Heat transfer enhancement of air flow through new types of perforated dimple/protrusion fins

酒窝 强化传热 材料科学 传热 机械 流量(数学) 机械工程 翼尖小翼 复合材料 工程类 传热系数 空气动力学 物理
作者
Palash Sen,Mengjie Chen,Bofeng Bai
出处
期刊:Applied Thermal Engineering [Elsevier]
卷期号:256: 124030-124030 被引量:7
标识
DOI:10.1016/j.applthermaleng.2024.124030
摘要

Heat transfer enhancement technologies of air flow are critical to energy conservation and emission reduction in industrial applications. This paper explores the numerical simulation analysis of 18 kinds of perforated dimple/protrusion fins. The wind speed varies within the range of 2 ∼ 6 m/s, and the investigation delves into the impact of different structural parameters on strengthening factor Nu/Nu0, resistance factor f/f0 and other comprehensive evaluation factor performance evaluation criteria (PEC) of perforated dimple/protrusion fins. The study specifically explores the influence of structural parameters, including intercepting perforated dimple/protrusion height (S0), hole diameter (D), hole structure offset (a), and the arrangement of perforated dimple/protrusion structure, on heat transfer performance. The comprehensive evaluation factor PEC of the new fin increased by 23.7 % compared with the porous fin. The overall heat transfer performance of the perforated dimple/protrusion fins rises with height under the same operating conditions. It first increases and then declines with the increasing hole diameter, and first drops and then increases with the increasing offset. The perforated protrusion/dimple fins obtain the best enhancement factor when the hole diameter is 3.4 mm. The hole structure offset is better on the inflowing leeward side than on the inflowing side. The fins with both perforated dimple and perforated protrusion structures have advantages over the one with either of two structures, and the alignment arrangements are identified as the optimal configuration. The new structural characteristics of perforated dimple/protrusion fins in heat exchangers can be utilized to create new designs that potentially outperform conventional models.
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