Comparison study on three ice storage equipments using micro-tube heat exchanger for cold chain logistics

ABSTRACT

Freezing efficiency was the main factor affecting the efficiency of cold chain logistics and transportation. To further the ice storage performance, three mathematical models of the ice storage equipments were established to explore the influence of the number and the diameter of the tubes on the freezing time, heat flux, and natural convection by analyzing the distribution of the ice fraction and velocity. The results showed that the total freezing time of Type II and Type III, which were arranged the micro-tube, were 76.42 min and 137.50 min, which was more than 90% less than that of type I. Due to a ring cavity of water appearing near the shell of the ice storage tank increased the water natural convection, the freezing time required to increase the ice fraction by 0.05 gradually increased especially with the ice fraction greater than 0.95. To better evaluate the ice storage performance, the freezing efficiency was introduced. The freezing efficiency of Type II and Type III was lower than that of Type I by more than 2.24% and 2.14%, respectively, for the ice fraction range of 0.75 ~ 1. Given the freezing time and ice storage efficiency, it could be found that using type II and making the ice storage rate reach 0.95 could shorten the freezing time and improve the ice storage efficiency.

KEYWORDS:

• Ice storage
• micro-tubules
• natural convection
• numerical simulation
• freezing efficiency

Disclosure statement

No potential conflict of interest was reported by the authors.

Nomenclature

Amush	=	The constant of the mushy zone
cp,mix	=	The specific heat capacity for the computational domain, J/kg·K
$c_{\mathit{p},\mathit{i}}$	=	Specific heat capacity of ice, J/kg·K
g	=	Gravitational acceleration, m/s2
href	=	The reference enthalpy, J/kg
L	=	The latent heat of the water, J/kg
m	=	Ice weight, kg
n	=	The normal direction of heat flux
P	=	Pressure, Pa
$S_h$	=	Source term in energy equation, J/m3
T	=	Temperature, K
$T_{\mathit{fin}}$	=	Fin temperature, K
Tref	=	The reference temperature, K
$T_w$	=	Water temperature, K
u	=	The velocity component along the x-axis, m/s
v	=	The velocity component along the y-axis, m/s
Vice	=	Ice volume, m3
Vt	=	Volume of the ice storage tank, m3
w	=	The velocity component along the z-axis, m/s
x	=	x axis, m
y	=	y axis, m
z	=	z axis, m
$\mathrm{\Delta }\mathit{H}$	=	The latent heat of water, J/kg
$\mathit{\eta }$	=	Freezing efficiency
$\vec{V}$	=	Velocity vector, m/s
${\tau }_f$	=	Freezing time, min
$\mathit{\rho }$	=	Density, kg/m3
Ω	=	The boundaries between the water and the fins
$\mathit{\mu }$	=	Dynamic viscosity of water, Pa·s
β	=	Liquid fraction
ξ	=	A constant number
${\lambda }_{\mathit{mix}}$	=	The thermal conductivity for the computational domain, J/kg·K
${\lambda }_w$	=	Water thermal conductivity, J/kg·K
${\lambda }_{\mathit{fin}}$	=	Thermal conductivity of fin, J/kg·K
${\lambda }_i$	=	Ice thermal conductivity, J/kg·K
$\mathit{\phi }$	=	Ice fraction

Additional information

Funding

The work was supported by the National Natural Science Foundation of China [51736006].

Notes on contributors

Zhengkun Jiang

Zhengkun Jiang is a doctoral student at Shanghai Jiao Tong University dedicated to research on the development of cold chain logistics equipments.

Shuangxi Xu

Shuangxi Xu is a postgraduate student at Tianjin University of Commerce. His research direction is cold storage technology.

Shifa Hu

Shifa Hu is a postgraduate student at Tianjin University of Commerce. His research direction is cold storage technology.

Yanjun Dai

Yanjun Dai is a professor at Shanghai Jiao Tong University.His research area of interest includes thermoelectric units and application, desiccant dehumidification and cooling and solar energy conversion and buidling energy. He has published more than 50 research paper in various National and International journal.