文献类型：期刊文献

英文题名：Multi-objective optimization of geometric parameters for the helically coiled tube using Markowitz optimization theory

作者：Han, Yong[2];Wang, Xue-sheng[1];Zhang, Zhao[1];Zhang, Hao-nan[1]

机构：[1]East China Univ Sci & Technol, Key Lab Pressure Syst & Safety, Minist Educ, Shanghai 200237, Peoples R China;[2]Zhengzhou Univ Light Ind, Sch Energy & Power Engn, Zhengzhou 450002, Henan, Peoples R China

年份：2020

卷号：192

外文期刊名：ENERGY

收录：;EI(收录号：20195007831275);WOS:【SCI-EXPANDED(收录号:WOS:000515212800081)】；

语种：英文

外文关键词：The helically coiled tube (HCT); Multi-objective optimization; Markowitz optimization; Effective boundary; Entropy generation number

摘要：In this research, a novel multi-objective optimization of helically coiled tube (HCT) using Markowitz effective boundary theory was studied. Firstly, the expressions of modified entropy generation number (EGN, N-s,N-c. N-s,N-p N-s) for the HCT was derived. Secondly, the mathematical relation between the heat transfer EGN (N-s,N-c) and NTU was validated. Then, a novel method to acquire the effective boundary was proposed. Finally, the results of Markowitz optimization were compared with MOGA. The results show that, in comparison between the 2 optimization methods, relative error of heat transfer coefficient using both optimization methods are below +/- 2%; as for the flow resistance, the relative error of Markowitz effective boundary is still below +/- 2%, the relative error of MOGA is more than +/- 2%; there are 3 optimal points in each optimization; when heat transfer coefficient (h) and pressure drop (vertical bar Delta p vertical bar) are selected as the objective functions, the PEC of Markowitz optimization increases by 1.63%, 3.36% and 0.4%, respectively; when heat transfer coefficient (h), pressure drop (vertical bar Delta p vertical bar) and total EGN (N-s) are selected as the objective functions, the PEC of Markowitz optimization increases by 3.25%, 25.47% and 21.97%, respectively. Therefore, the Markowitz optimization is more effective than the MOGA of ANSYS. (C) 2019 Elsevier Ltd. All rights reserved.