Optimizing Commercial-Scale Storage for Chinese Cabbage ( L. ssp. ): Integrating Morphological Classification, Respiratory Heat Effects, and Computational Fluid Dynamics for Enhanced Cooling Efficiency.

This study optimized Chinese cabbage ( L. ssp. ) storage design by integrating K-means clustering, heat transfer analysis, and respiratory heat effects. A morphological assessment identified three clusters: class 1 (73.32 ± 3.34 cm length, 46.73 ± 2.24 cm width, 1503.20 ± 118.39 g weight), class 2 (82.67 ± 1.17 cm, 51.89 ± 2.37 cm, 2132.48 ± 127.16 g), and class 3 (89.17 ± 2.45 cm, 58.67 ± 2.77 cm, 2826.37 ± 121.25 g), with a silhouette coefficient of 0.87 confirming robust clustering. The CO, relative humidity, and airflow analysis revealed hotspots and imbalances. Heat transfer modeling, incorporating respiratory heat, closely matched experimental data (RMSE < 0.54 °C), while excluding it caused deviations in storage. The validated model informed a modified geometry for scale-up CFD modeling, reducing the convergence time by 38% and the RAM usage by 30%. Three commercial storage designs were evaluated: fully filled, batch filled (50:50), and repositioned air conditioning with batch filling. The latter achieved a faster equilibrium (4.1 °C in 17 h 15 min vs. 21 h 30 min for fully packed) and improved airflow, reducing the hot zones. This study highlights the importance of integrating cabbage morphology, environmental factors, and respiratory heat into storage design to enhance cooling efficiency and product quality.