期刊
INTERNATIONAL JOURNAL OF HEAT AND MASS TRANSFER
卷 159, 期 -, 页码 -出版社
PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.ijheatmasstransfer.2020.120097
关键词
Plate-fin heat exchanger; Offset-strip fin; Many-objective optimization; Non-dominated sorting genetic algorithm; Random vector functional link network; Effectiveness
资金
- Associacao Paranaense de Cultura (APC)
- National Council of Scientific and Technologic Development of Brazil - CNPq [307958/2019-1, 404659/2016-0-Univ, 307966/2019-4, 405101/2016-3-Univ]
- Fundacao Araucaria by PRONEX Grant [042/2018]
In this study, an optimization technique combined with a Random Vector Functional Link (RVFL) network in the form of a surrogate-assisted approach was carried out for the optimal design of counter-flow plate-fin compact heat exchanger (PFHE) with offset-strip fins considering different mass flow rates. Computational Fluid Dynamics (CFD) simulations generated dataset for training and testing by RVFL network, reducing processing time and assuring PFHE accuracy when applied in many-objective optimization based on Non-Dominated Sorting Genetic Algorithm III (NSGA-III). Next, the NSGA-III was designed to achieve the maximum effectiveness, minimum volume, and pressure drop at the hot and cold sides in PFHE design. For experimental validation the Shear Stress Transport (SST) k-omega turbulence model was used to simulate the flow and heat transfer in PFHE showing 4.36% and 3.27% of error, respectively, to pressure drop and convective heat transfer coefficient measurements. The optimized results by NSGA-III indicated that the volume and effectiveness values are in agreement with the literature, decreasing approximately 55.4% and 72.3% of pressure drop at the hot and cold sides, respectively, which showed that the convective heat transfer was enhanced and the flow resistance was also significantly reduced. (C) 2020 Elsevier Ltd. All rights reserved.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据