脉动热管温度信号的小波分析及流型识别

doi:10.11949/0438-1157.20231319

摘要/Abstract

摘要：

脉动热管中的温度波动信号具有较复杂的瞬态波动特征，采用连续小波变换方法可以较好地分析此类信号特征。采用全玻璃脉动热管，在可视化实验基础上，应用小波分析方法，重点研究PHP温度振荡信号及流型识别。结果表明，采集频率、管壁材料以及热通量等会影响温度信号的主频值。当热通量较高（ $q$ =2.65~3.18 W/cm²）时，1 Hz的采集频率容易导致信号失真，应采用10 Hz及以上的采集频率。玻璃管的热惰性会导致温度信号失真，尤其在高热通量（ $q$ =2.65~3.18 W/cm²）时不可忽略。总体来看，随着热通量的增加，蒸发温度波动幅度减小、频率增加。当热通量从0.35 W/cm²增加到3.18 W/cm²，流体温度信号的主频值从0.02 Hz增加到3.88 Hz。相应地，管内流型由弹状流逐渐向环状流转变，并出现单向大循环流。由管内流体温度信号求得的主频值可推广至铜管或其他金属材料的脉动热管，有助于识别其内部流型及流态变化，更好地理解其传热特性。

关键词: 脉动热管, 小波分析, 信号主频, 流动, 两相流, 传热

Abstract:

The temperature signals in pulsating heat pipe (PHP) exhibit more complex transient fluctuation characteristics, which can be better analyzed by using continuous wavelet transform (CWT) method. Based on the visualization experiment (glass PHP), the wavelet analysis method was used to investigate the PHP temperature oscillation signal and flow pattern identification. The results revealed that the dominant frequency of the temperature signals is affected by the sampling frequency, wall material and heat flux. A sampling frequency of 1 Hz may lead to temperature signal distortion at high heat flux ( $q$ =2.65—3.18 W/cm²), and it is recommended to use a sampling frequency of 10 Hz or higher. The thermal inertia of the glass material introduces signal distortion in wall temperature measurements, particularly at high heat flux. With increasing heat flux ( $q$ =0.35—3.18 W/cm²), the fluctuation amplitude of the fluid temperature decreases, the frequency increases, and the dominant frequency of the temperature signal increases (0.02—3.88 Hz). Correspondingly, the internal flow of the PHP has gradually transformed from a slug flow to the annular flow, and the large one-direction circulation flow appears. The dominant frequency derived from the fluid temperature signal inside the tube can be generalized to copper tubes or other metal PHP, which helps to identify the internal flow pattern and the change in the flow regime, and to better understand their heat transfer characteristics.

Key words: pulsating heat pipe, wavelet analysis, signal dominant frequency, flow, two-phase flow, heat transfer

中图分类号:

TK 79

余清杰, 杨洪海, 刘玉浩, 方海洲, 何伟琪, 王军, 卢心诚. 脉动热管温度信号的小波分析及流型识别[J]. 化工学报, 2024, 75(7): 2497-2504.

Qingjie YU, Honghai YANG, Yuhao LIU, Haizhou FANG, Weiqi HE, Jun WANG, Xincheng LU. Wavelet analysis and flow pattern identification in pulsating heat pipes based on temperature signals[J]. CIESC Journal, 2024, 75(7): 2497-2504.

图/表 8

图1 PHP以及实验系统

Fig.1 PHP sample and experimental setup

表1 主要实验参数的最大相对不确定度

Table 1 Relative maximum uncertainties of main experimental parameters

参数	不确定度
$T e$	±0.24%
$Q$	±2.97%
$q$	±3.01%

表1 主要实验参数的最大相对不确定度

Table 1 Relative maximum uncertainties of main experimental parameters

参数	不确定度
$T e$	±0.24%
$Q$	±2.97%
$q$	±3.01%

图2 蒸发段内流体温度的时频能量图及小波功率谱

Fig.2 Time frequency energy map and wavelet power spectrum of fluid temperature in evaporation section

图3 3种采集频率下的小波功率谱

Fig.3 Wavelet power spectrum under three sampling frequencies

图4 蒸发段壁温（Tew）及流体温度（Tef）的波动曲线

Fig.4 Fluctuation curves of evaporation section wall temperature（Tew） and fluid temperature（Tef）

图5 热通量对主频值的影响

Fig.5 Effect of heat flux on dominant frequency value

图6 典型热通量下蒸发段内流体温度（Tef）波动曲线

Fig.6 Fluctuation curves of evaporation section fluid temperature （Tef） under representative heat fluxes

图7 蒸发段内流型随热通量的变化

Fig.7 Flow pattern images with different heat flux in evaporation section

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