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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 379 records · Page 21
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Constructing Ultrathin W-Doped NiFe Nanosheets via Facile Electrosynthesis as Bifunctional Electrocatalysts for Efficient Water Splitting

Exploring cost-effective and efficient bifunctional electrocatalysts via simple fabrication strategies is strongly desired for practical water splitting. Herein, an easy and fast one-step electrodeposition process is developed to fabricate W-doped NiFe (NiFeW)-layered double hydroxides with ultrathin nanosheet features at room temperature and ambient pressure as bifunctional catalysts for water splitting. Notably, the NiFeW nanosheets require overpotentials of only 239 and 115 mV for the oxygen evolution reaction (OER) and hydrogen evolution reaction (HER), respectively, to reach a current density of 10 mA/cm 2 in alkaline media. Their exceptional performance is further demonstrated in a full electrolyzer configuration with the NiFeW as both anode and cathode catalysts, which achieves a low cell voltage of 1.59 V at 10 mA/cm 2 , 110 mV lower than that of the commercial IrO 2 (anode) and Pt (cathode) catalysts. Moreover, the NiFeW nanosheets are superior to various recently reported bifunctional electrocatalysts. Such remarkable performances mainly ascribe to W doping, which not only effectively modulates the electrocatalyst morphology but also engineers the electronic structure of NiFe hydroxides to boost charge-transfer kinetics for both the OER and HER. Hence, the ultrathin NiFeW nanosheets with an efficient fabrication strategy are promising as bifunctional electrodes for alkaline water electrolyzers.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2023

Scoping Study for Fast Flux Testing in the Advanced Test Reactor

The value of fast spectrum reactors remains prominent in the nuclear technology portfolio. The performance of these reactors can be maximized with advancements in nuclear fuel technologies, but development of these technologies is currently held back by lack of fast spectrum test reactors available to the United States. Spectral modification of experiment positions in the thermal spectrum Advanced Test Reactor (ATR) has long been used to support fast reactor fuel development, but these methods have not been progressed to their full potential. This study investigated the use of concentric rings of aluminum-clad fuel plates in ATR flux traps and thermal neutron absorbing filters to increase fast neutron flux on test specimens. This concept was termed the Boosted Energy Advanced Spectrum Test (BEAST). This approach will enable irradiation of advanced fuel designs in prototypic-length fuel pins and representative flux environment to support post irradiation exams, enable transient testing, and produce the type of data that will permit lead test assembly irradiations in true Sodium Fast Reactors (SFRs) when they become available. Neutronic predictions were performed to investigate BEAST design options and thermal hydraulic models were produced to ensure feasibility of BEAST. Two versions were considered based on the geometric limitations of ATR’s small and large flux traps. The small version was found to be preferable due to slightly higher fast flux and fast-to-thermal neutron ratio. Perhaps more influentially, the small flux trap option was also preferred to avoid conflict with ongoing very high temperature reactor fuel irradiation programs in ATR’s large northeast flux trap. The small flux trap option provided less than half the test volume of the large version, but still had adequate volume for seven SFR pins in cross section which could be stacked two-high in ATR’s 1.2m long core to accommodate up to 14 EBR-II size pins. The preference for the small flux trap configuration should be revisited if additional collaborative test programs emerge with the need to irradiate a significant volume of additional specimens. Calculations were performed regarding a lithium deuteride ring to convert thermal neutrons into 14 MeV fusion neutrons. At the time this report was written these calculations were partially complete and it remains to be seen whether the concept would be worth including in BEAST. Given the preference for the small flux trap option, which does not afford enough room for the 14 MeV ring, it was concluded to defer future work on the lithium deuteride ring. This decision could be revisited if fusion material research programs emerge for collaborative testing in BEAST. A cadmium-lined specimen holder design was found to be adequate in filtering thermal neutrons and preferred over other neutron absorbers based on past experience with cadmium baskets. It was acknowledged that cadmium-bearing hardware would become depleted and need to be replaced occasionally, which appeared feasible from a mechanical design perspective. Neutronic studies investigated different enrichment levels in the booster fuel using uranium-molybdenum alloy dispersion fuel which has performed well in past ATR irradiations. Both options were able to drive fuel pins to SFR-like fission heating rates. The high enriched booster fuel option outperformed the low enriched option by ~20% on key metrics including fast flux and fast-to-thermal ratio, but the low enriched option was favored in order to broaden options for potential fuel suppliers. The preferred BEAST design options including cadmium filter with low enriched booster fuel in the small flux trap configuration was predicted to achieve 6.2E14 n/cm2sec fast flux (>0.1 MeV) with a fast-to-thermal ratio of 44.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2024

Simulated wildfire burned area over the CONUS during 2001-2020

Wildfires have shown increasing trends in both frequency and severity across the Contiguous United States (CONUS). However, process-based fire models have difficulties in accurately simulating the burned area over the CONUS due to a simplification of the physical process and cannot capture the interplay among fire, ignition, climate, and human activities. The deficiency of burned area simulation deteriorates the description of fire impact on energy balance, water budget, and carbon fluxes in the Earth System Models (ESMs). Alternatively, machine learning (ML) based fire models, which capture statistical relationships between the burned area and environmental factors, have shown promising burned area predictions and corresponding fire impact simulation. We develop a hybrid framework (ML4Fire-XGB) that integrates a pretrained eXtreme Gradient Boosting (XGBoost) wildfire model with the Energy Exascale Earth System Model (E3SM) land model (ELM). A Fortran-C-Python deep learning bridge is adapted to support online communication between ELM and the ML fire model. Specifically, the burned area predicted by the ML-based wildfire model is directly passed to ELM to adjust the carbon pool and vegetation dynamics after disturbance, which are then used as predictors in the ML-based fire model in the next time step. Evaluated against the historical burned area from Global Fire Emissions Database 5 from 2001-2020, the ML4Fire-XGB model outperforms process-based fire models in terms of spatial distribution and seasonal variations. Sensitivity analysis confirms that the ML4Fire-XGB well captures the responses of the burned area to rising temperatures. The ML4Fire-XGB model has proved to be a new tool for studying vegetation-fire interactions, and more importantly, enables seamless exploration of climate-fire feedback, working as an active component in E3SM.

Liu, Ye↗
DOE OSTI2023

Quantum Efficiency Gain in 2D Perovskite Photo and X‐Ray Detectors

Abstract The perovskite polycrystalline thin film detector fabricated by solution method is a promising low‐cost, scalable technology for radiation imaging, but its thin volume limits the sensing efficiency for high‐energy X‐ray photons. This work reports 2D perovskite thin film photo‐diodes with a detection gain when sensing visible and X‐ray photons. Detailed power and temperature‐dependent device characterizations reveal a charge multiplication effect to be responsible for the observed high gain. This is caused by a disparity in electron and hole transport, where electron transport is retarded by a trap/de‐trap process via shallow trap states, whereas the hole transport is fast enough to produce a photoconductive gain in satisfying the charge neutrality. The 2D perovskite made with butylamine spacers is also found to exhibit a larger efficiency gain than those made with phenylethylamines because of the higher likelihood of forming shallow traps in the former. The thin film diodes feature a high temporal response over 1 MHz due to the fast charge collection across a thin volume, and the discovery provides device physics mechanisms in connection to material structure‐function relationship for future optoelectronics design that can boost the efficiency of X‐ray sensing and dim light detection.

2D perovskites↗
DOE OSTI2024

Improving North American Wildfire Prediction by Integrating a Machine-Learning Fire Model in a Land Surface Model

Wildfires have shown increasing trends in both frequency and severity across the Contiguous United States (CONUS). However, process-based fire models have difficulties in accurately simulating the burned area over the CONUS due to a simplification of the physical process and cannot capture the interplay among fire, ignition, climate, and human activities. The deficiency of burned area simulation deteriorates the description of fire impact on energy balance, water budget, and carbon fluxes in the Earth System Models (ESMs). Alternatively, machine learning (ML) based fire models, which capture statistical relationships between the burned area and environmental factors, have shown promising burned area predictions and corresponding fire impact simulation. We develop a hybrid framework (ML4Fire-XGB) that integrates a pretrained eXtreme Gradient Boosting (XGBoost) wildfire model with the Energy Exascale Earth System Model (E3SM) land model (ELM) version 2.1. A Fortran-C-Python deep learning bridge is adapted to support online communication between ELM and the ML fire model. Specifically, the burned area predicted by the ML-based wildfire model is directly passed to ELM to adjust the carbon pool and vegetation dynamics after disturbance, which are then used as predictors in the ML-based fire model in the next time step. Evaluated against the historical burned area from Global Fire Emissions Database 5 from 2001-2020, the ML4Fire-XGB model outperforms process-based fire models in terms of spatial distribution and seasonal variations. Sensitivity analysis confirms that the ML4Fire-XGB well captures the responses of the burned area to rising temperatures. The ML4Fire-XGB model has proved to be a new tool for studying vegetation-fire interactions, and more importantly, enables seamless exploration of climate-fire feedback, working as an active component in E3SM.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Predicting Biomass Yields of Advanced Switchgrass Cultivars for Bioenergy and Ecosystem Services Using Machine Learning

The production of advanced perennial bioenergy crops within marginal areas of the agricultural landscape is gaining interest due to its potential to sustainably produce feedstocks for biofuels and bioproducts while also improving the sustainability and resilience of commodity crop production. However, predicting the biomass yields of this production system is challenging because marginal areas are often relatively small and spread around agricultural fields and are typically associated with various abiotic conditions that limit crop production. Machine learning (ML) offers a viable solution as a biomass yield prediction tool because it is suited to predicting relationships with complex functional associations. The objectives of this study were to (1) evaluate the accuracy of commonly applied ML algorithms in agricultural applications for predicting the biomass yields of advanced switchgrass cultivars for bioenergy and ecosystem services and (2) determine the most important biomass yield predictors. Datasets on biomass yield, weather, land marginality, soil properties, and agronomic management were generated from three field study sites in two U.S. Midwest states (Illinois and Iowa) over three growing seasons. The ML algorithms evaluated in the study included random forests (RFs), gradient boosting machines (GBMs), artificial neural networks (ANNs), K-neighbors regressor (KNR), AdaBoost regressor (ABR), and partial least squares regression (PLSR). Coefficient of determination (R 2 ) and mean absolute error (MAE) were used to evaluate the predictive accuracy of the tested algorithms. Results showed that the ensemble methods, RF (R 2 = 0.86, MAE = 0.62 Mg/ha), GBM (R 2 = 0.88, MAE = 0.57 Mg/ha), and GBM (R 2 = 0.78, MAE = 0.66 Mg/ha), were the most accurate in predicting biomass yields of the Independence, Liberty, and Shawnee switchgrass cultivars, respectively. This is in agreement with similar studies that apply ML to multi-feature problems where traditional statistical methods are less applicable and datasets used were considered to be relatively small for ANNs. Consistent with previous studies on switchgrass, the most important predictors of biomass yield included average annual temperature, average growing season temperature, sum of the growing season precipitation, field slope, and elevation. This study helps pave the way for applying ML as a management tool for alternative bioenergy landscapes where understanding agronomic and environmental performance of a multifunctional cropping system seasonally and interannually at the sub-field scale is critical.

09 BIOMASS FUELS↗
NASA NTRS1991

X ray microscope/telescope test and alignment

The tasks performed by the Center for Applied Optics (CAO) in support of the Normal Incidence Multilayer X-Ray Optics Program are detailed. The Multi-Spectral Solar Telescope Array (MSSTA) was launched on a Terrier-boosted Black Brant sounding rocket from White Sands Missile Range on 13 May 1991. High resolution images of the sun in the soft x ray to extreme ultraviolet (EUV) regime were obtained with normal-incidence Cassegrain, Ritchey-Chretien, and Herschelian telescopes mounted in the sounding rocket. MSSTA represents the first use of multilayer optics to study a very broad range of x ray and EUV solar emissions. Energy-selective properties of multilayer-coated optics allow distinct groups of emission lines to be isolated in the solar corona and transition region. Features of the near and far coronal structures including magnetic loops of plasmas, coronal plumes, coronal holes, faint structures, and cool prominences are visible in these images. MSSTA successfully obtained unprecedented information regarding the structure and dynamics of the solar atmosphere in the temperature range of 10(exp 4)-10(exp 7) K. The performance of the MSSTA has demonstrated a unique combination of ultra-high spatial resolution and spectral differentiation by use of multilayer optics.

Walker, Arthur B. C.↗
NASA NTRS2021

Aqua Factorem: Ultra Low Energy Lunar Water Extraction

We propose a new method we call Aqua Factorem to extract lunar water. It will drastically reduce energy and complexity of lunar mining operations helping to establish this industry. Other methods to extract water from the regolith are based on phase change, pumping energy into the regolith to sublimate the ice into vapor, then capturing the vapor, re-freezing it, and hauling the solid ice to a chemical processor where it is converted again into vapor for purification then electrolysis. This requires transporting high power into the permanently shadowed regions (PSRs) not only to vaporize the ice in situ in the soil but to heat the surrounding soil itself, which is wasted energy. Still other methods are based on strip mining and hauling the resource along with slag (the unwanted silicates, which constitutes about 95% of the mass), to a processing unit that is located outside the PSRs. That may be a simpler architecture but is generally not favored because hauling 49,000 t of regolith over many kilometers yearly to produce the projected demand of 2,450 t of propellant would be terribly difficult and expensive with high risk that the vehicles will get stuck driving with such loads across the unprepared regolith. We propose an alternative to both of these approaches, taking advantage of the processing that the unique lunar geology has already performed. Micrometeoroid bombardment has already broken most solid material in the upper part of the regolith into fine grains. This includes solid material of all compositions, including the ice, which is as hard as granite at PSR temperatures and is therefore essentially another type of rock. These ice grains are intermixed with all the other minerals, so a simple, ultra-low-energy grain-sorting process can extract the ice without phase change. As another benefit it can extract the 1 wt% free metal known to be in lunar soil, again with very little energy. The ice can then be hauled to the chemical processing unit in solid phase and converted into rocket propellant. We estimate the 800 kW power needed for thermal extraction can be reduced to less than 100 watts using the new method. This affects the entire architecture of the mining operation producing extensive economic benefit, which we will quantify in this study. We will study it in the context of a mission to mine propellants commercially for space tugs that boost commercial communication satellites from Geosynchronous Transfer Orbit (GTO) to Geostationary Orbit (GEO) then return to the lunar surface for refueling. This simple architecture requires the minimum number of in-space elements, and notably does not require an in-space propellant depot, so it provides the lowest cost and lowest risk startup for a commercial operation. The study will also test the innovative Aqua Factorem process through laboratory experiments, and this will produce basic insights into the handling of lunar resources.

lunar↗
NASA NTRS2004

Atlas V Launch Incorporated NASA Glenn Thermal Barrier

In the Spring of 2002, Aerojet experienced a major failure during a qualification test of the solid rocket motor that they were developing for the Atlas V Enhanced Expendable Launch Vehicle. In that test, hot combustion gas reached the O-rings in the nozzle-to-case joint and caused a structural failure that resulted in loss of the nozzle and aft dome sections of the motor. To improve the design of this joint, Aerojet decided to incorporate three braided carbon-fiber thermal barriers developed at the NASA Glenn Research Center. The thermal barriers were used to block the searing-hot 5500 F pressurized gases from reaching the temperature-sensitive O-rings that seal the joint. Glenn originally developed the thermal barriers for the nozzle joints of the space shuttle solid rocket motors, and Aerojet decided to use them on the basis of the results of several successful ground tests of the thermal barriers in the shuttle rockets. Aerojet undertook an aggressive schedule to redesign the rocket nozzle-to-case joint with the thermal barriers and to qualify it in time for a launch planned for the middle of 2003. They performed two successful qualification tests (Oct. and Dec. 2002) in which the Glenn thermal barriers effectively protected the O-rings. These qualification tests saved hundreds of thousands of dollars in development costs and put the Lockheed-Martin/Aerojet team back on schedule. On July 17, 2003, the first flight of an Atlas V boosted with solid rocket motors successfully launched a commercial satellite into orbit from Cape Canaveral Air Force Station. Aero-jet's two 67-ft solid rocket boosters performed flawlessly, with each providing thrust in excess of 250,000 lbf. Both motors incorporated three Glenn-developed thermal barriers in their nozzle-to-case joints. The Cablevision satellite launched on this mission will be used to provide direct-to-home satellite television programming for the U.S. market starting in late 2003. The Atlas V is a product of the military's Enhanced Expendable Launch Vehicle program designed to provide assured military access to space. It can lift payloads up to 19,100 lb to geosynchronous transfer orbit and was designed to meet Department of Defense, commercial, and NASA needs. The Atlas V and Delta IV are two launch systems being considered by NASA to launch the Orbital Space Plane/Crew Exploration Vehicle. The launch and rocket costs of this mission are valued at $250 million. Successful application of the Glenn thermal barrier to the Atlas V program was an enormous breakthrough for the program's technical and schedule success.

Dunlap, Patrick H., Jr.↗
DOE OSTI2024

Chemical composition based machine learning model to predict defect formation in additive manufacturing

With a goal of exploiting additive manufacturing to improve the manufacturing of existing reactor materials, we developed a chemical composition-based machine learning model to predict the printability of any given alloy in laser powder bed fusion (L-PBF) using experimental data from peer-reviewed literature. We defined printability as the ability to avoid defects like cracking, balling, porosity, and lack of fusion, that are caused by thermal stresses (during solidification or liquation), molten pool disintegration into disconnected small beads or lack of heat input respectively. Our models predict the tendency of balling defect formation and porosity percentage for a given composition, under a given set of processing conditions. To predict the likelihood of balling defect, three models: a random forest classifier, a gradient boost regressor and a neural network were trained on a dataset containing both traditional alloys and high entropy alloys. The neural network model showed the highest accuracy of 92.3 % in predicting the balling defect formation. A random forest regressor, gradient boost regressor and neural network were trained and tested on a dataset of various alloys to predict porosity. The random forest regressor showed the best predictions with an R 2 score of 0.97. The models also revealed the relative importance of the input descriptors on defect-formation tendency. Of particular significance was the identification of carbon as an important element in determining the occurrence of balling and percent porosity in alloys like steel, as well as being moderately important to the percentage porosity in other alloys as well as steel. Manganese was also identified as a key descriptor for the percentage of porosity in steel and other alloys. Manganese’s low thermal conductivity and consistent presence in the dataset is the likely cause for its contribution. Carbon’s role is attributable to its relatively high specific heat and high melting temperature. In conclusion, our model serves as a swift, chemistry-based tool to design experiments and find modified compositions better suited for additive manufacturing.

36 MATERIALS SCIENCE↗
DOE OSTI2021

An Approach to Bayesian Optimization for Design Feasibility Check on Discontinuous Black-Box Functions

The paper presents a novel approach to applying Bayesian Optimization (BO) in predicting an unknown constraint boundary, also representing the discontinuity of an unknown function, for a feasibility check on the design space, thereby representing a classification tool to discern between a feasible and infeasible region. Bayesian optimization is a low-cost black-box global optimization tool in the Sequential Design Methods where one learns and updates knowledge from prior evaluated designs, and proceeds to the selection of new designs for future evaluation. However, BO is best suited to problems with the assumption of a continuous objective function and does not guarantee true convergence when having a discontinuous design space. This is because of the insufficient knowledge of the BO about the nature of the discontinuity of the unknown true function. In this paper, we have proposed to predict the location of the discontinuity using a BO algorithm on an artificially projected continuous design space from the original discontinuous design space. The proposed approach has been implemented in a thin tube design with the risk of creep-fatigue failure under constant loading of temperature and pressure. The stated risk depends on the location of the designs in terms of safe and unsafe regions, where the discontinuities lie at the transition between those regions; therefore, the discontinuity has also been treated as an unknown creep-fatigue failure constraint. The proposed BO algorithm has been trained to maximize sampling toward the unknown transition region, to act as a high accuracy classifier between safe and unsafe designs with minimal training cost. The converged solution has been validated for different design parameters with classification error rate and function evaluations at an average of <1% and ~150, respectively. Finally, the performance of our proposed approach in terms of training cost and classification accuracy of thin tube design is shown to be better than the existing machine learning (ML) algorithms such as Support Vector Machine (SVM), Random Forest (RF), and Boosting.

Engineering↗
DOE OSTI2025

EVs@Scale Next-Gen Profiles - EV Profile Capture 2024

As part of the U.S. DOE EVs@Scale consortium Next-Gen Profiles (NGP) project, the profile capture and analysis of production electric vehicles undergoing high power charging (HPC) is conducted over a wide range of conditions to explore variance and performance. Charge session parameters are collected from both the electric vehicle (EV) and electric vehicle supply equipment (EVSE) at a rate of 10Hz and entered into a time-series database for analysis. These charge profiles are captured under nominal and off-nominal conditions, exploring the impact of battery state of charge (SOC), battery temperature, vehicle condition, smart charge management (SCM), and EVSE limitations. Nominal conditions are defined to be ideal conditions that should transfer the maximum allowable energy in the minimum possible amount of time. Nominal condition profiles are compared across EVs to characterize state-of-the-art EV charging performance against one another. Off-nominal condition profiles are compared against its nominal condition profile counterpart to highlight the variance across less desirable starting conditions within a single EV. This EV Profile Capture 2024 report stands as an update from the EV Profile Capture 2023 report to include the additional EV & EVSE assets tested and analyzed in 2024. The major updates within this report include the addition of three next-generation electric vehicles, added test cases, and further analysis. This expansion of analysis includes power profiles, power distribution, quantifying SOC, energy and range performance, EVSE limitation impacts, boost converter performance, etc. Additionally, NGP time-series data has been used as input towards three national laboratory-led grid modelling efforts: ANL’s IEEE-37 HIL model, INL’s Caldera model, and NREL’s EVI-X model. A summary of these platforms and how NGP has worked to improve their effectiveness has also been added to this years’ report.

Thurston, Sam↗
DOE OSTI2022

A High-Voltage High-Reliability Scalable Architecture for Electric Vehicle Power Electronics (Final Report)

This project developed and demonstrated new composite converter technologies that lead to high power density (> 20 kW/L) at power levels of 10s of kW, 100s of kW, or possibly higher, with fundamental advances in converter efficiency and Q that lead to substantial increases in mean time to failure (MTTF). These advantages were realized through development of new composite converter topologies that perform buck, boost, or other conversion functions and that are scalable to higher voltage and power levels through sharing of voltage and current stresses among multiple dissimilar partial-power converter modules. The project led to experimental demonstration of a125 kW multifunction electric vehicle power conversion system having in-creased dc bus voltage (950 V nominal, 1200 V peak) that interfaces a 200 V to 400 V battery pack, and that includes integrated level 2 wired charging and wireless charging functions. The project incorporated SiC MOSFET modules having switching frequencies in excess of 100 kHz, planar magnetics, a hierarchical control architecture that enables scaling to higher voltages and powers with additional converter modules, and a high-power density in excess of 20 kW/L. The research demonstrated how a more complex converter approach can increase mean-time-to-failure, even though the number of elements is increased. This is achieved through significant reduction of temperature rise through fundamentally superior converter circuit topologies. The research also demonstrated new high power planar magnetics that increase power density. The technology is appropriate to a variety of applications including EV power trains, EV charging, PV inverters, battery storage, and similar areas. These systems potentially can be manufactured in the U.S.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2023

Improving Proton Conductivity by Navigating Proton Trapping in High Scandium-Doped Barium Zirconate Electrolytes

Proton-conducting oxides are used in intermediate-temperature (300 °C < T < 700 °C) applications of fuel cells, electrolyzers, membrane reactors, hydrogen pumps, and sensors. The proton conductivity of doped ABO 3 perovskites is partly dependent on the proton concentration and hence on the level of material hydration. However, how the material hydration ability is affected by differences in doping is not fully understood. Here, we show the prospect of proton trapping and detrapping that influences material hydration and dehydration. The proton-trapping influence is significant for Sc-doped BaZrO 3 compared to Y-doped BaZrO 3 . Here, our work offers a perspective to understand how defect interaction/associations influence the hydration ability of proton-conducting oxides. Furthermore, positron annihilation lifetime spectroscopy was revealed to be a valuable technique for studying the proton-trapping phenomena and the defect chemistry of dense proton conductors. Density functional theory calculations also showed that a high hydration level in Sc-doped BaZrO 3 boosts proton migration. In contrast, the effect of boosting the proton migration due to increased hydration is limited for Y-doped BaZrO 3 . This effect, in turn, might explain the trend of conductivity with dopant concentration in doped BaZrO 3 .

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

Active analog tuning of the phase of light in the visible regime by bismuth-based metamaterials

The active and analog tuning of the phase of light by metamaterials is needed to boost the switching performance of photonic devices. However, demonstrations of this type of tuning in the pivotal visible spectral region are still scarce. Herein, we report the active analog tuning of the phase of visible light reflected by a bismuth (Bi)-based metamaterial, enabled by a reversible solid-liquid transition. This metamaterial, fabricated by following a lithography-free approach, consists of twodimensional assemblies of polydisperse plasmonic Bi nanostructures embedded in a refractory and transparent aluminum oxide matrix. The analog tuning of the phase is achieved by the controlled heating of the metamaterial to melt a fraction of the nanostructures. A maximum tuning of 320° (1.8 π) is observed upon the complete melting of the nanostructures at 230°C. This tuning is reversible by cooling to 25°C. In addition, it presents a wide hysteretic character due to liquid Bi undercooling. This enables the phase achieved by this analog approach to remain stable over a broad temperature range upon cooling and until resolidification occurs around 100°C. Therefore, Bi-based metamaterials are endowed with analog optical memory capabilities, which are appealing for a wide range of applications, including optical data storage with enhanced information density or bistable photonic switching with a tunable “on” state.

36 MATERIALS SCIENCE↗
NASA NTRS2011

Sloshing of the Magnetized Cool Gas in the Cores of Galaxy Clusters

X-ray observations of many clusters of galaxies reveal the presence of edges in surface brightness and temperature, known as "cold fronts". In relaxed clusters with cool cores, these edges have been interpreted as evidence for the "sloshing" of the core gas in the cluster's gravitational potential. The smoothness of these edges has been interpreted as evidence for the stabilizing effect of magnetic fields "draped" around the front surfaces. To check this hypothesis, we perform high-resolution magnetohydrodynamics simulations of magnetized gas sloshing in galaxy clusters initiated by encounters with subclusters. We go beyond previous works on the simulation of cold fronts in a magnetized intracluster medium by simulating their formation in realistic, idealized mergers with high resolution ((Delta)x approx. 2 kpc). Our simulations sample a parameter space of plausible initial magnetic field strengths and field configurations. In the simulations, we observe strong velocity shears associated with the cold fronts amplifying the magnetic field along the cold front surfaces, increasing the magnetic field strength in these layers by up to an order of magnitude, and boosting the magnetic pressure up to near-equipartition with thermal pressure in some cases. In these layers, the magnetic field becomes strong enough to stabilize the cold fronts against Kelvin-Helmholtz instabilities, resulting in sharp, smooth fronts as those seen in observations of real clusters. These magnetic fields also result in strong suppression of mixing of high and low-entropy gas in the cluster, seen in our simulations of mergers in the absence of a magnetic field. As a result, the heating of the core due to sloshing is very modest and is unable to stave off a cooling catastrophe.

ZuHone, J. A.↗
NASA NTRS2008

LED Device Illuminates New Path to Healing

Among NASA s research goals is increased understanding of factors affecting plant growth, including the effects of microgravity. Impeding such studies, traditional light sources used to grow plants on Earth are difficult to adapt to space flight, as they require considerable amounts of power and produce relatively large amounts of heat. As such, an optimized experimental system requires much less energy and reduces temperature variance without negatively affecting plant growth results. Ronald W. Ignatius, founder and chairman of the board at Quantum Devices Inc. (QDI), of Barneveld, Wisconsin, proposed using light-emitting diodes (LEDs) as the photon source for plant growth experiments in space. This proposition was made at a meeting held by the Wisconsin Center for Space Automation and Robotics, a NASA-sponsored research center that facilitates the commercialization of robotics, automation, and other advanced technologies. The Wisconsin group teamed with QDI to determine whether an LED system could provide the necessary wavelengths and intensities for photosynthesis, and the resultant system proved successful. The center then produced the Astroculture3, a plant growth chamber that successfully incorporated this LED light source, which has now flown on several space shuttle missions. NASA subsequently identified another need that could be addressed with the use of LEDs: astronaut health. A central concern in astronaut health is maintaining healthy growth of cells, including preventing bone and muscle loss and boosting the body s ability to heal wounds all adversely affected by prolonged weightlessness. Thus, having determined that LEDs can be used to grow plants in space, NASA decided to investigate whether LEDs might be used for photobiomodulation therapy (PBMT).

Source record↗
DOE OSTI2020

THE EFFECT OF FUEL REACTIVITY AND EXHAUST GAS RECIRCULATION ON KNOCK PROPENSITY OF NATURAL GAS

The development of high efficiency, spark ignited natural gas engines is currently limited by engine knock at high compression ratio/elevated boost pressures and misfire at lean conditions/high exhaust gas recirculation (EGR) levels. The knock and misfire limits are further confounded by the wide variety in fuel reactivity observed in “pipeline quality” natural gas. In this study, a rapid compression machine was used to characterize the effects of EGR and variation in natural gas fuel reactivity on the homogeneous ignition delay, flame propagation rate, and end- gas autoignition propensity for stoichiometric natural gas/oxidizer/EGR blends. A reduced chemical kinetic mechanism was also developed to accurately model the homogeneous ignition delays measured in the Colorado State University rapid compression machine (CSU RCM). Pipeline quality natural gas with a range of chemical reactivity (68 < Methane Number < 95) was simulated using mixtures of CH4, C2H6, and C3H8. Exhaust gas recirculation gases were simulated with mixtures of Ar, CO2, CO, and NO at substitution rates of 0 to 30 mass percent. Ignition delay period under homogeneous autoignition conditions was measured at compressed pressures of 30.2 to 34.0 bar and compressed temperatures of 667 to 980 K. End-gas autoignition fraction and flame propagation rate were measured by initiating a laser spark in the center of the combustion chamber, after compression, at pressures of 30.7 to 32.7 bar and temperatures of 751 to 795 K. The results indicate that both fuel reactivity and the presence of reactive species (NO and CO) in the exhaust gas recirculation have a strong impact on end-gas autoignition fraction. A chemical kinetic mechanism was developed to predict homogeneous ignition delays for pipeline quality natural gas in a pressure and temperature range of 1-100 bar and 500-1000 K respectively. This mechanism accurately predicted measured homogeneous ignition delay in the RCM with a total average relative error of 11.0%.

Mohr, Jeffrey↗