摘要 :
Artificial neural networks (ANNs) for predicting critical heat flux (CHF) under low pressure and oscillation conditions have been trained successfully for either natural circulation or forced circulation (FC) in the present study....
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Artificial neural networks (ANNs) for predicting critical heat flux (CHF) under low pressure and oscillation conditions have been trained successfully for either natural circulation or forced circulation (FC) in the present study. The input parameters of the ANN are pressure, mean mass flow rate, relative amplitude, inlet subcooling, oscillation period and the ratio of the heated length to the diameter of the tube, L/D. The output is a nondimensionalized factor F, which expresses the relative CHF under oscillation conditions. Based on the trained ANN, the influences of principal parameters on F for FC were analyzed. The parametric trends of the CHF under oscillation obtained by the trained ANN are as follows: the effects of pressure below 500 kPa are complex due to the influence of other parameters. F will increase with increasing mean mass flow rate under any conditions, and will decrease generally with an increase in relative amplitude. F will decrease initially and then increase with increasing inlet subcooling. The influence curves of mean mass flow rate on F will be almost the same when the period is shorter than 5.0 s or longer than 15 s. The influence of L/D will be negligible if L/D > 200. It is found that the minimum number of neurons in the hidden layer is a product of the number of neurons in the input layer and in the output layer.
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摘要 :
This paper describes section topography using an X-ray microbeam and a novel slit having a V-shaped crevice (V-slit). The V-slit is characterized by a sharp-pointed exponential transmission curve, which enables depth-resolved imag...
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This paper describes section topography using an X-ray microbeam and a novel slit having a V-shaped crevice (V-slit). The V-slit is characterized by a sharp-pointed exponential transmission curve, which enables depth-resolved imaging with high spatial resolution. An iterative deconvolution for image restoration is effectively executable, providing submicron resolution in cross-sectional diffraction imaging. The new method is applied to the analysis of screw dislocation in a SiC diode.
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摘要 :
The microscopic behavior of nitrogen atoms in the SiO2-SiC interface regions of n-channel lateral 4H-SiC metal-oxide-semiconductor field effect transistors (MOSFETs) was studied using low-temperature electrically detected magnetic...
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The microscopic behavior of nitrogen atoms in the SiO2-SiC interface regions of n-channel lateral 4H-SiC metal-oxide-semiconductor field effect transistors (MOSFETs) was studied using low-temperature electrically detected magnetic resonance spectroscopy and other techniques. The results show that nitrogen atoms eliminated shallow interface states observable at 20 K and further diffused into the channel region of the MOSFETs as shallow donors. These two behaviors enable nitrogen atoms to change the channel conductivity of SiC MOSFETs.
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