scholarly journals Application of the machine learning technique for the development of a condensation heat transfer model for a passive containment cooling system

Dong Hyun Lee ◽  
Jee Min Yoo ◽  
Hui Yung Kim ◽  
Dong Jin Hong ◽  
Byong Jo Yun ◽  
2006 ◽  
Vol 128 (10) ◽  
pp. 1050-1059 ◽  
Todd M. Bandhauer ◽  
Akhil Agarwal ◽  
Srinivas Garimella

A model for predicting heat transfer during condensation of refrigerant R134a in horizontal microchannels is presented. The thermal amplification technique is used to measure condensation heat transfer coefficients accurately over small increments of refrigerant quality across the vapor-liquid dome (0<x<1). A combination of a high flow rate closed loop primary coolant and a low flow rate open loop secondary coolant ensures the accurate measurement of the small heat duties in these microchannels and the deduction of condensation heat transfer coefficients from measured UA values. Measurements were conducted for three circular microchannels (0.506<Dh<1.524mm) over the mass flux range 150<G<750kg∕m2s. Results from previous work by the authors on condensation flow mechanisms in microchannel geometries were used to interpret the results based on the applicable flow regimes. The heat transfer model is based on the approach originally developed by Traviss, D. P., Rohsenow, W. M., and Baron, A. B., 1973, “Forced-Convection Condensation Inside Tubes: A Heat Transfer Equation For Condenser Design,” ASHRAE Trans., 79(1), pp. 157–165 and Moser, K. W., Webb, R. L., and Na, B., 1998, “A New Equivalent Reynolds Number Model for Condensation in Smooth Tubes,” ASME, J. Heat Transfer, 120(2), pp. 410–417. The multiple-flow-regime model of Garimella, S., Agarwal, A., and Killion, J. D., 2005, “Condensation Pressure Drop in Circular Microchannels,” Heat Transfer Eng., 26(3), pp. 1–8 for predicting condensation pressure drops in microchannels is used to predict the pertinent interfacial shear stresses required in this heat transfer model. The resulting heat transfer model predicts 86% of the data within ±20%.

2014 ◽  
Vol 926-930 ◽  
pp. 802-805
Jun Li Jia ◽  
Jin Hong Zhang ◽  
Guo Zhen Wang

Efficient secondary cooling water control level slab continuous casting process and quality are closely related. Casting solidification heat transfer model is the basis of process control and optimization, heat transfer model based on determining the secondary cooling system is the most widely used method for casting production process can be simulated. However, when considering the many factors affecting the production and input conditions change significantly, real-time and strain of this method is not guaranteed. Therefore, the artificial intelligence optimization algorithms such as genetic algorithms, neural networks, fuzzy controllers, introducing continuous casting secondary cooling water distribution and dynamics of optimal control methods, the rational allocation of caster secondary cooling water and dynamic control is important.

2013 ◽  
Vol 45 (6) ◽  
pp. 759-766 ◽  

Masaya Kondo ◽  
Hideo Nakamura ◽  
Yoshinari Anoda ◽  
Sadanori Saishu ◽  
Hiroyuki Obata ◽  

A horizontal in-tube condensation heat exchanger is under investigation to be used for a passive containment cooling system (PCCS) of a next generation-type BWR. The flow conditions in the horizontal condenser tube were observed both visually and by local void fraction fluctuation. The observed flow regimes at a rated condition were annular flow at the tube inlet, and turned gradually into wavy flow and smooth stratified flow along the length of the tube. It was found further that frequency of the roll waves that appear on the liquid film in the annular flow is closely related to the measured local condensation heat transfer coefficient. Based on the flow observation, the roll wave frequency and measured condensation heat transfer coefficient, a model is proposed which predicts the condensation heat transfer coefficient particularly for annular flows around the tube inlet region. The proposed heat transfer model predicts well the influences of pressure, local gas-phase velocity and film thickness.

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