CFD analysis and IoT integration on effect of the temperature distribution on cold storage system

Cold storage systems play a crucial role in preserving perishable goods, with temperature distribution being a key factor in maintaining product quality and safety. This study integrates Computational Fluid Dynamics (CFD) analysis with Internet of Things (IoT) technology to comprehensively investigate and optimize temperature distribution within cold storage environments. Advanced CFD simulations are employed to model airflow patterns, heat transfer mechanisms, and temperature variations throughout the storage system. Concurrently, IoT sensors are strategically deployed to collect real-time data on temperature fluctuations at various points within the facility. The collected data is continuously analyzed to detect irregularities, allowing for dynamic adjustments in airflow control and cooling efficiency. By integrating real-time sensor feedback with CFD models, the system enables predictive optimization, ensuring uniform air distribution, reducing temperature deviations, and enhancing energy efficiency. This adaptive approach not only maintains optimal storage conditions for perishable goods but also minimizes energy consumption and prevents localized overheating or overcooling. The study highlights the synergy between CFD analysis and IoT-based monitoring as a powerful tool for improving cold storage performance.