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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 361 records · Page 20
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Stresses within rare-earth doped yttria-stabilized zirconia thermal barrier coatings from in-situ synchrotron X-ray diffraction at high temperatures

There is a growing interest for smart coatings that can be integrated into turbine engines for in-situ temperature measurements or health monitoring. The addition of rare-earth dopants into standard thermal barrier ceramic top coat materials is used to obtain luminescent coatings that enable spectral measurements, for real-time temperature or health monitoring. The thermomechanical performance and durability of such novel coating compositions in extreme environments still remains to be evaluated. Consequently, the ability to manufacture sensor coatings which present suitable thermal properties needs to be demonstrated. For this study, highly luminescent erbium and europium doped yttria-stabilized zirconia and state-of-the-art yttria-stabilized zirconia coatings manufactured by air plasma spray were characterized to determine the effects of the embedded rareearth dopants on coating internal strain and stress and to quantify and compare their high temperature response using synchrotron X-ray diffraction. In-situ depth-resolved strain measurements were performed at 15 μm intervals along the depth of the coatings to evaluate materials response at key locations, specifically at layer interfaces. In-plane stress was calculated for the coatings and a finite element model was implemented to supplement the results and enable further predictions. The results show that the sensor coatings that were manufactured in this work revealed only minor variations in the strain response of sensor coatings under a typical thermal cycle and especially at temperatures closer to that of gas turbine operating conditions, compared to state-of-the-art coatings. This work demonstrates the viability of manufacturing rare-earth doped yttria-stabilized zirconia coatings that provide beneficial spectroscopic monitoring capabilities while having minimal impact on the thermomechanical response of the thermal barrier coatings.

Air plasma spray↗
DOE OSTI2020

Combined Plasmonic Au-nanoparticle and Conducting Metal Oxide High-temperature Optical Sensing with LSTO

<span style="color: rgba(0, 0, 0, 0.79); font-family: &quot;Source Sans Pro&quot;, source-sans-pro, museo_sans300, museo-sans, Arial, sans-serif; font-size: 16px; background-color: rgb(255, 255, 255);">Fiber optic sensor technology offers several advantages for harsh-environment applications. However, the development of optical gas sensing layers that are stable under harsh environmental conditions is an ongoing research challenge. In this work, electronically conducting metal oxide lanthanum-doped strontium titanate (LSTO) films embedded with gold nanoparticles are examined as a sensing layer for application in reducing gas flows at high temperature (600–800 °C). A strong localized surface plasmon resonance (LSPR) based response to hydrogen is demonstrated in the visible region of the spectrum, while a Drude free electron-based response is observed in the near-IR. Characteristics of these responses are studied both on planar glass substrates and on silica fibers. Charge transfer between the oxide film and the gold nanoparticles is explored as a possible mechanism governing the Au LSPR response and is considered in terms of the corresponding properties of the conducting metal oxide-based matrix phase. Principal component analysis is applied to the combined plasmonic and free-carrier based response over a range of temperatures and hydrogen concentrations. It is demonstrated that the combined visible and near-IR response of these films provides improved versatility for multiwavelength interrogation, as well as improved discrimination of important process parameters (concentration and temperature) through application of multivariate analysis techniques.</span>

Wuenschell, Jeffrey K.↗
DOE OSTI2022

First-principles study of the defect-activity and optical properties of FAPbCl 3

With promising solar cell applications, organic–inorganic lead halide perovskites belong to a novel and rapidly developing class of semiconducting materials. Unlike its well-investigated iodide counterparts, formamidinium lead chloride (FAPbCl 3 ) is a much less studied perovskite that shows superior stability and surprisingly high selectivity and sensitivity towards certain gas analytes. The origin of the sensing ability of this perovskite material can be traced to its high defect tolerance and the existence of some defects which act as the “lock” to a certain gas analyte's “key”. In this work, we deepen the understanding of this proven perovskite sensor through first-principles computational study of its defect formation energies, charge transition energies, and optical properties. These studies are done with the widely used GGA exchange–correlation functional PBE and the newer meta-GGA functional SCAN, both incorporating the Grimme's D3 dispersion correction. Additionally, we also consider experimental conditions and the effect of temperature on electron screening in the simulations, which demonstrates that screening is particularly important when considering high oxidation states of the defects. We also note the importance of antisites in the Fermi Pinning as well as the importance of FA disorder, which are previously unreported insights.

36 MATERIALS SCIENCE↗
DOE OSTI2025

A device for volatile organic compound (VOC) analysis from skin using heated dynamic headspace sampling

Abstract Human skin is an important source of volatile organic compounds (VOCs) offering noninvasive methods to gain clinical metabolite information. This work was focused on the development of a skin sampling device based on a dynamic headspace sampling method with the addition of temperature to increase VOC metabolite recovery. The device preconcentrates skin VOC emissions onto a sorbent substrate, which can either be preserved for offline analysis or attached to a real time sensor downstream. In this work, skin VOC samples were analyzed offline using thermal desorption-gas chromatography-mass spectrometry. A list of 10 common skin VOCs was pre-selected to optimize parameters of sampling time, sampling temperature, and sorbent selection. Overall, this study highlights an effective skin VOC sampling technology with a heating dimension (40 °C, rather than 30 °C or no heating) with a sampling time of 15 min (rather than 5 or 30 mins) and onto Tenax TA sorbent (rather than PDMS), which collectively increases the recovery of compounds with lower vapor pressure and decreases the observed variability in skin VOC measurements. Finally, a list of 79 skin VOC compounds were detected and identified within a cohort of 20 young, healthy volunteers.

Biochemistry & Molecular Biology↗
DOE OSTI2025

Impact of shading following installation of agrivoltaic systems on eco-physiological and biochemical attributes of “Howes” cranberry in Massachusetts

Agrivoltaic (AV) systems, which integrate solar energy generation with crop production on the same piece of land, offer a sustainable solution for optimizing land-use efficiency. However, the impact of shading induced by solar panels on crop growth, physiology, and metabolic activities of cranberry is not known. Understanding these interactions is essential for assessing the feasibility of AV systems in cranberry production. In this study we evaluated eco-physiological and biochemical crop responses on a commercial ‘Howes’ cranberry bog grown under an agrivoltaics system using different row spacings in the year immediately following installation. Following installation of fixed tilt solar panels at a height of 3.0 m above the plant canopy, four shading treatments were evaluated: an uncovered control area and row spacings of 9.0 m, 7.0 m, and 5.0 m, corresponding to 30%, 35%, and 37% shading respectively. The shading effect was also evaluated at three different fruit developmental stages (green, blush, and full-red stage). Using microclimate sensors installed in the treatment areas, key eco-physiological and biochemical parameters, including leaf gas exchange, chlorophyll, anthocyanin, and antioxidant content, electrolyte leakage, proline and trehalose accumulation, were assessed. While moderate shading (30% and 35%) alleviated environmental stress, as reflected by improved leaf water status and reduced electrolyte leakage, these same treatments suppressed key physiological and metabolic functions, including photosynthetic activity, carbon reserve accumulation, and the biosynthesis of stress-related osmolytes and antioxidant compounds. Under higher shading (37%), these reductions were even more pronounced, indicating a potential threshold beyond which the benefits of shading are outweighed by declines in assimilate production and secondary metabolism. These findings highlight a trade-off between the stress-mitigating benefits of shading and the alterations in key physiological and metabolic processes under agrivoltaic systems. Minimizing these trade-offs by optimizing row spacing in agrivoltaics systems is critical to maintain cranberry physiological status. Since commercial cranberry is grown as a continuous plant canopy (no interrow spaces for equipment travel), damage caused by the installation process likely impacted crop response evaluated in the first year; multi-year studies are needed to fully understand the dynamics of a dual-use system. Notably, this work provides the first direct measurements of proline, trehalose content, and electrolyte leakage in cranberry grown under AV conditions, offering new insight into their physiological adjustments to shading.

14 SOLAR ENERGY↗
DOE OSTI2025

One-shot gas detection with transformer paired neural networks in Mako collected longwave infrared hyperspectral imagery

To date, careful data treatment workflows and statistical detectors are used to perform hyperspectral image (HSI) detection of any gas contained in a spectral library, which is often expanded with physics models to incorporate different spectral characteristics. In general, surrounding evidence or known gas-release parameters are used to provide confidence in or confirm detection capability, respectively. This makes quantifying detection performance difficult as it is nearly impossible to develop an absolute ground truth for gas target pixel presence in collected HSI. Consequently, developing and comparing new detection methods, especially machine learning (ML)-based methods, is susceptible to subjectivity in derived detection map quality. Here, in this work, we demonstrate the first use of transformer-based paired neural networks (PNNs) for one-shot gas target detection for multiple gases while providing quantitative classification and detection metrics for their use on labeled data. Terabytes of training data are generated from a database of long-wave infrared HSI obtained from historical Mako sensor campaigns over Los Angeles. By incorporating labels, singular signature representations, and a model development pipeline, we can tune and select PNNs to detect multiple gas targets that are not seen in training on a quantitative basis. We additionally assess our test set detections using interpretability techniques widely employed with ML-based predictors, but less common with detection methods relying on learned latent spaces.

Hyperspectral imaging↗
DOE OSTI2024

Low-cost Retrofit Kit for Integral Reciprocating Compressors (IRCs) to Reduce Emissions and Enhance Efficiency

Methane emissions from natural gas engines within the oil and gas industry pose a significant environmental challenge, contributing approximately 34.1 MMTCO2 eq to the total of 239 MMTCO2 eq of methane emissions in 2021, according to the EPA report. In response to this pressing issue, a collaborative effort involving the University of Oklahoma and key industry partners—WAGO Automation, Mid Continental Rental, Elipsa, and Perscient—has resulted in the development of a retrofit kit designed to reduce emissions from integral reciprocating compressors (IRCs), which are integrated compressors and engines. The retrofit kit developed comprises an Air Management System (AMS), Integrated Sensors, and a Cloud-Connected Control Unit with Graphical User Interface (GUI)/Human-Machine Interface (HMI). This solution enhances operational efficiency, reduces emissions, and expands the operational envelope of IRCs in the natural gas industry. The project successfully completed all tasks, including the installation of a full-size IRC at a designated site in Oklahoma, the development of an optimized AMS, integration of sensors, and implementation of a data acquisition system. Significant achievements include a notable reduction in CH 4 emissions, up to 84% at specific loads, and the successful deployment of the retrofit kit in diverse field conditions. The system's capabilities were enhanced through the creation of a feedback control algorithm for the AMS using a correlation matrix illustrating relationships between engine parameters, and the design of a predictive and preventive maintenance platform. The project concluded with the deployment of the entire retrofit kit to another location, confirming its effectiveness in reducing emissions and enhancing IRC performance. The comprehensive solution offers valuable benefits for IRCs, making them invaluable assets in the natural gas industry.

03 NATURAL GAS↗
DOE OSTI2025

Improved Gas Plume Identification Using Nearest Neighbor Methods for Background Estimation

Longwave infrared (LWIR) hyperspectral imaging (HSI) can be used for many tasks in remote sensing, including detecting and identifying effluent gases by LWIR sensors on airborne platforms. Identification is used after detection to increase confidence in weakly detected plumes, reduce false positives from detection, and distinguish between similar and confounding material signatures. Background estimation is an important step used to reveal the unique spectral characteristics of the detected gas, allowing the identification model to determine what the gas is specifically. The importance of proper background estimation increases when dealing with weak signals, large libraries of gases of interest, and uncommon or heterogeneous backgrounds. In this article, we propose two methods for background estimation: a novel k-nearest segments (KNS) algorithm and the standard k-nearest neighbors (KNN) algorithm. We test our methods and three existing background estimation methods for comparison against global background estimation to determine which performs best at estimating the true background radiance under a plume and for increasing identification confidence using a neural network classification model. We compare the different methods using 640 simulated weak plumes in an urban environment. For identification, our KNS algorithm improves median neural network identification confidence by 53.2%. For background radiance estimation, the KNN algorithm provides a median of 49 times less RMSE than global background estimation. Furthermore, KNN is the easiest method to tune for different plumes, making it an excellent “out of the box” background estimator.

47 OTHER INSTRUMENTATION↗
DOE OSTI2020

Sensors for measuring thermal conductivity and related methods

A sensor for measuring thermal conductivity includes an insulator, a test material over the insulator, a conductor over the test material, and a gas within an open volume adjacent the test material and the conductor. An electrical source is configured to provide an alternating current through the conductor to heat the test material. Leads are connected to the conductor and configured to connect to a voltmeter. A method of measuring thermal conductivity includes disposing the sensor in a reactor core in which a nuclear fuel undergoes irradiation and radioactive decay. An alternating current is provided from the electrical source through the conductor to heat the test material. A voltage is measured as a function of time at the leads connected to the conductor. A thermal conductivity of the test material is calculated based on the voltage measured as a function of time. Methods of forming a sensor are also disclosed.

Daw, Joshua↗
DOE OSTI2020

Liquid-phase turbulence measurements in air-water two-phase flows using particle image velocimetry

Liquid-phase turbulence plays a vital role in determining various gas-liquid two-phase flow parameters, such as void fraction distribution, bubble morphology, bubble-bubble interactions, and interfacial area concentration. In this study, a two-phase flow database including both the gas- and liquid-phases measurements was developed focusing on three bubbly flow conditions in an air-water two-phase flow loop with a vertical one-inch diameter circular pipe test section. A particle image velocimetry (PIV) system integrating an optical phase separation method, i.e., the planar laser-induced fluorescence (PLIF) technique using fluorescent particles and optical filtration, was applied to measure the liquid-phase turbulence information, including the time-averaged velocity, Reynolds stress, and turbulent kinetic energy for the liquid phase. The PIV measurements were taken at three ports along the test section at 14.5, 51.5, and 88.5 pipe inner diameters downstream of a bubble injector. In addition, a double-sensor conductivity probe was used to measure radial distributions of the local time-averaged void fraction and gas velocity. The measured liquid-phase turbulence was used to benchmark Sato's turbulence model considering the bubble-induced shear stress for the three tested bubbly flows. The benchmark results showed good agreement between the PIV measurements and model predictions. Lastly, in the two bubbly flows tested that have low void fractions being less than 3%, the effect of the bubble-induced turbulence was found not significant. However, the bubble-induced shear stress becomes important with the increase of the void fraction.

42 ENGINEERING↗
DOE OSTI2020

Development of a Comprehensive Two-Phase Flow Database for the Validation of NEK-2P

Three-dimensional (3-D) two-phase Computational Fluid Dynamics (CFD) codes are emerging as a powerful and potentially practical tool for applications in which detailed local flow information is needed. However, two-phase flow models and associated closure relations are not well established for CFD applications, which is partly due to the lack of high-quality validation data. The main objective of this work is to develop a comprehensive database of two-phase flows that can be used to validate two-phase CFD codes such as NEK-2P. In this project, four advanced local measurement systems, including Particle Image Velocimetry and Planar Laser-Induced Fluorescence (PIV-PLIF), high-speed imaging, x-ray densitometry, and multi-sensor conductivity probe are employed to measure the local two-phase flow parameters of both gas and liquid phases. By combining these techniques, the local void fraction, bubble velocity, interfacial area concentration, bubble frequency, liquid velocity, turbulence intensity, etc., in various two-phase flow regimes can be obtained. These local measurement techniques are first used in a 25.4 mm circular pipe test section. Seven air-water two-phase flow conditions spanning the bubbly, slug, churn-turbulent, and annular flow regimes are measured in this facility. The obtained database contains the radial profiles of both gas- and liquid-phase parameters at three axial locations along the test section. The second facility used in this work contains a 30 mm × 10 mm rectangular test section, in which three two-phase flow and two single-phase flow conditions are measured. Two-dimensional distributions of local two-phase flow parameters in the cross-sectional plane are measured in this facility at three axial locations as well. A facility featuring a 3×3 electrically heated rod bundle is also designed and being constructed in this project. This facility is specially designed for optical measurements and is expected to provide high-quality boiling data in the future. Preliminary analyses have been performed for the data taken in the 25.4 mm circular pipe. Both center-peaked and wall-peaked void fraction profiles have been observed in the data depending on the two-phase flow conditions and/or developing lengths. The 1-D drift-flux model was evaluated with the newly obtained datasets, in which both gas- and liquid-phases data were directly measured. The distribution parameter model has been optimized based on a new void-profile classification method proposed in this study. The optimized drift-flux model shows a significant improvement in predicting both gas velocity and void fraction. The measured liquid-phase turbulence was used to benchmark Sato’s turbulence model considering the bubble-induced shear stress for the three tested bubbly flows. The benchmark results showed good agreement between the PIV measurements and model predictions. In the bubbly flows tested that have low void fractions less than 3%, the effect of the bubble-induced turbulence was found not significant. However, the bubble-induced shear stress becomes important with the increase of the void fraction. The Conjugate Heat Transfer (CHT) model was developed and implemented in NEK-2P. This model allows the coupled simulation of the solid domain and two-phase fluid domain, allowing the specification of realistic boundary conditions. The CHT implementation was verified first with non-boiling simulations. The predicted temperatures in the fluid domain were shown to be identical in simulations with or without the CHT model. The CHT model was then validated through simulations of three Becker benchmark CHF tests performed under both Dryout (DO) and Departure from Nucleate Boiling (DNB) conditions. Reasonably good agreement was obtained between calculated wall temperatures and corresponding experimental data.

42 ENGINEERING↗
DOE OSTI2020

Hydrogen sensing and separation

Techniques for hydrogen sensing and mitigation are provided. As one example, a device includes a chamber and a membrane that is permeable to a first gas and is impermeable to a second gas. The membrane separates the chamber from a gas mixture that contains the first gas, such that the first gas in the gas mixture can only enter the chamber via the membrane. The device also includes a pressure sensor configured to measure a pressure within the chamber.

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

Alaska North Slope 2018 Hydrate-01 Stratigraphic Test Well: Technical Results

The Hydrate-01 Stratigraphic Test Well was drilled in December 2018 to confirm that a seismically-identified location within the western Prudhoe Bay Unit might be suitable for extended-duration scientific production testing. The well featured the acquisition of a full suite of logging-while-drilling data, collection of side-wall pressure cores, and installation of distributed temperature and distributed acoustic sensor (DTS, DAS) fiber-optic cables. Evaluation of the logging-while-drilling data confirms gas hydrate is present at high saturation in two target sands of high reservoir quality. Acquired logs and samples enable critical petrophysical information to be compiled for input into production models. The well tested two primary targets: the deeper Unit B is highly favorable due to optimal reservoir temperature and minimal observed risk for direct communication with permeable, hydrate-free water-bearing zones. The shallower Unit D provides a secondary target and opportunity to

Boswell, Ray↗
DOE OSTI2021

Sublimation temperature sensor for temperature locale

The sublimation temperature sensor (or “sublime sensor”) provides a continuum of measurement locations in which certain maximum temperatures are achieved during a heat up/cool down cycle. A predetermined material is encapsulated within a vacuum-sealed, non-volatile long tube (i.e., both ends capped and L$\gg$ D). This assembly is then inserted and centered into a heated zone, such as a furnace, exhaust pipe, or reactor. As the temperature increases, the material will sublimate (i.e., a process of having both the solid and gaseous states of matter simultaneously present) and will begin to fill the void - moving outward in both directions toward the ends of the tube. Once beyond the elevated temperatures, the gas will de-sublimate (i.e., deposition) onto the inner wall of the tube. The desired result of the sensor is the ring of material that develops over a relatively short period of time. This material deposit can be equated with temperature at an exact location. There is no need to interpolate and/or extrapolate for the desired measurement. Accuracy has been recorded for temperature locations on the range of ±2 mm over a 1 m span. Likewise, the precision of the measurement is ±0.2% the overall sensor domain. Furthermore, individual tubes with unique materials and pressures can be bundled together to provide a complete temperature profile of the heated zone.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2024

Field demonstration of a tracer method to track simulated exhaled air trajectories and mixing in three connected rooms with upper-room GUV

We conducted tracer gas experiments in three connected nursery rooms to track simulated exhaled air trajectories and mixing. We emulated exhaled air with a pulse release of ethanol and measured its concentration with 2s time resolution with spatially distributed metal oxide sensors in both the upper and occupied levels of the room. We found that the overhead cooling supply air enhanced vertical air mixing within the rooms. When a room had higher supply airflow than the others, the tracer gas mixed quickly in that room because the supply air dominated the room mixing, thereby isolating it from air mixing with the other rooms. When the forced air system was not operating, the tracer gas resided longer in the upper room before descending. The tracer method also depicts air trajectories from different release locations by detecting the release point and affecting nearby sensors. These experiments support the potential of this method to be used in the field for understanding air trajectories in rooms with upper-room GUV.

Um, Chai Yoon↗
DOE OSTI2026

Predicting multiphase flow and tracer transport for an underground chemical explosive test

Detecting radionuclide gas seepage from clandestine underground nuclear tests is central to nonproliferation explosion monitoring research. Yet, early-time (<6 day) gas transport driven by the explosive pressure wave remains poorly constrained due to scarcity of field data. We simulate multi-phase gas transport in the vadose zone using pre-shot data from a recent chemical explosion in P-Tunnel at the Nevada National Security Site, USA. Despite using a simplified 2D-radial model, predictions of tracer arrival matched observations within one order-of-magnitude. Our results show how transient blast forcing rapidly mobilizes gases from the cavity into surrounding rock – critical for optimizing sensor placement and test planning. This unique integration of field data and modeling represents a significant improvement in our ability to predict gas migration from underground explosions. More broadly, it offers insights into the coupled dynamics of pressure waves and contaminant transport in the vadose zone, with implications for monitoring and hazard assessment.

54 ENVIRONMENTAL SCIENCES↗