Analysis of the Temperature Distribution in a Refrigerated Truck Body Depending on the Box Loading Patterns

• Published in: Foods 2021-10, Vol.10 (11), p.2560
• Main Authors: So, Jun-Hwi, Joe, Sung-Yong, Hwang, Seon-Ho, Jun, Soojin, Lee, Seung-Hyun
• Format: Article
• Language: English
• Subjects: Air flow; airflow; Body temperature; Boxes; CFD modelling; Cold storage; Experiments; Food science; Heat transfer; High temperature; Humidity; Insulation; K-epsilon turbulence model; loading pattern; Logistics; Monitoring systems; Numerical analysis; Numerical methods; refrigerated truck; Refrigeration; Reynolds number; Sensors; Simulation; Supply chains; Temperature; Temperature distribution; Temperature gradients; temperature prediction; Temperature sensors; Trucks
• ISSN: 2304-8158; 2304-8158
• DOI: 10.3390/foods10112560

The main purpose of cold chain is to keep the temperature of products constant during transportation. The internal temperature of refrigerated truck body is mainly measured with a temperature sensor installed at the hottest point on the body. Hence, the measured temperature cannot represent the overall temperature values of transported products in the body. Moreover, the airflow pattern in the refrigerated body can vary depending on the arrangement of loaded logistics, resulting temperature differences between the transported products. In this study, the airflow and temperature change in the refrigerated body depending on the loading patterns of box were analyzed using experimental and numerical analysis methods. Ten different box loading patterns were applied to the body of 0.5 ton refrigerated truck. The temperatures inside boxes were measured depending on the loading patterns. CFD modeling with two different turbulence models (k-ε and SST k-ω) was developed using COMSOL Multiphysics for predicting the temperatures inside boxes loaded with different patterns, and the predicted data were compared to the experimental data. The k-ε turbulence model showed a higher temperature error than the SST k-ω model; however, the highest temperature point inside the boxes was almost accurately predicted. The developed model derived an approximate temperature distribution in the boxes loaded in the refrigerated body.