Experimental investigations on post-dryout heat transfer in 10×8.1, 10×7 and 10×6mm annular test sections have been carried out under low-pressure and low mass flow rate conditions. An Artificial Neural Network (ANN) was trained successfully based on the experimental data for predicting the average post-dryout Nusselt number. Based on the ANN, the effects of gap size, pressure, steam Reynolds number, Reg, inlet quality, xi, Prandtl number, (Prg)W, and the ratio of heat flux of inner-tube to that of outer-tube, qi/qo, on post-dryout heat transfer were analyzed, respectively. In present study, Nusselt number in annular channels with big gap size is larger than that in annular channels with small gap size. Nusselt number increases significantly in 1.5mm and 2.0mm annular channels while it is almost constant in 0.95mm annular channel with increasing pressure or qi/qo. Nusselt number increases with Reg in case of 0.95mm and 1.5mm gap sizes. However, Nusselt number in 2.0mm annular channel firstly increases and then decreases with increasing Reg. Nusselt number decreases with increasing inlet quality under all three annular channels condition. Nusselt number decreases significantly with increasing (Prg)W when (Prg)W is less than 1.5. The changes of Nusselt number in 1.5mm or 2.0mm annular channels are larger than that in 0.95mm annular channel.
- Nuclear Engineering Division
Prediction of Post-Dryout Heat Transfer in Vertical Annular Channels Using Artificial Neural Network Method
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Zhao, DW, Su, GH, Qiu, SZ, & Tian, WX. "Prediction of Post-Dryout Heat Transfer in Vertical Annular Channels Using Artificial Neural Network Method." Proceedings of the 16th International Conference on Nuclear Engineering. Volume 2: Fuel Cycle and High Level Waste Management; Computational Fluid Dynamics, Neutronics Methods and Coupled Codes; Student Paper Competition. Orlando, Florida, USA. May 11–15, 2008. pp. 705-711. ASME. https://doi.org/10.1115/ICONE16-48258
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