Validation of CFD models for the deep-bed drying of rice using thermal imaging

Abstract

Validation is one of the most important steps in modelling the drying process of cereal crops. Once a simulation model is validated, it can be used for further practical applications. This study examines the potential of the thermal imaging technique to validate computational fluid dynamic (CFD) simulation models developed for describing the deep-bed drying process of rough rice and to visualise the temperature profiles throughout the bed under different drying conditions. A laboratory forced-air convective dryer was designed and fabricated to dry the rough rice in deep layers and thermal images of the rough rice inside the drying bin were directly acquired during drying process. The predicted data of the CFD models for moisture and temperature distributions through the deep bed during drying were verified against the experimental results. The results revealed that the CFD model developed for predicting moisture content exhibited good correlation with a coefficient of determination R2 = 0.96. The model was also very accurate for predicting the temperature of rough rice in the deep-bed dryer with coefficients of determination > 0.90 and low RMSE (