Temperature digital twins model for blueberry pre-cooling based on micro-cluster method

Abstract

Abstract Objectives In order to improve the prediction accuracy of forced-air pre-cooling for blueberries, a mathematical model of forced-air pre-cooling for blueberries based on the micro-cluster method was established. Materials and Methods In order to determine the optimal micro-cluster model parameters suitable for forced air pre-cooling of blueberries, three factors controlling the micro-cluster geometry parameters were evaluated by 7/8 pre-cooling time, uniformity, and convective heat transfer coefficient. Results It was found that the optimal values of the number of micro-clusters (n3), the distance between individual units within a micro-cluster (a) and the distance between micro-clusters (c) were 3, 0.75, and 0.2, respectively. Under these optimal values, the temperature error of the micro-cluster method remained below 1 °C, achieving highly accurate temperature predictions during the blueberry pre-cooling process. The results showed that the micro-cluster method effectively solved the challenges of complex configuration, long simulation time, and low accuracy compared to the porous medium and equivalent sphere methods. Conclusion Based on the above analysis, it can be concluded that the micro-cluster method provids a theoretical basis for optimizing forced-air pre-cooling processes and making informed control decisions.