Digital Twin–Based Predictive Thermal Management of Cold Plate for Electronics Devices
This study proposes a digital twin–based thermal management method for cold plate. The goal is to achieve real-time precise control and energy-efficient design. First, a conjugate heat transfer model of the cold plate is developed in the COMSOL Heat Transfer module, and 100 samples of chips power and coolant flow rate are generated using Latin Hypercube Design (LHD). For each sample condition, simulations are performed to extract temperature distribution with the maximum chip temperature as the output. A high-precision surrogate model is then generated by deep neural network (DNN) training on this dataset using the COMSOL DNN feature. Finally, the trained surrogate model is deployed as a COMSOL APP and integrated with external hardware signal interfaces. When real-time chip power variations are read in, and the surrogate model predicts the required coolant flow rate to maintain safe temperature level while minimizing energy consumption.
The proposed approach reduces the number of required physical sensors, achieves more precise temperature control, and lowers average coolant flow. By leveraging the COMSOL App with Timer Events, it enables automated and real-time monitoring and control during operation. This demonstrates fast response and high energy efficiency, confirming that digital twin–based thermal management is both practical and advantageous for advanced electronics cooling.
