Modelling of the thermal conductivity in cold chain logistics based on micro-PCMs

Modelling of the thermal conductivity in cold chain logistics based on micro-PCMs

Xuanxuan Zhang

College of Economics and Business Administration, Chongqing University, Chongqing 400044, China

Corresponding Author Email: 
jimmyzxx@126.com
Page: 
1075-1080
|
DOI: 
https://doi.org/10.18280/ijht.360339
Received: 
10 November 2017
| |
Accepted: 
30 May 20118
| | Citation

OPEN ACCESS

Abstract: 

This paper focuses on the thermal conductivity in cold chain logistics. Based on the basic theory of heat transfer, it improves the heat transfer performance of micro-PCMs by adding thermally conductive fluid into micro-PCMs, and then analyzes the change in the thermal conductivity before and after improvement as well as the cold storage speed and temperature control performance after the addition of the thermally conductive fluid. In addition, it also establishes an effective thermal conductivity model for the dispersion system based on the fractal theory. The results show that the effective thermal conductivity of micro-PCMs increases with the increasing volume of thermally conductive fluid added. When the volume fraction reaches about 50%, the increase of the effective thermal conductivity significantly slows down. Through model analysis, it is concluded that the effective thermal conductivity of the dispersion system cannot be accurately characterized without considering the gas phase influences, and that the simulated value considering the gas phase influences is consistent with the test value. Through test analysis and numerical modelling, it is found that the optimal volume fraction of thermally conductive fluid in micro-PCMs is between 36-40%. This study provides theoretical support for the heat transfer system of micro-PCMs in cold chain logistics.

Keywords: 

thermal conductivity, micro-PCMs, fractal theory, thermally conductive fluid

1. Introduction
2. Performance Optimization of the Thermal Conductivity and Characterization Analysis After Optimization
3. Effective Thermal Conductivity Modelling Method and Analysis
4. Conclusions
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