微型翅片疏水复合强化管管内流动沸腾换热预测模型

doi:10.11949/0438-1157.20240262

摘要/Abstract

摘要：

采用实验的方法对比制冷剂在一根光滑管（ST管）与三根强化管内的流动沸腾换热特性。实验所采用的三根强化管分别为具有疏水表面的换热管（HYD管）、具有人字形微翅片的换热管（HB管）和同时具有人字形微翅片与疏水表面复合结构的换热管（HB/HYD管）。在饱和温度为279、283与288 K，质量流速范围为50～150 kg/(m²·s)，进出口干度分别保持在0.2和0.8的工况下进行实验。从结果分析可以看出，HB管与HYD管的传热系数分别约为光滑管的1.37倍与1.42倍，而具有复合表面结构的HB/HYD管具有最高的传热系数，约为光滑管的1.45～1.63倍。针对所测传热系数选择了6种经验预测关联式模型，并利用光滑管进行验证，证明所选关联式可靠。结合强化管的结构特点开发新型关联式模型，该模型对所研究强化管的传热系数预测较好，三种强化管约有90%以上的数据点在误差范围为±10%以内，这对工业设计换热器的方案进行了优化，进而提高换热器的效率与可靠性。

关键词: 相变, 气液两相流, 流动沸腾, 换热, 预测模型

Abstract:

The flow boiling heat transfer characteristics of the refrigerant in one smooth tube (ST tube) and three enhanced tubes were compared experimentally. The three enhanced tubes used in the experiment are respectively the heat transfer tube with hydrophobic surface (HYD tube), the heat transfer tube with herringbone microfin (HB tube) and the heat transfer tube with the composite structure of herringbone microfin and hydrophobic surface (HB/HYD tube). The experiments were carried out at saturation temperatures of 279, 283 and 288 K, mass flux ranging from 50 kg/(m²·s) to 150 kg/(m²·s), and inlet and outlet vapor quality maintained at 0.2 and 0.8, respectively. It can be seen from the analysis of the results that the average heat transfer coefficient of HB tube and HYD tube is about 1.37 and 1.42 times that of smooth tube, respectively, the HB/HYD tubes with composite surface structure have the highest heat transfer coefficient, about 1.45—1.63 times that of smooth tube. According to the measured heat transfer coefficient, six empirical prediction correlation models are selected and verified by smooth tube, which proves the reliability of the selected correlation models. Combined with the structural characteristics of the enhanced tubes, a new correlation model is developed, which can predict the heat transfer coefficient of the studied enhanced tubes well. About 90% of the data points of the three enhanced tubes are within the error range of ±10%, this optimizes the scheme of industrial design of heat exchanger, thereby improving the efficiency and reliability of heat exchanger.

Key words: phase change, gas-liquid flow, flow boiling, heat transfer, prediction model

中图分类号:

TK 124

杜得辉, 冯威, 张江辉, 项燕龙, 乔高攀, 李蔚. 微型翅片疏水复合强化管管内流动沸腾换热预测模型[J]. 化工学报, 2024, 75(S1): 95-107.

Dehui DU, Wei FENG, Jianghui ZHANG, Yanlong XIANG, Gaopan QIAO, Wei LI. Prediction model of flow boiling heat transfer in microfinned hydrophobic composite enhanced tube[J]. CIESC Journal, 2024, 75(S1): 95-107.

图/表 15

图1 实验系统组成

Fig.1 Composition diagram of experimental system

图2 三个强化管的接触角示意图

Fig.2 Schematic diagram of the contact angles of the three enhanced tubes

图3 强化管表面结构与内表面映射图

Fig.3 Map between the surface structure of the enhanced tube and the inner surface

表1 四根测试管的几何参数

Table 1 Geometric parameters of four test tubes

参数	ST管	HB管	HYD管	HB/HYD管
长度/m	1.7	1.7	1.7	1.7
厚度/mm	0.61	0.61	0.61	0.61
翅片高度/mm	—	0.052	—	0.052
翅片间距/mm	—	0.636	—	0.636
螺旋角/(°)	—	18	—	18
补强角/(°)	—	161.4	—	161.4

表2 R32在不同温度下的基本热物性参数

Table 2 Basic physical parameters of R32 at different temperatures

饱和温度/K

液相密度/

(kg/m³)

气相密度/

(kg/m³)

液相动力黏度/

(Pa·s)

气相动力黏度/

(Pa·s)

液态热导率/

[W/(m·K)]

表面张力/

(N/m)

汽化潜热/

(kJ/kg)

图4 单相热平衡实验结果

Fig.4 Experimental results of single-phase heat balance

表3 测量参数的误差值

Table 3 Uncertainties of measuring parameters

测量参数	误差值	计算参数	误差值
直径/mm	± 0.02	质量流速/(kg/(m²·s))	± 1.47%
长度/mm	± 1	热通量/(W/m²)	± 1.76%
温度/K	± 0.1	蒸汽质量	± 2.68%
压力/kPa	满量程±0.075%	传热系数/(W/(m²·K))	± 9.15%
水流量/(kg/h)	观测值的± 0.35%
制冷剂流量/(kg/h)	观测值的±0.2%

图5 传热系数随质量流量变化

Fig.5 Change of heat transfer coefficient with mass flow rate

表4 HB/HYD管与现有商业强化管强化性能对比

Table 4 Comparison of strengthening performance between HB/HYD tube and existing commercial enhanced tube

强化性能	HB/HYD管	现有商业强化管
强化结构	采用人字形翅片与疏水表面复合强化结构，其表面结构具有一定的螺旋角，流体流经时有一定的接触角	一般采用单一结构，为内螺纹、凹坑、微翅片或涂层结构
强化原理	人字形微翅片强化结构可增加流体的换热面积，削弱了流体的表面张力，使流体分布均匀，翅片的存在增加了二次流，使得换热更充分。疏水表面，可增加成核位点的数量。二者共同作用，从而强化换热	一般是增加成核位点、增加换热面积或者促进形成涡流等
传热系数	提高，并且其疏水表面结构可以减少换热阻力	提高，但压降阻力也会增加
耐腐蚀性	采用不锈钢304L，具有耐腐蚀性能	一般为铜管，随着使用时间的增加，对腐蚀的敏感程度也会增加

表5 传热系数预测关联式模型

Table 5 Correlation model for heat transfer coefficient prediction

文献	公式
Shah^[25]	$h_{t p} = m a x (h_{c v}, h_{n b})$ $h_{c v} = \frac{1.8}{N^{0.8}} h_{l}, h_{l} = 0.023 R e^{0.8} P {r_{l}}^{0.4} \frac{λ_{l}}{d_{h}}$ $N = \{\begin{matrix} 0.38 F {r_{l}}^{- 0.3} C o F r_{l} < 0.04 \\ C o F r_{l} \geq 0.04 \end{matrix}$ $C o = {(\frac{1 - x}{x})}^{0.9} {(\frac{ρ_{v}}{ρ_{l}})}^{0.5}, F r_{l} = \frac{G^{2}}{{ρ_{l}}^{2} g d}, B o = \frac{q}{G h_{l g}}$ $N$ >1时 $h_{n b} = \{\begin{matrix} 230 B o^{0.5} h_{l} α_{b} \geq 3 \times 10^{- 4} \\ (1 + 46 B o^{0.5}) h_{l} α_{b} < 3 \times 10^{- 4} \end{matrix}$ $1 \geq N > 0.1$ 时 $h_{n b} = F B o^{0.5} e x p (2.74 N^{- 0.1}) h_{l}$ $N \leq 0.1$ 时 $h_{n b} = F B o^{0.5} e x p (2.74 N^{- 0.15}) h_{l}$ $B o > 0.0011, F = 14.7; B o < 0.0011, F = 15.43$
Chaddock等^[26]	$h_{t p} = 1.91 {[(B o \times 10^{4}) + 1.5 {(\frac{1}{X_{t t}})}^{0.67}]}^{0.6} h_{l}$ $X_{t t} = {(\frac{1 - x}{x})}^{0.9} {(\frac{ρ_{v}}{ρ_{l}})}^{0.5} {(\frac{μ_{l}}{μ_{v}})}^{0.1}$
Kandlikar^[15]	$h_{t p} = m a x (h_{c b}, h_{n b})$ $C o < 0.65, h_{c b} = [1.136 C o^{- 0.9} {(25 F r_{l})}^{0.3} + 667.2 B o^{0.7}] h_{l}$ $C o > 0.65, h_{c b} = [0.6683 C o^{- 0.2} {(25 F r_{l})}^{0.3} + 1058 B o^{0.7}] h_{l}$ $h_{l} = 0.023 R {e_{l}}^{0.8} P {r_{c}}^{0.4} \frac{λ_{l}}{d_{h}}$ ; $F r_{l} = \frac{G^{2}}{g d {ρ_{l}}^{2}}; B o = \frac{q}{G h_{l v}}; C o = {(\frac{1 - x}{x})}^{0.8} {(\frac{ρ_{v}}{ρ_{l}})}^{0.5}$
Kandlikar and Balasabramanian^[27]	$h_{t p} = m a x (h_{c b}, h_{n b})$ $C o < 0.65, h_{c b} = [1.136 C o^{- 0.9} {(1 - x)}^{0.8} + 667.2 B o^{0.7} {(1 - x)}^{0.8}] h_{l}$ $C o > 0.65, h_{c b} = [0.6683 C o^{- 0.2} {(1 - x)}^{0.8} + 1058 B o^{0.7} {(1 - x)}^{0.8}] h_{l}$
Kuang等^[28]	$h_{t p} = 427.13 B o^{0.6223} h_{l o}$ $h_{l o} = 0.023 R {e_{l o}}^{0.8} P {r_{l}}^{0.4} \frac{λ_{l}}{d_{h}}; R e_{l o} = \frac{G d}{μ_{l}}$
Schrock等^[29]	$h_{t p} = 170 (B o + 1.5 \times 10^{- 4} {X_{t t}}^{- \frac{2}{3}}) R e^{0.8} P {r_{l}}^{\frac{1}{3}} \frac{λ_{l}}{d}$ $X_{t t} = {(\frac{1 - x}{x})}^{0.9} {(\frac{ρ_{v}}{ρ_{l}})}^{0.5} {(\frac{μ_{l}}{μ_{v}})}^{0.1}$

表5 传热系数预测关联式模型

Table 5 Correlation model for heat transfer coefficient prediction

文献	公式
Shah^[25]	$h_{t p} = m a x (h_{c v}, h_{n b})$ $h_{c v} = \frac{1.8}{N^{0.8}} h_{l}, h_{l} = 0.023 R e^{0.8} P {r_{l}}^{0.4} \frac{λ_{l}}{d_{h}}$ $N = \{\begin{matrix} 0.38 F {r_{l}}^{- 0.3} C o F r_{l} < 0.04 \\ C o F r_{l} \geq 0.04 \end{matrix}$ $C o = {(\frac{1 - x}{x})}^{0.9} {(\frac{ρ_{v}}{ρ_{l}})}^{0.5}, F r_{l} = \frac{G^{2}}{{ρ_{l}}^{2} g d}, B o = \frac{q}{G h_{l g}}$ $N$ >1时 $h_{n b} = \{\begin{matrix} 230 B o^{0.5} h_{l} α_{b} \geq 3 \times 10^{- 4} \\ (1 + 46 B o^{0.5}) h_{l} α_{b} < 3 \times 10^{- 4} \end{matrix}$ $1 \geq N > 0.1$ 时 $h_{n b} = F B o^{0.5} e x p (2.74 N^{- 0.1}) h_{l}$ $N \leq 0.1$ 时 $h_{n b} = F B o^{0.5} e x p (2.74 N^{- 0.15}) h_{l}$ $B o > 0.0011, F = 14.7; B o < 0.0011, F = 15.43$
Chaddock等^[26]	$h_{t p} = 1.91 {[(B o \times 10^{4}) + 1.5 {(\frac{1}{X_{t t}})}^{0.67}]}^{0.6} h_{l}$ $X_{t t} = {(\frac{1 - x}{x})}^{0.9} {(\frac{ρ_{v}}{ρ_{l}})}^{0.5} {(\frac{μ_{l}}{μ_{v}})}^{0.1}$
Kandlikar^[15]	$h_{t p} = m a x (h_{c b}, h_{n b})$ $C o < 0.65, h_{c b} = [1.136 C o^{- 0.9} {(25 F r_{l})}^{0.3} + 667.2 B o^{0.7}] h_{l}$ $C o > 0.65, h_{c b} = [0.6683 C o^{- 0.2} {(25 F r_{l})}^{0.3} + 1058 B o^{0.7}] h_{l}$ $h_{l} = 0.023 R {e_{l}}^{0.8} P {r_{c}}^{0.4} \frac{λ_{l}}{d_{h}}$ ; $F r_{l} = \frac{G^{2}}{g d {ρ_{l}}^{2}}; B o = \frac{q}{G h_{l v}}; C o = {(\frac{1 - x}{x})}^{0.8} {(\frac{ρ_{v}}{ρ_{l}})}^{0.5}$
Kandlikar and Balasabramanian^[27]	$h_{t p} = m a x (h_{c b}, h_{n b})$ $C o < 0.65, h_{c b} = [1.136 C o^{- 0.9} {(1 - x)}^{0.8} + 667.2 B o^{0.7} {(1 - x)}^{0.8}] h_{l}$ $C o > 0.65, h_{c b} = [0.6683 C o^{- 0.2} {(1 - x)}^{0.8} + 1058 B o^{0.7} {(1 - x)}^{0.8}] h_{l}$
Kuang等^[28]	$h_{t p} = 427.13 B o^{0.6223} h_{l o}$ $h_{l o} = 0.023 R {e_{l o}}^{0.8} P {r_{l}}^{0.4} \frac{λ_{l}}{d_{h}}; R e_{l o} = \frac{G d}{μ_{l}}$
Schrock等^[29]	$h_{t p} = 170 (B o + 1.5 \times 10^{- 4} {X_{t t}}^{- \frac{2}{3}}) R e^{0.8} P {r_{l}}^{\frac{1}{3}} \frac{λ_{l}}{d}$ $X_{t t} = {(\frac{1 - x}{x})}^{0.9} {(\frac{ρ_{v}}{ρ_{l}})}^{0.5} {(\frac{μ_{l}}{μ_{v}})}^{0.1}$

图6 光滑管蒸发传热系数预测值与实验值对比

Fig.6 Comparison between the predicted value and the experimental value of evaporative heat transfer coefficient of smooth tube

表6 光滑管实验值与预测值之间的偏差

Table 6 Deviations between experimental and predicted values of smooth tube

误差	Shah等^[25]	Chaddock等^[26]	Kandlikar等^[27]	Kandlikar^[15]	Kuang等^[28]	Shrock等^[29]
MAE	13.3%	11.45%	26.3%	20.9%	21.8%	21.2%
MRE	-11.5%	8.41%	26.3%	-18.7%	21.8%	21.2%

表7 新型关联式各待定系数值

Table 7 Values of undetermined coefficients of the new correlation

强化管	a	b	c	d	e
HB管	192	0.244	0.068	0.128	0.126
HYD管	600	0.2	-0.085	0.368	0.055
HB/HYD管	1110	0.233	0.06	-0.151	0.492

图7 新型关联式对三种强化管的预测对比

Fig.7 Comparison of prediction of three kinds of enhanced tubes by the new correlation

表8 强化管实验值与新关联式预测值的偏差

Table 8 Deviations between the experimental value of the enhanced tube and the predicted value of the new correlation

误差	HB管	HYD管	HB/HYD管
MAE	4.97%	4.95%	4.14%
MRE	-1.32%	0.67%	-0.53%

参考文献 30

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