SiO 2 /Al 2 O 3 catalyst. We evaluated possible reaction mechanisms by analyzing the correlation of gas phase, surface and plasma-produced species. Plasma feed gas compositions, plasma powers, and catalyst temperatures were varied to expand the experimental parameters. Real-time Fourier-transform infrared spectroscopy was applied to quantify gas phase species from the reactions. The reactive incident fluxes generated by plasma were measured by molecular beam mass spectroscopy using an identical APPJ operating at the same conditions. A strong correlation of the quantified fluxes of plasma-produced atomic oxygen with that of CH 4 consumption, and CO and CO 2 formation implies that O atoms play an essential role in CH 4 oxidation for the investigated conditions. With the integration of APPJ, the apparent activation energy was lowered and a synergistic effect of 30% was observed. We also performed in-situ diffuse reflectance infrared Fourier-transform spectroscopy to analyze the catalyst surface. The surface analysis showed that surface CO abundance mirrored the surface coverage of CH n at 25 °C. This suggests that CH n adsorbed on the catalyst surface as an intermediate species that was subsequently transformed into surface CO. We observed very little surface CH n absorbance at 500 °C, while a ten-fold increase of surface CO and stronger CO 2 absorption were seen. This indicates that for a nickel catalyst at 500 °C, the dissociation of CH 4 to CHn may be the rate-determining step in the plasma-assisted CH 4 oxidation for our conditions. We also found the CO vibrational frequency changes from 2143 cm –1 for gas phase CO to 2196 cm –1 for CO on a 25 °C catalyst surface, whereas the frequency of CO on a 500 °C catalyst was 2188 cm –1 . Here, the change in CO vibrational frequency may be related to the oxidation of the catalyst.

methane oxidation↗
DOE OSTI2024

Volatility Basis Set Distributions and Viscosity of Organic Aerosol Mixtures: Insights from Chemical Characterization Using Temperature-Programmed Desorption–Direct Analysis in Real-Time High-Resolution Mass Spectrometry

Quantitative assessment of gas-particle partitioning of individual components within complex atmospheric organic aerosols (OA) mixtures is critical for predicting and comprehending the formation and evolution of OA particles in the atmosphere. This investigation leverages previously documented data obtained through a temperature programmed desorption - direct analysis in real time – high resolution mass spectrometry (TPD-DART-HRMS) platform. This methodology facilitates the bottom-up construction of volatility basis set (VBS) distributions for constituents found in three biogenic secondary organic aerosol (SOA) mixtures produced through the ozonolysis of a-pinene, limonene, and ocimene. The apparent enthalpies (ΔH*, kJ mol -1 ) and saturated vapor mass concentrations (C T *, µg∙m -3 ) of individual SOA components, determined as a function of temperature (T, K), facilitated an assessment of changes in VBS distributions and gas-particle partitioning with respect to T and atmospheric total organic mass loadings (tOM, µg∙m -3 ). Further, the VBS distributions reveal distinct differences in volatilities among monomers, dimers, and trimers, enabling their categorization into separate volatility bins. At the ambient temperature of T = 298 K, only monomers efficiently partition between gas and particle phases across a broad range of atmospherically relevant total organic mass loadings (tOM) values of 1–100 µg∙m -3 . Partitioning of dimers and trimers becomes notable only at T > 360 K and T > 420 K, respectively. The viscosity of SOA mixtures is assessed using a bottom-up calculation approach, incorporating the input of elemental formulas, ΔH*, C T *, and particle-phase mass fractions of the SOA components. Through this approach, we are able to accurately estimate the variations in SOA viscosity that result from the evaporation of its components. These variations are, in turn, influenced by atmospherically relevant changes in tOM and T. Comparison of the calculated SOA viscosity and diffusivity values with literature reported experimental results shows close agreement, thereby validating the employed calculation approach. These findings underscore the significant potential for TPD-DART-HRMS measurements in enabling the untargeted analysis of organic molecules within OA mixtures. This approach facilitates quantitative assessment of their gas-particle partitioning and allows for the estimation of their viscosity and condensed-phase diffusion, thereby contributing valuable insights to atmospheric models.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2020

Deciphering atomistic mechanisms of the gas-solid interfacial reaction during alloy oxidation

Gas-solid interfacial reaction is critical to many technological applications from heterogeneous catalysis to stress corrosion cracking. A prominent question that remains unclear is how gas and solid interact beyond chemisorption to form a stable interphase for bridging subsequent gas-solid reactions. Here, we report real-time atomic-scale observations of Ni-Al alloy oxidation reaction from initial surface adsorption to interfacial reaction into the bulk. We found distinct atomistic mechanisms for oxide growth in O 2 and H 2 O vapor, featuring a “step-edge” mechanism with severe interfacial strain in O 2 , and a “subsurface” one in H 2 O. Ab initio density functional theory simulations rationalize the H 2 O dissociation to favor the formation of a disordered oxide, which promotes ion diffusion to the oxide-metal interface and leads to an eased interfacial strain, therefore enhancing inward oxidation. Our findings depict a complete pathway for the Ni-Al surface oxidation reaction and delineate the delicate coupling of chemomechanical effect on gas-solid interactions.

36 MATERIALS SCIENCE↗
DOE OSTI2026

Gas-driven short disconnection mitigates thermal runaway in Li-ion batteries under mechanical abuse

Mechanical abuse poses a critical safety risk to Li-ion batteries by inducing internal short circuits that initiate thermal runaway. Remarkably, voltage recovery frequently emerges during thermal runaway initiation, a phenomenon that conventional theories fail to explain. Here, our study develops a new multiphysics mechanism to explain voltage recovery, termed gas-driven short disconnection, whereby internal gas pressure mechanically disengages short-circuit contacts and causes the cell voltage to rebound. This mechanism incorporates gas generation and its structural impact on the short circuit. Real-time optical and thermal imaging and X-ray computed tomography reveal fluid-structure interaction between internal gas flow and adjacent shorting contacts. We establish a mechanistic framework linking gas-driven short disconnection to cell-level voltage and temperature responses, elucidating the extension–truncation pattern of voltage recovery. Furthermore, thermal regime maps show that a voltage recovery duration exceeding 5 s correlates with limited temperature rise below 150 °C, indicating that sustained short-circuit disconnection suppresses Joule heating. Additionally, a dimensionless criterion is deduced from scaling analysis for physical plausibility of gas-driven short disconnection in mechanically abused cells. This finding inspires smart venting control, which regulates gas release to maintain the internal pressure while dissipating gas enthalpy, thereby providing a device-level strategy for thermal runaway mitigation.

Gas-driven short disconnection↗
DOE OSTI2021

Fe Thin Film-Coated Optics for Corrosion Monitoring: Optical and Electrochemical Studies

Corrosion proxy materials integrated with an optical sensing platform enable a real-time optical corrosion sensor for natural gas pipelines to prevent methane leaks and catastrophic events. Effects of CO 2 , pH, and film thickness on corrosion of Fe thin films (25, 50, 100 nm) were studied using optical and electrochemical methods in 3.5%wt. NaCl solutions at 30 °C. An increase in light transmission (T%) corresponded to corrosion of Fe thin films. CO 2 accelerated corrosion of Fe thin films due to the lower pH and the promoted corrosion reactions, resulting in a faster increase of T% than without CO 2 or at higher pH. While the corrosion rate increased with increasing film thickness, electrochemical corrosion of Fe thin films was in good agreement with that of bulk carbon steel, verifying that Fe thin films can serve as a representative corrosion proxy when integrated with the optical sensing platform.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Real-Time FPGA Implementation For Frequency Sweep Interferometry In The LBNF Complex

The Long Baseline Neutrino Facility (LBNF) will deliver a 2.4 MW muon neutrino beam from Fermilab to the Deep Underground Neutrino Experiment (DUNE), requiring unprecedented precision in beamline alignment to achieve DUNE's neutrino oscillation measurement goals. Vertical misalignments of beamline components as small as 0.5 mm can contribute 6-7\% uncertainty in predicted neutrino flux, necessitating sub-0.1 mm alignment monitoring capabilities. The Horn Location Sensor (HLS) system employs frequency sweep interferometry (FSI) in a distributed hydrostatic leveling network to achieve the required precision under harsh radiation conditions up to 5000 kRad/year. Traditional FSI implementations suffer from laser sweep nonlinearities that degrade resolution and require computationally intensive post-processing corrections using gas reference cells. This work presents a real-time FPGA-based implementation of the HLS data acquisition and processing system using a sweep tracker interferometer for dynamic sweep linearization. The system utilizes a PYNQ-Z2 FPGA with programmable logic implementing parallel 16k-point FFT processing across four channels, synchronized by the sweep tracker signal to eliminate post-processing requirements. Spectral performance testing demonstrates significant improvements in peak sharpness compared to traditional fixed-frequency digitization. The FPGA implementation enables real-time displacement monitoring with processing speeds orders of magnitude faster than software-based approaches, essential for the operational requirements of LBNF's eventual distributed sensor network. This advancement in real-time FSI processing directly supports DUNE's precision neutrino physics program by providing the rapid feedback necessary for maintaining stringent beamline alignment tolerances during high-power beam operations.

Rossel, A. Jacob [Fermilab; Unlisted]↗
DOE OSTI2024

Cyber-Physical Simulation of the Cold Startup of Solid Oxide Fuel Cell – Gas Turbine (SOFC-GT) Hybrid Systems

This work introduces experimental studies for the cold startup process (CPS) of the SOFC-GT hybrid system using the cyber-physical simulation approach. The physical gas turbine is coupled with a cyber-physical SOFC stack, which is represented using the integration of a real time dynamic SOFC model with physical components (e.g., pressure chamber, natural gas burner, etc.). Different ramp rates of the turbine speed were tested out during the startup processes. Bypass valves were also used to manipulate the airflow during SOFC-GT hybrid system start-up process. Different ramp rates enable the rapid start-up of the turbine to avoid surge and stall, meanwhile enable acceptable warm rate of the fuel cell stack without damaging the cell material. CPS can enable dynamic characterizations of highly integrated systems at lower cost.

Zhou, Nana↗