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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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Presentation for USNCCM on work on non-intrusive reduced order models for aerothermal modeling.
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Abstract not provided.
The chemical kinetics of diisobutylene, a proposed biofuel, has been studied behind reflected shock waves near 10 atm over a temperature range of 1186 – 1414 K. A stoichiometric mixture consisting of 0.15% diisobutylene was used to investigate ignition behavior and speciation during oxidation experiments performed in a shock tube. Time resolved measurements of carbon monoxide were measured behind reflected shock waves with a continuous wave quantum cascade laser centered at a frequency of 2046.20 cm^-1 using a fixed-wavelength spectroscopy scheme.
Abstract not provided.
From the lightest Hydrogen isotopes up to the recently synthesized Oganesson (Z = 118), it is estimated that as many as about 8,000 atomic nuclei could exist in nature. Most of these nuclei are too short-lived to be occurring on Earth, but they play an essential role in astrophysical events such as supernova explosions or neutron star mergers that are presumed to be at the origin of most heavy elements in the Universe. Understanding the structure, reactions, and decays of nuclei across the entire chart of nuclides is an enormous challenge because of the experimental difficulties in measuring properties of interest in such fleeting objects and the theoretical and computational issues of simulating strongly-interacting quantum many-body systems. Nuclear density functional theory (DFT) is a fully microscopic theoretical framework which has the potential of providing such a quantitatively accurate description of nuclear properties for every nucleus in the chart of nuclides. Thanks to high-performance computing facilities, it has already been successfully applied to predict nuclear masses, global patterns of radioactive decay like β or γ decay, and several aspects of the nuclear fission process such as, e.g., spontaneous fission half-lives. Yet, predictive simulations of nuclear spectroscopy—the low-lying excited states and transitions between them—or of nuclear fission, or the quantification of theoretical uncertainties and their propagation to basic or applied nuclear science applications, would require several orders of magnitude more calculations than currently possible. However, most of this computational effort would be spent into generating a suitable basis of DFT wavefunctions. Such a task could potentially be considerably accelerated by borrowing tools from the field of machine learning and artificial intelligence. In this paper, we review different approaches to applying supervised and unsupervised learning techniques to nuclear DFT.
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A new heterogeneous ice nucleation parameterization that covers a wide temperature range (-36 °C to -78 °C) is presented. Developing and testing such an ice nucleation parameterization, which is constrained through identical experimental conditions, is critical in order to accurately simulate the ice nucleation processes in cirrus clouds. The surface-scaled ice nucleation efficiencies of hematite particles, inferred by n s , were derived from AIDA (Aerosol Interaction and Dynamics in the Atmosphere) cloud chamber measurements under water subsaturated conditions that were realized by continuously changing temperature (T) and relative humidity with respect to ice (RH ice ) in the chamber. Our measurements showed several different pathways to nucleate ice depending on T and RH ice conditions. For instance, almost independent freezing was observed at -60 °C < T < -50 °C, where RH ice explicitly controlled ice nucleation efficiency, while both T and RH ice played roles in other two T regimes: -78 °C < T < -60 °C and -50 °C < T < -36 °C. More specifically, observations at T colder than -60 °C revealed that higher RHice was necessary to maintain constant n s , whereas T may have played a significant role in ice nucleation at T warmer than -50 °C. We implemented new n s parameterizations into two cloud models to investigate its sensitivity and compare with the existing ice nucleation schemes towards simulating cirrus cloud properties. Our results show that the new AIDA-based parameterizations lead to an order of magnitude higher ice crystal concentrations and inhibition of homogeneous nucleation in colder temperature regions. Our cloud simulation results suggest that atmospheric dust particles that form ice nuclei at lower temperatures, below -36 °C, can potentially have stronger influence on cloud properties such as cloud longevity and initiation when compared to previous parameterizations.
The goal of this work is to generate large statistically representative datasets to train machine learning models for disruption prediction provided by data from few existing discharges. Such a comprehensive training database is important to achieve satisfying and reliable prediction results in artificial neural network classifiers. Here, we aim for a robust augmentation of the training database for multivariate time series data using Student-t process regression. We apply Student-t process regression in a state space formulation via Bayesian filtering to tackle challenges imposed by outliers and noise in the training data set and to reduce the computational complexity. Thus, the method can also be used if the time resolution is high. We use an uncorrelated model for each dimension and impose correlations afterwards via coloring transformations. We demonstrate the efficacy of our approach on plasma diagnostics data of three different disruption classes from the DIII-D tokamak. To evaluate if the distribution of the generated data is similar to the training data, we additionally perform statistical analyses using methods from time series analysis, descriptive statistics, and classic machine learning clustering algorithms.
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Samples of 1% Pt/SnO2 catalyst were exposed to a stoichiometric gas mixture of 1% CO and 1.2% O2 in helium over a range of flowrates from 5 to 15 sccm and temperatures from 338 to 394 Kelvin. Reaction rate constants for the catalytic oxidation of carbon monoxide and their temperature dependence were determined and compared with previous literature values.
Larger crystals of higher quality grown. Alternative method for starting growth of crystal involves use of seed crystal of different material instead of same material as solution. Intended for growing single-crystal proteins for experiments but applicable in general to growth of crystals from solutions and to growth of semiconductor or other crystals from melts.
Empirical relationships between variables are ways of securing estimates of quantities difficult to measure by remote sensing methods. The use of empirical functions was explored between: (1) atmospheric column moisture abundance W (gm H2O/cm(sup 2) and surface absolute water vapor density rho(q-bar) (gm H2O/cm(sup 3), with rho density of moist air (gm/cm(sup 3), q-bar specific humidity (gm H2O/gm moist air), and (2) column abundance and surface moisture flux E (gm H2O/(cm(sup 2)sec)) to infer regional evapotranspiration from Airborne Visible/Infrared Imaging Spectrometers (AVIRIS) water vapor mapping data. AVIRIS provides, via analysis of atmospheric water absorption features, estimates of column moisture abundance at very high mapping rate (at approximately 100 km(sup 2)/40 sec) over large areas at 20 m ground resolution.
Reduction of noise generated by turbulent flow past the trailing-edge of a lifting surface is a challenge in many aeronautical and naval applications. Numerical predictions of trailing-edge noise necessitate the use of advanced simulation techniques such as large-eddy simulation (LES) in order to capture a wide range of turbulence scales which are the source of broadband noise. Aeroacoustic calculations of the flow over a model airfoil trailing edge using LES and aeroacoustic theory have been presented in Wang and Moin and were shown to agree favorably with experiments. The goal of the present work is to apply shape optimization to the trailing edge flow previously studied, in order to control aerodynamic noise.
OBJECTIVE: The goal of this study was to analyze left ventricular outflow tract systolic acceleration (LVOT(Acc)) during alterations in left ventricular (LV) contractility and LV filling. BACKGROUND: Most indexes described to quantify LV systolic function, such as LV ejection fraction and cardiac output, are dependent on loading conditions. METHODS: In 18 sheep (4 normal, 6 with aortic regurgitation, and 8 with old myocardial infarction), blood flow velocities through the LVOT were recorded using conventional pulsed Doppler. The LVOT(Acc) was calculated as the aortic peak velocity divided by the time to peak flow; LVOT(Acc) was compared with LV maximal elastance (E(m)) acquired by conductance catheter under different loading conditions, including volume and pressure overload during an acute coronary occlusion (n = 10). In addition, a clinically validated lumped-parameter numerical model of the cardiovascular system was used to support our findings. RESULTS: Left ventricular E(m) and LVOT(Acc) decreased during ischemia (1.67 +/- 0.67 mm Hg.ml(-1) before vs. 0.93 +/- 0.41 mm Hg.ml(-1) during acute coronary occlusion [p < 0.05] and 7.9 +/- 3.1 m.s(-2) before vs. 4.4 +/- 1.0 m.s(-2) during coronary occlusion [p < 0.05], respectively). Left ventricular outflow tract systolic acceleration showed a strong linear correlation with LV E(m) (y = 3.84x + 1.87, r = 0.85, p < 0.001). Similar findings were obtained with the numerical modeling, which demonstrated a strong correlation between predicted and actual LV E(m) (predicted = 0.98 [actual] -0.01, r = 0.86). By analysis of variance, there was no statistically significant difference in LVOT(Acc) under different loading conditions. CONCLUSIONS: For a variety of hemodynamic conditions, LVOT(Acc) was linearly related to the LV contractility index LV E(m) and was independent of loading conditions. These findings were consistent with numerical modeling. Thus, this Doppler index may serve as a good noninvasive index of LV contractility.