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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 145 records · Page 8
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

Autoignition behavior of gasoline/ethanol blends at engine-relevant conditions

Ethanol is an attractive oxygenate increasingly used for blending with petroleum-derived gasoline yielding beneficial combustion and emissions behavior for a range of internal combustion engine schemes, including stoichiometric spark-ignition and low temperature combustion (LTC). As such, it is important to fundamentally understand the autoignition behavior of gasoline/ethanol blends. This work utilizes a rapid compression machine (RCM) and a homogeneous charge compression ignition (HCCI) engine to experimentally quantify changes in fuel reactivity, through ignition delay times and preliminary heat release, for blends of 0 to 30% vol./vol. into a full boiling range research gasoline (FACE-F). Diluted/stoichiometric and undiluted/fuel-lean conditions are explored covering a wide range of compressed temperatures and pressures relevant to conventional and advanced, gasoline combustion engines. Detailed chemical kinetic modeling is undertaken using a recently updated gasoline surrogate model in conjunction with a five-component surrogate to model the RCM experiments and provide chemical insight into the perturbative effects of ethanol on the autoignition process. The diluted/stoichiometric RCM measurements reveal that within the low-temperature regime ethanol retards first-stage and main ignition delay times, and suppresses both the rates and extents of low-temperature heat release (LTHR), while within the intermediate-temperature regime ethanol only causes slight changes. Good agreement of ignition delay time and preliminary heat release prediction is found between model and experimental results. Sensitivity and flux analyses further show that ethanol blending effects are dominated by the competition between the H-atom abstraction from ethanol and other fuel components by OH radical at low temperatures and by HO 2 radical at intermediate temperatures. These findings are consistent across both fuel loading conditions explored in this study. In addition, when HCCI engine experiments are mapped onto undiluted/lean RCM measurements under a constant combustion phasing scenario, good correspondence between the two apparatuses is observed for LTHR and start of high-temperature heat release. Finally, the current study highlights the importance of characterizing LTHR in predicting fuel behaviors in high-boost/low-temperature engines, and demonstrates that RCM experiments can provide an alternative, and more-efficient avenue for such characterization.

02 PETROLEUM↗
DOE OSTI2021

Investigating the Impact of Thickness, Calendering and Channel Structures of Printed Electrodes on the Energy Density of LIBs - 3D Simulation and Validation

Current lithium ion batteries (LIBs) are expensive and bulky, limited by relatively low charging rates. To increase the rate of charging and reduce weight, thin electrodes with high energy density are required. The increase in energy density can be achieved by several techniques including boosting electrolyte transport, high loading/utilization of active material, employing high conductive electrolytes and electrodes with advanced architectures, and increasing cell temperature. In this paper, a 3D physics-based electrochemical model of LIBs is developed in COMSOL simulation software for different thickness, calendering steps as well as channel structures (conical, cylindrical) to optimize the electrode design and in turn maximize volumetric energy density. The simulation results demonstrated that calendering the electrodes with high initial porosity increases the volumetric energy density of the cell. In addition, cylindrical channel structures with relatively lower edge-to-edge distance also results in increased volumetric energy density. The simulation results of the 3D model was validated by comparing it with experimental results.

improving volumetric energy density↗
NASA NTRS2002

Magnetic Microspheres for Therapeutical Applications

Hyperthermia is a well known cancer therapy and consists of heating a tumor region to the elevated temperatures in the range of 40-45 C for an extended period of time (2-8 hours). This leads to thermal inactivation of cell regulatory and growth processes with resulting widespread necrosis, carbonization and coagulation. Moreover, heat boosts the tumor response to other treatments such as radiation, chemotherapy or immunotherapy. Of particular importance is careful control of generated heat in the treated region and keeping it localized. Higher heating, to about 56 C can lead to tissue thermo-ablation. With accurate temperature control, hyperthermia has the advantage of having minimal side effects. Several heating techniques are utilized for this purpose, such as whole body hyperthermia, radio-frequency (RF) hyperthermia, ultrasound technique, inductive microwave antenna hyperthermia, inductive needles (thermoseeds), and magnetic fluid hyperthermia (MFH).MFH offers many advantages as targeting capability by applying magnets. However, this technology still suffers significant inefficiencies due to lack of thermal control. This paper will provide a review of the topic and outline the ongoing work in this area. The main emphasis is in devising ways to overcome the technical difficulty in hyperthermia therapy of achieving a uniform therapeutic temperature over the required region of the body and holding it steady. The basic obstacle of the present heating methods are non-uniform thermal properties of the tissue. Our approach is to develop a novel class of magnetic fluids which have inherent thermoregulating properties. We have identified a few magnetic alloys which can serve as a suitable nano-particle material. The objective is to synthesize, characterize and evaluate the efficacy of TRMF for hyperthermia therapy.

Mazuruk, K.↗
NASA NTRS2003

Magnetic Microspheres and Tissue Model Studies for Therapeutical Applications

Hyperthermia is a well known cancer therapy and consists of heating a tumor region to the elevated temperatures in the range of 40-45 C for an extended period of time (2-8 hours). This leads to thermal inactivation of cell regulatory and growth processes with resulting widespread necrosis, carbonization and coagulation. Moreover, heat boosts the tumor response to other treatments such as radiation, chemotherapy or immunotherapy. Of particular importance is careful control of generated heat in the treated region and keeping it localized. Higher heating, to about 56 C can lead to tissue thermo-ablation. With accurate temperature control, hyperthermia has the advantage of having minimal side effects. Several heating techniques are utilized for this purpose, such as whole body hyperthermia, radio-frequency (RF) hyperthermia, ultrasound technique, inductive microwave antenna hyperthermia, inductive needles (thermoseeds), and magnetic fluid hyperthermia (MFH).MFH offers many advantages as targeting capability by applying magnets. However, this technology still suffers significant inefficiencies due to lack of thermal control. This paper will provide a review of the topic and outline the ongoing work in this area. The main emphasis is in devising ways to overcome the technical difficulty in hyperthermia breast therapy of achieving a uniform therapeutic temperature over the required region of the body and holding it steady for an extended period (2-3 hours). The basic shortcomings of the presently utilized heating methods stem from the non-uniform thermal properties of the tissue and the point heating characteristics of the techniques without any thermal control. Our approach is to develop a novel class of magnetic fluids, which have inherent thermoregulating properties. We have identified a few magnetic alloys which can serve as suitable nano to micron-size particle material. The objective is to synthesize, characterize and evaluate the efficacy of Thermo Regulating Magnetic Fluids (TRMF) for hyperthermia therapy. The development of a tissue model and testing the fluid dynamics of particle motion, settling, distribution in the tissue matrix and heat generation will be discussed.

Ramachandran, N.↗
DOE OSTI2021

Improving Data and Prediction Quality of High-Throughput Perovskite Synthesis with Model Fusion

Combinatorial fusion analysis (CFA) is an approach for combining multiple scoring systems using the rank-score characteristic function and cognitive diversity measure. One example is to combine diverse machine learning models to achieve better prediction quality. In this work, we apply CFA to the synthesis of metal halide perovskites containing organic ammonium cations via inverse temperature crystallization. Using a data set generated by high-throughput experimentation, four individual models (support vector machines, random forests, weighted logistic classifier, and gradient boosted trees) were developed. We characterize each of these scoring systems and explore 66 possible combinations of the models. When measured by the precision on predicting crystal formation, the majority of the combination models improves the individual model results. The best combination models outperform the best individual models by 3.9 percentage points in precision. In addition to improving prediction quality, we demonstrate how the fusion models can be used to identify mislabeled input data and address issues of data quality. In particular, we identify example cases where all single models and all fusion models do not give the correct prediction. Experimental replication of these syntheses reveals that these compositions are sensitive to modest temperature variations across the different locations of the heating element that can hinder or enhance the crystallization process. In summary, we demonstrate that model fusion using CFA can not only identify a previously unconsidered influence on reaction outcome but also be used as a form of quality control for high-throughput experimentation.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2022

Engineered disorder in CO 2 photocatalysis

Light harvesting, separation of charge carriers, and surface reactions are three fundamental steps that are essential for an efficient photocatalyst. Here we show that these steps in the TiO 2 can be boosted simultaneously by disorder engineering. A solid-state reduction reaction between sodium and TiO 2 forms a core-shell c-TiO 2 @a-TiO 2-x (OH) y heterostructure, comprised of HO-Ti-[O]-Ti surface frustrated Lewis pairs (SFLPs) embedded in an amorphous shell surrounding a crystalline core, which enables a new genre of chemical reactivity. Specifically, these SFLPs heterolytically dissociate dihydrogen at room temperature to form charge-balancing protonated hydroxyl groups and hydrides at unsaturated titanium surface sites, which display high reactivity towards CO 2 reduction. This crystalline-amorphous heterostructure also boosts light absorption, charge carrier separation and transfer to SFLPs, while prolonged carrier lifetimes and photothermal heat generation further enhance reactivity. The collective results of this study motivate a general approach for catalytically generating sustainable chemicals and fuels through engineered disorder in heterogeneous CO 2 photocatalysts.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2026

Electric accumulation of millicharged particles

A terrestrial population of millicharged particles that interact significantly with normal matter can arise if they make up a dark matter subcomponent or if they are light enough to be produced in cosmic-ray air showers. Such particles thermalize to terrestrial temperatures through repeated scatters with normal matter in Earth’s environment. We show that a simple electrified shell (for example, a Van de Graaff generator) functions as an efficient accumulator of such room-temperature millicharged particles, parametrically enhancing their local density by as much as 12 orders of magnitude. This can be used to boost the sensitivity of any detector housed in the shell’s interior, such as ion traps and tests of Coulomb’s law. In a companion paper, we apply this specifically to Cavendish tests of Coulomb’s law, and we show that a well-established setup can probe a large region of unexplored parameter space, with sensitivity to the irreducible density of millicharged particles generated from cosmic rays that outperforms future accelerator searches for sub-GeV masses.

Berlin, Asher [Fermilab] (ORCID:0000000211561482)↗
DOE OSTI2023

CMOS-Compatible Ultrathin Superconducting NbN Thin Films Deposited by Reactive Ion Sputtering on 300 mm Si Wafer

We report a milestone in achieving large-scale, ultrathin (~5 nm) superconducting NbN thin films on 300 mm Si wafers using a high-volume manufacturing (HVM) industrial physical vapor deposition (PVD) system. The NbN thin films possess remarkable structural uniformity and consistently high superconducting quality across the entire 300 mm Si wafer, by incorporating an AlN buffer layer. High-resolution X-ray diffraction and transmission electron microscopy analyses unveiled enhanced crystallinity of (111)-oriented δ-phase NbN with the AlN buffer layer. Notably, NbN films deposited on AlN-buffered Si substrates exhibited a significantly elevated superconducting critical temperature (~2 K higher for the 10 nm NbN) and a higher upper critical magnetic field or H c2 (34.06 T boost in H c2 for the 50 nm NbN) in comparison with those without AlN. These findings present a promising pathway for the integration of quantum-grade superconducting NbN films with the existing 300 mm CMOS Si platform for quantum information applications.

36 MATERIALS SCIENCE↗
NASA NTRS1984

Molten Slag Would Boost Coal Conversion

Reactor increases residence time of uncovered char. Near-100percent carbon conversion achievable in reactor incorporating moltenslag bath. Slag maintains unconverted carbon impinging on surface at high temperatures for longer period of time, enhancing conversion.

Ferrall, J. F.↗
DOE OSTI2024

Recent increases in annual, seasonal, and extreme methane fluxes driven by changes in climate and vegetation in boreal and temperate wetland ecosystems

Climate warming is expected to increase global methane (CH 4 ) emissions from wetland ecosystems. Although in situ eddy covariance (EC) measurements at ecosystem scales can potentially detect CH 4 flux changes, most EC systems have only a few years of data collected, so temporal trends in CH 4 remain uncertain. Here, we use established drivers to hindcast changes in CH 4 fluxes (FCH 4 ) since the early 1980s. We trained a machine learning (ML) model on CH 4 flux measurements from 22 [methane-producing sites] in wetland, upland, and lake sites of the FLUXNET-CH 4 database with at least two full years of measurements across temperate and boreal biomes. The gradient boosting decision tree ML model then hindcasted daily FCH 4 over 1981-2018 using meteorological reanalysis data. We found that, mainly driven by rising temperature, half of the sites (n = 11) showed significant increases in annual, seasonal, and extreme FCH 4 , with increases in FCH 4 of ca. 10% or higher found in the fall from 1981–1989 to 2010–2018. The annual trends were driven by increases during summer and fall, particularly at high-CH 4 -emitting fen sites dominated by aerenchymatous plants. We also found that the distribution of days of extremely high FCH 4 (defined according to the 95th percentile of the daily FCH 4 values over a reference period) have become more frequent during the last four decades and currently account for 10–40% of the total seasonal fluxes. The share of extreme FCH 4 days in the total seasonal fluxes was greatest in winter for boreal/taiga sites and in spring for temperate sites, which highlights the increasing importance of the non-growing seasons in annual budgets. Our results shed light on the effects of climate warming on wetlands, which appears to be extending the CH 4 emission seasons and boosting extreme emissions.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

A Predictive Prescription Framework for Stochastic Unit Commitment Using Boosting Ensemble Learning Algorithms

To take unit commitment (UC) decisions under uncertain load, most existing stochastic optimization (SO) frameworks adopt a generic representation of uncertainty. While load levels that materialize on a particular day are influenced by various covariates (such as the day of the week or temperature), SO frameworks typically disregard such side observations, wasting actionable information that could significantly enhance decision quality. Here, this article proposes a contextual SO (CSO) framework for UC under uncertain load, which can effectively exploit covariate observations in conjunction with a class of machine learning (ML) algorithms to improve the out-of-sample performance of UC decisions. It shows how three ML algorithms, adaptive boosting, gradient boosted trees, and extreme gradient boosting, can be used to this end, constituting the first application of these algorithms in any CSO framework. Using real-world data harvested from the New York ISO grid, we measure the out-of-sample performance of the framework in terms of total operation cost, shed load values, locational marginal prices, and total payments by the loads, against several benchmark methods proposed in the literature. The article has an online companion (Yurdakul et al.), wherein we present additional results and lay out further mathematical formulations used in this work.

42 ENGINEERING↗
DOE OSTI2020

Unveiling the critical role of interfacial ionic conductivity in all-solid-state lithium batteries

Advancement of all-solid-state lithium-ion (Li + ) batteries (ASSLIBs) has been hindered by the large interfacial resistance mainly originating from interfacial reactions between oxide cathodes and solid-state sulfide electrolytes (SEs). To suppress the interfacial reactions, an interfacial coating layer between cathodes and SEs is indispensable. However, the kinetics of interfacial Li + transport across the coating layer has not been well understood yet. Herein, we tune the interfacial ionic conductivity of the coating layer LiNb 0.5 Ta 0.5 O 3 (LNTO) by manipulating post-annealing temperature. It is found that the interfacial ionic conductivity determines interfacial Li + transport kinetics and enhancing the interfacial ionic conductivity can significantly boost the electrochemical performance of SE-based ASSLIBs. A representative cathode LiNi 0.5 Mn 0.3 Co 0.2 O 2 coated by LNTO with the highest interfacial ionic conductivity exhibits a high initial capacity of 152 mAh.g -1 at 0.1 C and 107.5 mAh.g -1 at 1C. This work highlights the importance of increasing interfacial ionic conductivity for high-performance SE-based ASSLIBs.

25 ENERGY STORAGE↗
DOE OSTI2020

W-band system-on-chip electron cyclotron emission imaging system on DIII-D

Monolithic, millimeter wave “system-on-chip” (SoC) technology has been employed in heterodyne receiver integrated circuit radiometers in a newly developed Electron Cyclotron Emission Imaging (ECEI) system on the DIII-D tokamak for 2D electron temperature profile and fluctuation evolution diagnostics. Here, a prototype module operating in E-band (72-80 GHz) was first employed in a 2 x 10 element array that demonstrated significant improvements over the previous quasi-optical Schottky diode mixer arrays during the 2018 operational campaign of the DIII-D tokamak. For compatibility with International Thermonuclear Experimental Reactor (ITER) relevant scenarios on DIII-D, the SoC ECEI system was upgraded with 20 horn-waveguide receiver modules. Each individual module contains a University of California at Davis designed W-band (75 -110 GHz) receiver die that integrates a broadband low noise amplifier (LNA), a double balanced down-converting mixer, and a x4 multiplier on the local oscillator (LO) chain. A x2 multiplier and two IF amplifiers are packaged and selected to further boost the signal strength as well as downconvert the signal frequency. The upgraded W-band array exhibits > 30 dB additional gain and 20x improvement in noise temperature comparing with the previous Schottky diode radio frequency (RF) mixer input systems; an internal 8 times multiplier chain is used to bring down the LO frequency below 12 GHz, thereby obviating the need for a large aperture for quasi-optical LO coupling and replacing it with coaxial connectors. The horn-waveguide shielding housing avoids out-of-band noise interference on each individual module. The upgraded ECEI system plays an important role for absolute electron temperature evolution and fluctuation measurements for edge and core region transport physics studies.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2024

A Methodology for Simulating Supercritical CO2 Heat Transfer Experiments Using Machine Learning Models

To support the growth of supercritical carbon dioxide (sCO2) power cycles in the energy industry, this study seeks to train a machine learning model to mirror experimental data to predict new heat transfer data. To do this experimental data was amassed, one preliminary set comprised of 16 test results, and an expanded version comprised of 38 test results. With the goal of predicting experimental apparatus temperatures and pressures, several iterations of models were tested investigating the impact of model hyper-parameters, data inclusion, and data pre-processing on model performance. A total of 15 variations cumulatively of Gaussian Process Regressors, Gradient Boosting Regressors, and Multi-Layer Perceptrons were trained and validated on the preliminary set, and the best algorithm of each class was re-trained on the expanded set. These were compared based on test/train R^2 , test/train mean absolute error (MAE), and validation MAE, to identify the successfulness of these models. It was shown temperatures could be predicted within just a few degrees, showing the potential of this approach. Future research has been identified with approaches to improve pressure and temperature predictions going forward.

Grabowski, Owen↗
DOE OSTI2026

Enhancing mobility and interface state engineering via UV-ozone treatment in BEOL-compatible ultrathin TiO 2 transistors

It has emerged as a potential candidate to improve the performance of monolithic-three-dimensional (M3D) integration of fused logic and memories through low-temperature in situ synthesis of high-performance metal–oxide–semiconductor (MOS) transistors. Here, we report the demonstration of the BEOL-compatible low thermal budget (350 °C) fabrication process of ultrathin-TiO2 transistors by the combination of RTA and UV-ozone treatment (RTA-UVOz). UV–ozone (UVOz) treatment of TiO2 anatase films significantly enhances stability, boosting ION current and field effect mobility (μ FE ) by two times of magnitude in TiO2 TFTs. UV ozone treatment helps to eliminate pre-existing oxygen vacancies and carbon contamination on TiO2 channels even at low temperature (100 °C), resulting in high-quality channel/dielectric interfaces with low interface states (D it ). During UVOz treatment, oxygen species (O x ) passivate the oxygen vacancies ($V$$^{2+}_{o}$), and hence low concentration of $V$$^{2+}_{o}$ would be left for ionization/de-ionization under PBS/NBS, leading to improved bias stress stability. Furthermore, the TiO 2 TFTs with thin ZrO 2 gate dielectric exhibited excellent performance including a lower subthreshold swing (SS) of 98 mV/dec with high drive I ON current ∼ 4.5 μA/μm, a high I ON /I OFF > 10 9 , and mobility μFE of 7 cm 2 /V-s under a battery powered voltage of 1 V. UV–ozone treatment enables high-performance, CMOS-compatible TiO 2 transistors with a low thermal budget, ideal for next-generation flexible, energy-efficient electronics.

BEOL↗
DOE OSTI2021

Machine learning assisted prediction of the Young’s modulus of compositionally complex alloys

We identify compositionally complex alloys (CCAs) that offer exceptional mechanical properties for elevated temperature applications by employing machine learning (ML) in conjunction with rapid synthesis and testing of alloys for validation to accelerate alloy design. The advantages of this approach are scalability, rapidity, and reasonably accurate predictions. ML tools were implemented to predict Young’s modulus of refractory-based CCAs by employing different ML models. Our results, in conjunction with experimental validation, suggest that average valence electron concentration, the difference in atomic radius, a geometrical parameter λ and melting temperature of the alloys are the key features that determine the Young’s modulus of CCAs and refractory-based CCAs. The Gradient Boosting model provided the best predictive capabilities (mean absolute error of 6.15 GPa) among the models studied. Our approach integrates high-quality validation data from experiments, literature data for training machine-learning models, and feature selection based on physical insights. It opens a new avenue to optimize the desired materials property for different engineering applications.

36 MATERIALS SCIENCE↗
NASA NTRS1971

Space shuttle low pressure auxiliary propulsion subsystem design definition: Design handbook

A detailed description of recommended low pressure auxiliary propulsion subsystems (APS) for a space shuttle orbiter and booster is presented. The APS designs are the product of a study to identify and evaluate APS concepts, and to perform in-depth design and performance analyses for the most attractive of these. Selected APS baselines use the main engine propellant tanks as low pressure gas accumulators. For the orbiter, propellants from separate liquid tanks are used for main engine tank resupply. Resupply propellants are first circulated through tubular, passive heat exchangers, where they are vaporized and superheated prior to injection into the main engine tanks. Warm propellant vapors from the main engine tanks are mixed with additional liquid propellants in a downstream liquid/vapor mixer and supplied to the engines at constant temperature and pressure (constant density). The booster APS requires no separate propellant storage, since propellant residuals, trapped in the main engine tanks following boost, are sufficient to meet APS propellant demands.

Bruns, A. E.↗