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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

Real-Time Detection of Hydrogen and Ammonia Isotopologues for Impurity Removal and Recovery of Tritium

To accommodate gas measurements for impurity removal and recovery of tritium, a silver-coated optical or waveguide is employed for collecting Raman scattered signals to determine relative hydrogen and ammonia isotopologue populations in real time. The data and results presented here demonstrate an analytical methodology for the analysis of four ammonia and three hydrogen isotopologues in a hydrogen–deuterium exchange reaction by gas phase Raman spectroscopy. Standard chemometric modeling techniques effectively unravel the signatures of the isotopologues involved observed here; however, a sophisticated quantum chemical approach supports the spectral assignments. An interpretation of the data presented here can emphasize the practicality and reliability of the gaseous monitoring system in complex chemical environments for the hydrogen fuel economy as well as the more distant energy source from a facility that handles tritium. There are still considerable concerns about the measurement of tritium in isotope separation and radiological impurities from gas processing. A common impurity in gas processing is ammonia, which can form readily in the presence of nitrogen and tritium. Substituted ammonia (NQ 3 ), where Q = H, D, or T, is traditionally removed through getters or diffusers along with other non-hydrogen contaminants. A preferable analytical approach is noninvasive and can be deployed for real-time process evaluation in radiological environments.

Ammonia↗

Elucidating the Reaction Pathways of Veratrylglycero-β-Guaiacyl Ether Degradation over Metal-Free Solid Acid Catalyst with Hydrogen

Efficient cleavage of β-O-4 bonds in lignin to high-yield aromatic compounds for the potential production of fuels and chemicals is vital for the economics of the modern biorefinery industry. This work is distinct in that a detailed mechanistic analysis of the reaction pathways of veratrylglycero-β-guaiacyl ether (VGE) catalyzed by transition-metal-free solid acid zeolite in aqueous conditions at high hydrogen pressure has been performed. VGE degradation produced high monomers yields (≈87 %), including guaiacol (48.2 %), 1-(3,4-dimethoxyphenyl)ethanol (10.3 %), 1-(3,4-dimethoxyphenyl)-2-propanol (6.1 %), 3,4-dimethoxyphenylpropanol (4.7 %), 3,4-dimethoxycinnamyl alcohol (4.1 %), and 1,2-dimethoxy-4-propylbenzene (2 %). The products were identified and confirmed by the in situ solid-state magic angle spinning (MAS) 13 C NMR spectroscopy in real-time conditions and the two-dimensional gas chromatography (GC×GC). A variety of products reveal the crucial role of hydrogen, water, and acid sites for heterolytic cleavage of the β-O-4 bond in VGE. Decarbonylation, hydrogenolysis, hydrogenation, and dehydration reaction pathways are proposed and further validated using first-principles calculations.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Characterization and controllability of radiated power via extrinsic impurity seeding in strongly negative triangularity plasmas in DIII-D

Experiments with extrinsic impurity seeding in strongly negative triangularity shapes in DIII-D achieved radiated power fractions (relative to input power) of up to ≈85% total radiation and ≈55% core radiation in steady-operating conditions. The relationship between core and total radiation was sensitive to impurity species and input power. Attempts to reach higher radiation levels via higher impurity flows resulted in radiative collapse disruptions. Nitrogen, neon, argon, and krypton were tested. Injection was by gas puffing, usually controlled by feeding back real-time estimates for core or total radiating fractions ($P_\textrm{rad} / P_\textrm{input}$). Argon and krypton were controlled by feeding back total radiating fraction, whereas neon was controlled by feeding back the core radiating fraction, due to the lower efficiency of neon as a divertor radiator. Nitrogen flows were pre-programmed. Reasonable total $P_\textrm{rad}$ control target following was achieved with argon or krypton. Control was more challenging with the neon/core radiation configuration, which was more prone to slow response and overshooting of the control target. Poor particle removal contributed to the control challenge: neon particle inventory within the last closed flux surface was roughly constant for up to 1 s (the longest duration tested) after neon injection was halted. However, separate experiments with laser-blow off of non-recycling impurities measured a short impurity confinement time, on the scale of the energy confinement time of ∼100 ms. Modeling with the Aurora impurity transport simulation matched experimental neon density profiles with full recycling (R = 1.0) and weak pumping but predicted rapid decreases in neon inventory if pumping were increased or recycling decreased. This indicates that changes outside the confined plasma (adding a well-placed pump) would improve controllability for all highly recycling species.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

Development of an integrated core–edge scenario using the super H-mode

An optimized pedestal regime called the super-H (SH) mode is leveraged to couple a fusion relevant core plasma with a high density scrape-off layer appropriate for realistic reactor power exhaust solutions. Recent DIII-D experiments have expanded the operating space of the SH regime using advanced control algorithms and investigated optimization of impurity seeding, deuterium gas puffing, and 3D magnetic perturbations. Simultaneous real-time control of the pedestal density and radiated power with in-vessel coils and nitrogen seeding enable optimal coupled divertor and pedestal conditions. Four case studies are analysed with varied levels of radiated power in the divertor volume ranging from 0 (no seeding) to 8.5 MW radiated from carbon and nitrogen emission. Plasmas with a 4.5 MW radiated power target establish a radiative mantle, leading to divertor temperatures of ~16 eV while maintaining SH-mode, and with only marginal impact on the pedestal and core performance. Increased levels of N 2 seeding with a 7.5 MW radiated power target facilitate detachment onset and divertor temperatures <5 eV, with no degradation in stored energy and the operational point remaining inside the SH-mode channel for $ > 2.5{\tau }_{\text{E}}$. Finally, a 8.5 MW radiated power target leads to partial detachment, which is so far associated with the loss of access to SH-mode pedestal conditions.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

Leveraging FPGA Advantages for Quicker Data Processing for LBNF

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, Jacob↗

Center for Gas Separations (CGS)

The total energy consumption in the U.S. has been rising steadily for decades, and it currently amounts to ~98,000 TBtu/yr, with approximately 30% of this total attributable to the industrial sector. Reasonable estimates indicate that 45–55% of total industry energy consumption derives from chemical separations, and for example, over 120 TBtu/yr alone is used in carrying out olefin/paraffin separations via energy-intensive cryogenic distillation. Therefore, the pursuit of new, even radically different approaches to some of the most energy-intensive industrial separations processes is an imperative scientific pursuit for reducing energy consumption toward a more sustainable future. Adsorbent and membrane-based separations can require a fraction of the energy needed for distillation methods, and as such are considered promising solutions for balancing increasing energy demand in the U.S. with the need for a massive reduction in energy consumption. Although considerable research effort has been devoted to the design of materials capable of carrying out various gas separations, usually operating through size-selective, chemisorptive, or physisorptive mechanisms, it remains a great challenge to design materials that function adequately for real-world applications. Indeed, the chemical and physical differences between molecules in gas mixtures of interest are often small, and therefore it is necessary, through the use of nanoscience and synthetic chemistry, to engineer unprecedented molecular-level control in adsorbate–adsorbent interactions. The overarching mission of the Center for Gas Separations (CGS) was to discover fundamental innovations that have the potential to dramatically reduce the energy associated with critical gas separations. In particular, the CGS developed novel synthetic routes, guided by molecular chemistry principles, as well as advanced characterization and computational methods, that have enabled the discovery of new materials and membranes tailor-made to exhibit exceptional performance for a range of gas separations processes, as required in the clean use of fossil fuels and in reducing CO 2 emissions from industry. A challenge of this magnitude required the collaboration and synergy of a large team of researchers with expertise in materials synthesis, characterization, and computations. During the 11-year project period, the CGS created a range of new materials within the family of highly-tunable, porous solids known as metal–organic frameworks (MOFs). These new frameworks demonstrate novel mechanisms for key industrial gas separations, including revolutionary new cooperative adsorption processes that enable low-energy CO 2 and CO capture, and are capable of efficiently separating olefins from paraffins, O 2 from air, and the shape-selective separation of alkane isomers. In addition, the CGS developed new strategies for incorporating these materials into composite membranes toward highly efficient and selective membrane-based separations. As a testament to the success of the CGS, two start-up companies, Mosaic Materials,4 Inc. and Flux Technology, Inc., grew out of these research efforts, and these companies are seeking to commercialize MOF and composite membranes materials for key separations in industry, including large-scale CO 2 capture and hydrocarbon separations, respectively. Another company, framergy, Inc., licensed IP resulting from CGS research toward the commercialization of adsorbents for various energy-relevant applications.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Predictive Self-Healing Seals for Gas Transmission

Damaged elastomeric diaphragms within pneumatic controllers used in the oil and gas industry lead to an unintended release of methane. Self-healing microvascular materials capable of healing various types of damage have been fabricated. These microvascular materials are designed to replace currently available commercial diaphragms found in pneumatic controllers and provide a solution to reduce unintended methane leaks. This project demonstrated the ability of these materials to reduce methane release by more than 80% in lab and pilot settings. Additionally, ML approaches successfully identified leaking systems based on training using synthetic and real data.

03 NATURAL GAS↗

Using Machine Learning Tools to Predict Compressor Stall

Clean energy has become an increasingly important consideration in today’s power systems. As the push for clean energy continues, many coal-fired power plants are being decommissioned in favor of renewable power sources such as wind and solar. However, the intermittent nature of renewables means that dynamic load following traditional power systems is crucial to grid stability. With high flexibility and fast response at a wide range of operating conditions, gas turbine systems are poised to become the main load following component in the power grid. Yet, rapid changes in load can lead to fluid flow instabilities in gas turbine power systems. These instabilities often lead to compressor surge and stall, which are some of the most critical problems facing the safe and efficient operation of compressors in turbomachinery today. Although the topic of compressor surge and stall has been extensively researched, no methods for early prediction have been proven effective. This study explores the utilization of machine learning tools to predict compressor stall. The long short-term memory (LSTM) model, a form of recurrent neural network (RNN), was trained using real compressor stall datasets from a 100 kW recuperated gas turbine power system designed for hybrid configuration. Two variations of the LSTM model, classification and regression, were tested to determine optimal performance. The regression scheme was determined to be the most accurate approach, and a tool for predicting compressor stall was developed using this configuration. Overall, results show that the tool is capable of predicting stalls 5–20 ms before they occur. With a high-speed controller capable of 5 ms time-steps, mitigating action could be taken to prevent compressor stall before it occurs.

42 ENGINEERING↗

Machine learning-based ethylene concentration estimation, real-time optimization and feedback control of an experimental electrochemical reactor

With the increase in electricity supply from clean energy sources, electrochemical reduction of carbon dioxide (CO 2 ) has received increasing attention as an alternative source of carbon-based fuels. As CO 2 reduction is becoming a stronger alternative for the clean production of chemicals, the need to model, optimize and control the electrochemical reduction of the CO 2 process becomes inevitable. However, on one hand, a first-principles model to represent the electrochemical CO 2 reduction has not been fully developed yet because of the complexity of its reaction mechanism, which makes it challenging to define a precise state-space model for the control system. On the other hand, the unavailability of efficient concentration measurement sensors continues to challenge our ability to develop feedback control systems. Gas chromatography (GC) is the most common equipment for monitoring the gas product composition, but it requires a period of time to analyze the sample, which means that GC can provide only delayed measurements during the operation. Moreover, the electrochemical CO 2 reduction process is catalyzed by a fast-deactivating copper catalyst and undergoes a selectivity shift from the product-of-interest at the later stages of experiments, which can pose a challenge for conventional control methods. To this end, machine learning (ML) techniques provide a potential approach to overcome those difficulties due to their demonstrated ability to capture the dynamic behavior of a chemical process from data. Motivated by the above considerations, we propose a machine learning-based modeling methodology that integrates support vector regression and first-principles modeling to capture the dynamic behavior of an experimental electrochemical reactor; this model, together with limited gas chromatography measurements, is employed to predict the evolution of gas-phase ethylene concentration. The model prediction is directly used in a proportional-integral (PI) controller that manipulates the applied potential to regulate the gas-phase ethylene concentration at energy-optimal set-point values computed by a real-time process optimizer (RTO). Specifically, the RTO calculates the operation set-point by solving an optimization problem to maximize the economic benefit of the reactor. Finally, suitable compensation methods are introduced to further account for the experimental uncertainties and handle catalyst deactivation. The proposed modeling, optimization, and control approaches are the first demonstration of active control for a CO 2 electrolyzer and contribute to the automation and scale-up efforts for electrified manufacturing of fuels and chemicals starting from CO 2 .

42 ENGINEERING↗

PRIMO – The Oil & Gas Well Plugging Optimizer

This work presents PRIMO’s main capabilities and introduces the PRIMO web application, an intuitive user interface that leverages our sophisticated mathematical optimization model to rigorously optimize P&A priorities and plugging campaign efficiency. The web app simplifies user interaction, provides a powerful data management framework, and supports a broad user base (e.g., state agencies, well owners/operators, and plugging companies) to use PRIMO for decision-making. Specifically, we provide a demonstration of how to input the information on candidate wells, plugging campaign budget, user-defined priority and efficiency criteria to PRIMO. A real-world case study that consists of 1411 oil and gas wells and impact and efficiency priorities (e.g., well age, well proximity to schools/hospitals, well accessibility, distance between wells in projects) is presented to showcase PRIMO’s core capabilities: (i) ranking a candidate well population based on priorities, (ii) recommending high-impact and high-efficiency P&A projects, and (iii) assigning impact and efficiency scores to projects allowing for rigorous quantitative comparison among them.

02 PETROLEUM↗

CAMERA: A method for cost-aware, adaptive, multifidelity, efficient reliability analysis

Estimating probability of failure in aerospace systems is a critical requirement for flight certification and qualification. Failure probability estimation involves resolving tails of probability distributions, and Monte Carlo sampling methods are intractable when expensive high-fidelity simulations have to be queried. Here, we propose a method to use models of multiple fidelities that trade accuracy for computational efficiency. Specifically, we propose the use of multifidelity Gaussian process models to efficiently fuse models at multiple fidelity, thereby offering a cheap surrogate model that emulates the original model at all fidelities. Furthermore, we propose a novel sequential acquisition function based experiment design framework that can automatically select samples from appropriate fidelity models to make predictions about quantities of interest at the highest fidelity. We use our proposed approach in an importance sampling setting and demonstrate our method on the failure level set and probability estimation on synthetic test functions and two real-world applications, namely, the reliability analysis of a gas turbine engine blade using a finite element method and a transonic aerodynamic wing test case using Reynolds-averaged Navier-Stokes equations. We show that our method predicts the failure boundary and probability more accurately and at a fraction of the computational cost compared with using just a single expensive high-fidelity model. Finally, we show that our sequential approach is guaranteed to asymptotically converge to the true failure boundary with high probability.

97 MATHEMATICS AND COMPUTING↗

Facile hermetic TEM grid preparation for molecular imaging of hydrated biological samples at room temperature

Abstract Although structures of vitrified supramolecular complexes have been determined at near-atomic resolution, elucidating in situ molecular structure in living cells remains a challenge. Here, we report a straightforward liquid cell technique, originally developed for real-time visualization of dynamics at a liquid-gas interface using transmission electron microscopy, to image wet biological samples. Due to the scattering effects from the liquid phase, the micrographs display an amplitude contrast comparable to that observed in negatively stained samples. We succeed in resolving subunits within the protein complex GroEL imaged in a buffer solution at room temperature. Additionally, we capture various stages of virus cell entry, a process for which only sparse structural data exists due to their transient nature. To scrutinize the morphological details further, we used individual particle electron tomography for 3D reconstruction of each virus. These findings showcase this approach potential as an efficient, cost-effective complement to other microscopy technique in addressing biological questions at the molecular level.

59 BASIC BIOLOGICAL SCIENCES↗

Development of an In Situ Fission Gas Release Instrument for Fuel Sample Irradiations in the High Flux Isotope Reactor

Experimental measurement of gaseous fission product release with respect to temperature and burnup is a critical aspect of understanding nuclear fuel performance, validating predictive models, and qualifying new fuels. To measure this phenomenon in real-time, Oak Ridge National Laboratory has developed an instrument for measuring in situ fission gas release from small-scale fuel samples irradiated in the High Flux Isotope Reactor (HFIR). The instrument uses a continuous flow of Heover the fuel samples to sweep gaseous fission products from a sealed capsule in the HFIR Be reflector to an instrument station adjacent to the reactor. The instrument station houses two high-purity germanium (HPGe) detectors that measure decay gamma rays from fission products passing through a room temperature dwell chamber placed over the detector crystal. The sealed capsules in the reactor are designed to modulate fuel sample temperatures between 700 and 1,100°C by changing the Ar/He gas mixture surrounding the capsules during irradiation. N-type thermocouples are incorporated into the capsule housing to record real-time fuel temperatures. The capsules are heated primarily by prompt gamma rays emitted from the HFIR core with minimal heat contributions from fission in the fuel samples to minimize temperature gradients in the specimens for separate-effects characterization of the material. This paper describes modeling of time-dependent nuclear heating and fission product formation in fuel samples, thermal characteristics of the in-core capsules, and expected gaseous fission product gamma spectra at the HPGe instrument station.

Mulligan, Padhraic L [ORNL] (ORCID:000000025826540↗

Monitoring Noble Gases (Xe and Kr) and Aerosols (Cs and Rb) in a Molten Salt Reactor Surrogate Off-Gas Stream Using Laser-Induced Breakdown Spectroscopy (LIBS)

In this study with surrogate materials we show that laser-induced breakdown spectroscopy (LIBS) is a robust tool with promising capability toward monitoring gaseous (Xe and Kr) and aerosol (Cs and Rb) species in an off-gas stream from a molten salt reactor (MSR). MSRs will continually evolve fission products into the cover gas flowing across the reactor headspace. The cover gas entrains Xe and Kr gases, along with aerosol particles, before passing into an off-gas treatment system. Univariate models of Xe and Kr peaks showed a strong correlation to concentration indicated by their coefficients of determination of 0.983 and 0.997, respectively. Multivariate models were built for all four analytes using partial least squares regression coupled with preprocessing steps including normalization, trimming, and/or genetic algorithm derived filters. The models were evaluated by predicting the concentrations of the analytes in four validation samples, in which all calibration models were successfully validated at a confidence interval of 99.9%. Finally, pressure controllers were used to regulate the mass flow rate of Kr flowing into the measurement cell in sinusoidal and stepwise waveforms to test the real-time monitoring capabilities of the regression models. Both univariate and partial least squares Kr models were able to successfully quantify the gas concentration in the real-time evaluation. The root mean squared error of prediction (RMSEP) values for these real-time tests were calculated to be 0.051, 0.060, and 0.121 mol% demonstrating the measurement systems’ capability to perform online monitoring with acceptable accuracy.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗

Single Bimodular Sensor for Differentiated Detection of Multiple Oxidative Gases

Semiconductive metal-oxide sensors suffer from cross-sensitivities under mixed chemical condition, specifically upon mixture of multiple oxidative or reductive gases. Herein, a single bimodular sensor is demonstrated for smart differentiation of multiple oxidative analytes by relating the resistance-metric mode to impedance-metric mode. The sensor construct based on ZnO nanorods readily outputs three response datasets upon exposure of oxidative-gas mixture including O 2 , SO 2 , and NO 2 , the resistance, real part impedance, and imaginary part impedance. The differentiative and correlated nature between these response signals allows such a single sensor platform to differentiate these oxidative gases accurately and robustly. Linear and non-linear decision boundaries are established over a large gas-concentration range from 2 ppm to 3% through a combination of principal component analysis and artificial neural network training. A facile user interface is demonstrated for recognition and measurement of unknown gas analytes, with the error of the predicted analyte-concentration as low as 2%.

36 MATERIALS SCIENCE↗

Advances in understanding vacuum break dynamics in liquid helium-cooled tubes for accelerator beamline applications

Understanding air propagation and condensation following a catastrophic vacuum break in particle accelerator beamlines cooled by liquid helium is essential for ensuring operational safety. This review summarizes experimental and theoretical work conducted in our cryogenics lab to address this issue. Systematic measurements were performed to study nitrogen gas propagation in uniform copper tubes cooled by both normal liquid helium (He I) and superfluid helium (He II). These experiments revealed a nearly exponential deceleration of the gas front, with stronger deceleration observed in He II-cooled tubes. To interpret these results, a one-dimensional (1D) theoretical model was developed, incorporating gas dynamics, heat transfer, and condensation mechanisms. The model successfully reproduced key experimental observations in the uniform tube system. However, recent experiments involving a bulky copper cavity designed to mimic the geometry of a superconducting radiofrequency (SRF) cavity revealed strong anisotropic flow patterns of nitrogen gas within the cavity, highlighting limitations in extrapolating results from simplified tube geometries to real accelerator beamlines. To address these complexities, we outline plans for systematic studies using tubes with multiple bulky cavities and the development of a two-dimensional (2D) model to simulate gas dynamics in these more intricate configurations. As a result, these efforts aim to provide a comprehensive understanding of vacuum breaks in particle accelerators and improve predictive capabilities for their operational safety.

Beamline tube↗

Strengths of plasmon satellites in XPS: Real-time cumulant approach

Plasmon satellites observed in x-ray photoemission spectra (XPS) reflect many-body effects beyond the single-electron approximation. However, theoretical calculations are challenging and simplified theories based on electron gas models are not always satisfactory. Here, a cumulant Green’s function approach with a real-time time-dependent density functional theory calculation of the cumulant is described for calculations of these effects, without the need for adjustable parameters. Here, the approach yields good agreement with experimental XPS results, as illustrated for Ge and Al. Corrections due to extrinsic losses and interference are also briefly discussed.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗

On-Line Monitoring of Gas-Phase Molecular Iodine Using Raman and Fluorescence Spectroscopy Paired with Chemometric Analysis

Molten salt reactors (MSRs) have the potential to safely support green energy goals. However, licensing and deployment of these systems will be aided through development of new technology. This includes on-line monitoring tools for real-time compositional analysis. Of particular interest is quantifying iodine within reactor off-gas streams to support design and operational control of reactor off-gas treatment systems. Here we discuss the development of advanced Raman spectroscopy systems for the on-line analysis of I 2(g) within the gas phase. Signal response is explored with two Raman instruments utilizing a 532 nm and a 671 nm excitation source, as a function of I 2(g) pressure and temperature. Furthermore, the applicability of chemometric modeling for advanced analysis of data is explored. Raman spectroscopy paired with chemometric analysis is demonstrated to be a powerful route to analyzing I 2(g) composition within the gas phase, which lays the foundation for applications within molten salt reactor off-gas analysis and other significant chemical processes producing iodine species.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗