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9. Analysis of the critical heat flux in round vertical tubes under low pressure and flow oscillation conditions:Applications of artificial neural network

Author

Jaakko Miettinen, Kenji Fukuda, Koji Morita, Su Guanghui, Jia Dounan, and Mark Pidduck

Subjects
Nuclear and High Energy Physics, Materials science, Thermodynamics, critical heat flux, law.invention, Physics::Fluid Dynamics, law, Mass flow rate, General Materials Science, Safety, Risk, Reliability and Quality, Waste Management and Disposal, Parametric statistics, turbulent flow, boiling water reactors, geography, geography.geographical_feature_category, Oscillation, Critical heat flux, Mechanical Engineering, Pressurized water reactor, Mechanics, Inlet, neural networks, Subcooling, Natural circulation, Nuclear Energy and Engineering, mass flow, nuclear reactors, artificial neural networks
Abstract

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.

Published
2003

10. Estimation of thermodynamic properties of the ternary molten salt system, LiF-NaF-BeF2, by the modified Peng-Robinson equation.

Author

Zhang, Dalin, Qiu, Suizheng, Su, Guanghui, and Jia, Dounan

Abstract

The molten salt reactor (MSR), which is one of the generation IV reactors, can meet the demand of transmutation and breeding. The thermodynamic properties of the molten salt system like LiF-NaF-BeF2 influence the design and construction of the fuel salt and coolant in the MSR for the new generation. In this paper, the equation of state of the ternary system 15%LiF-58%NaF-27%BeF2, over the temperature range from 873.15 to 1 073.15 K at one atmosphere pressure, is described using a modified Peng-Robinson (PR) equation. The densities of the ternary system and its components are estimated by this equation directly, and compared with the experimental data. Based on the equation of state, the other thermodynamic properties such as the enthalpy, entropy and heat capacity at constant pressure are estimated by the residual function method and the fugacity coefficient method respectively. The densities calculated by PR equation are highly in agreement with the experimental data, and the enthalpy, entropy and heat capacity evaluated by the two different methods are consistent with each other. It can be concluded that the modified PR equation can be applied to evaluate the density of the molten salt system, and it is recommended that it be used as the basis to estimate the enthalpy, entropy and heat capacity of the molten salt system. [ABSTRACT FROM AUTHOR]

Published
2007
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