Chul-Hee Lee

The heat dissipation structure was developed using a composite bionic design method. An adjoint solver optimized the shape of the heat dissipation structure to achieve a more uniform temperature and velocity distribution, addressing the heat dissipation challenges of high-performance electronic equipment. Additionally, 3D metal printing technology was employed for performance testing. This study primarily examined the effects of flow channel geometry (pressure drop), inlet flow rate, and working fluid type on the heat transfer efficiency and the uniformity of temperature and velocity distribution in microchannel heat exchangers. The pressure drop was reduced by 50.63% (from 0.079 MPa to 0.039 MPa), and the maximum temperature decreased by 7.2 K. As the inlet flow rate increased, the heat dissipation effect continued to improve. Regarding working fluid selection, the heat transfer efficiency of a calcium formate/water mixture surpassed that of ethylene glycol/water and propylene glycol/water mixtures; however, the latter two exhibit better temperature distribution uniformity. The final research findings offer an effective solution to the heat dissipation issues faced by high-performance equipment and present a novel approach to optimizing the design of microchannel heat exchangers, which holds significant reference value.

This study introduces a deep learning (DL)-based optimal condition monitoring and control system tailored to indoor smart greenhouses, with a novel focus on maintaining symmetry—defined as a dynamic equilibrium among temperature, humidity, and CO₂ levels—critical in plant growth. A hydroponic greenhouse prototype was developed to capture real-time climate data at high temporal resolution. A custom 1D convolutional neural network (1D-CNN) optimized via a genetic algorithm (GA) was employed to predict environmental fluctuations, achieving R² scores up to 0.99 and a standard error of prediction (SEP) as low as 0.35%. The system then actuated climate control mechanisms to restore and maintain symmetry. Experimental validation revealed that plants grown under the symmetry-aware control system exhibited significantly improved growth metrics. The results underscore the potential of integrating symmetry-aware DL strategies into precision agriculture in achieving sustainable and resilient plant production systems.