Liquid metal “divertorlets” concept for fusion reactors
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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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Generation of electric power by liquid sodium-jet MHD generator
Thermionic diode pressure sensor for liquid, vapor, and two phase potassium flow measurements at high temperatures
The surface composition of liquid gallium-tin alloys is studied in an Auger electron spectrometer as a function of bulk composition and temperature. The sessile drop samples are cleaned by argon ion bombardment sputtering of the liquid. This technique produces surfaces that are entirely free of impurities within the sensitivity of the spectrometer and remain so for many days. Tin is found to be strongly adsorbed at the liquid-vacuum interface. Surface concentrations based on Auger measurements are found to be in reasonably good agreement with values calculated from surface tension measurements interpreted in terms of a monolayer depth distribution model for the adsorbed tin.
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Construction and performance tests of induction type liquid potassium magnetohydrodynamic generator
Cavitation erosion in high purity liquid potassium at high temperature
Heat transfer measurements in flow system pipes containing sodium, sodium potassium alloy, lithium, or mercury for reactor cooling
Advanced high-temperature fluid reactors (ARs), such as sodium fast reactors (SFRs) and molten salt cooled reactors (MSCRs) utilize high-temperature fluids at ambient pressure. To melt the fluid during reactor startup and prevent fluid freezing during cooldown, the thermal–hydraulic systems of such ARs include heater zones consisting of specific heaters with controllers, temperature sensors, and thermal insulation. The failure of heater zones due to insulation material degradation or improper installation, resulting in parasitic heat losses, can lead to fluid freezing. The detection of faults using a heat-transfer model is difficult because of a lack of knowledge of the experimental details. Data-driven machine learning of heater zone temperature time series offers a viable alternative. In this study, we benchmarked the performance of recurrent neural networks (RNNs) in an analysis of heat-up transient temperature time series of heater zones installed on a liquid sodium vessel. The RNN models include long short-term memory (LSTM) and gated recurrent unit (GRU) networks, as well as their bi-directional variants, BiLSTM and BiGRU. Anomalous temperature points were designated using a percentile-based threshold applied to residual fluctuations in the detrended temperature time series. Additionally, the impact of the exponentially weighted moving average (EWMA) method on detection accuracy was examined. The RNN models’ performance was assessed using precision, recall, and F 1 score metrics. Results demonstrated that RNN models effectively detect anomalies in temperature time series with the best models for each heater zone achieving F 1 scores of over 93%. To explain the variations in RNN model performance across different heater zones, we used Kullback–Leibler (KL) divergence to quantify the relative entropy between training and testing data, and the Detrended Fluctuation Analysis (DFA) to assess long-range temporal correlations. For datasets with strong long-range correlations and minimal relative entropy between training and testing data, GRU is the best-performing model. When the data exhibits weaker long-term correlations and a significant relative entropy between training and testing distributions, BiGRU shows the best performance. For the data sets with intermediate values of both KL divergence and DFA, the best performance is obtained with LSTM and BiLSTM, respectively.
The developments in wire-wrapped fuel bundle thermal-hydraulics research have become increasingly diverse and this makes comparisons across studies difficult. This paper describes an ongoing benchmark that will address this issue by directly comparing computational results with experimental data from two unique wire-wrapped thermal-hydraulic facilities. Preliminary subchannel sensitivity / uncertainty analyses show largely expected behavior for the presented cases, where rod diameter and friction factor show the greatest sensitivities to geometrical perturbations. Guidelines for computational fluid dynamics evaluations have been prepared and highlight the need for consolidated modeling strategies. (authors)
Thermodynamic properties of liquid cesium-mercury solutions
Cavitation damage resistance of Fe, Ni and Co alloys in liquid sodium and mercury
The first liquid gallium–CO 2 battery achieves unprecedented power density and carbon negative effect without precious metal catalysts.
Annular two-phase flow inside circular tubes for estimating liquid film thickness and calculating heat transfer coefficient of pure fluid
High-temperature liquid potassium cavitation damage tests
Cavitation damage resistance of iron alloys, nickel alloys, and cobalt alloys in liquid sodium and mercury - review of NASA program