کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
7053971 | 1458014 | 2018 | 14 صفحه PDF | دانلود رایگان |
عنوان انگلیسی مقاله ISI
A “2.5-D” modeling approach for single-phase flow and heat transfer in manifold microchannels
ترجمه فارسی عنوان
یک 2.5؟ رویکرد مدل سازی برای انتقال تک فاز و انتقال گرما در میکرو کانال های چند منظوره
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کلمات کلیدی
موضوعات مرتبط
مهندسی و علوم پایه
مهندسی شیمی
جریان سیال و فرایندهای انتقال
چکیده انگلیسی
A reduced-order “2.5-D” computational fluid dynamics (CFD) modeling approach for single-phase flow and heat transfer in manifold-microchannel heat exchangers was developed, and found to exhibit an order-of-magnitude reduced computational cost compared to a full 3-D simulation. Unlike previous approaches that neglect the convective terms in the momentum equations and assume fully developed flow, in the present work, the inertial terms in the momentum equations were retained, and a user-defined-scalar was used to calculate flow distance so that developing flow could be assumed. The 2.5-D model was then compared to a full 3-D CFD simulation, and was shown to be accurate as long as inertia is low enough to prevent the onset of secondary flows. The governing dimensionless parameters were defined, and the effect of each dimensionless parameter was investigated via parametric studies. Finally, a multi-dimensional parametric study was performed to determine the dimensionless parameter that governs the accuracy of the 2.5-D approach. In the end, it was determined that as long as dimensionless length is above 0.1, pressure drop can be predicted to within an average error of â¼7% for any fluid, and heat transfer can be predicted to within an average error of 6% for water and air.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: International Journal of Heat and Mass Transfer - Volume 126, Part A, November 2018, Pages 317-330
Journal: International Journal of Heat and Mass Transfer - Volume 126, Part A, November 2018, Pages 317-330
نویسندگان
Raphael Mandel, Amir Shooshtari, Michael Ohadi,