کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
6760551 | 511700 | 2015 | 11 صفحه PDF | دانلود رایگان |
عنوان انگلیسی مقاله ISI
A porous medium model for predicting the duct wall temperature of sodium fast reactor fuel assembly
ترجمه فارسی عنوان
مدل محیطی متخلخل برای پیش بینی دمای دیواره مجرای سوخت راکتور سوخت سدیم
دانلود مقاله + سفارش ترجمه
دانلود مقاله ISI انگلیسی
رایگان برای ایرانیان
موضوعات مرتبط
مهندسی و علوم پایه
مهندسی انرژی
مهندسی انرژی و فناوری های برق
چکیده انگلیسی
Porous medium models have been established for predicting duct wall temperature of sodium fast reactor rod bundle assembly, which is much less computationally expensive than conventional CFD simulations that explicitly represent the wire-wrap and fuel pin geometry. Three porous medium models are proposed in this paper. Porous medium model 1 takes the whole assembly as one porous medium of uniform characteristics in the conventional approach. Porous medium model 2 distinguishes the pins along the assembly's edge from those in the interior with two distinct regions, each with a distinct porosity, resistance, and volumetric heat source. This accounts for the different fuel-to-coolant volume ratio in the two regions, which is important for predicting the temperature of the assembly's exterior duct wall. In Porous medium model 3, a precise resistance distribution was employed to define the characteristic of the porous medium. The results show that both porous medium model 2 and 3 can capture the average duct wall temperature well. Furthermore, the local duct wall variations due to different sub-channel patterns in bare rod bundles are well captured by porous medium model 3, although the wire effect on the duct wall temperature in wire wrap rod bundle has not been fully reproduced yet.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Nuclear Engineering and Design - Volume 295, 15 December 2015, Pages 48-58
Journal: Nuclear Engineering and Design - Volume 295, 15 December 2015, Pages 48-58
نویسندگان
Yiqi Yu, Elia Merzari, Aleksandr Obabko, Justin Thomas,