The reasonable optimization and improvement of the structure of the refrigerated compartment plays an important role in efficiently improving the cooling effect of a refrigerated truck

which ensures the quality and safety of the goods during refrigerated transport. It is also an effective way to deal with the needs of cold chain suppliers. In this study

four kinds of refrigerated trucks with the same working conditions but different structures were examined

with apples used as the test material and based on the same goods stack. Computational fluid dynamics simulations were used for the internal temperature distributions of the four compartments under specific wind speed and cooling temperature conditions. The cooling performances (temperature distribution

cooling time

cooling uniformity) of the four refrigerated trucks were compared and analyzed. The results showed that the installation of both a side ventilation trough and ground rail was the best way to improve the cooling performance of refrigerated trucks. The coefficient of temperature variation was 0.0013

with 62.06% of the goods were within a temperature range of 3–4.5 ℃. However

there was no obvious improvement in the cooling performance compared with the single installation of a side ventilation trough

where the coefficient of temperature variation was 0.0015

and 59.26% of the goods were within a temperature range of 3–4.5 ℃. However

compared with the single installation of the guide rail

the single side ventilation trough could significantly reduce the cooling time and improve the cooling uniformity. Compared with a refrigerated truck with no guide rail

although there was no obvious reduction in the cooling time

the installation of the guide rail by itself could improve the uniform cooling of the cargo. The coefficient of temperature variation for the single installation of the guide rail was 0.0021

and the coefficient of temperature variation of the no guide rail was 0.0024. A comparison of the experimental and simulated values for the wind speed and temperature showed good agreement

with temperature maximum root mean square errors of 0.221 ℃ and 0.198 ℃

respectively

and maximum average relative errors of 18.35% and 16.91%

respectively. The maximum air speed deviation between the simulated and measured values was 0.3 m/s. This study provided some reference values for the optimization of different cold chain requirements to ensure the quality and safety of agricultural products during refrigerated transportation.