Analysis of the critical heat flux in round vertical tubes under low pressure and flow oscillation conditions:Applications of artificial neural network
| Autor: | Jaakko Miettinen, Kenji Fukuda, Koji Morita, Su Guanghui, Jia Dounan, Mark Pidduck |
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| Jazyk: | angličtina |
| Rok vydání: | 2003 |
| Předmět: |
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 |
| Zdroj: | Guanghui, S, Morita, K, Fukuda, K, Pidduck, M, Dounan, J & Miettinen, J 2003, ' Analysis of the critical heat flux in round vertical tubes under low pressure and flow oscillation conditions : Applications of artificial neural network ', Nuclear Engineering and Design, vol. 220, no. 1, pp. 17-35 . https://doi.org/10.1016/S0029-5493(02)00304-7 |
| DOI: | 10.1016/S0029-5493(02)00304-7 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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