Zhang Jian, Xie Zhihui, Lu Zhuoqun, Li Penglei, Xi Kun
College of Power Engineering, Naval University of Engineering, Wuhan 430033, China.
School of Energy and Electromechanic Engineering, Hunan University of Humanities, Science and Technology, Loudi 417000, China.
Micromachines (Basel). 2022 Jun 9;13(6):918. doi: 10.3390/mi13060918.
A numerical simulation model of embedded liquid microchannels for cooling 3D multi-core chips is established. For the thermal management problem when the operating power of a chip changes dynamically, an intelligent method combining BP neural network and genetic algorithm is used for distribution optimization of coolant flow under the condition with a fixed total mass flow rate. Firstly, a sample point dataset containing temperature field information is obtained by numerical calculation of convective heat transfer, and the constructed BP neural network is trained using these data. The "working condition-flow distribution-temperature" mapping relationship is predicted by the BP neural network. The genetic algorithm is further used to optimize the optimal flow distribution strategy to adapt to the dynamic change of power. Compared with the commonly used uniform flow distribution method, the intelligently optimized nonuniform flow distribution method can further reduce the temperature of the chip and improve the temperature uniformity of the chip.
建立了用于冷却三维多核芯片的嵌入式液体微通道数值模拟模型。针对芯片工作功率动态变化时的热管理问题，采用一种结合BP神经网络和遗传算法的智能方法，在总质量流量固定的条件下对冷却液流量进行分配优化。首先，通过对流换热数值计算得到包含温度场信息的样本点数据集，并利用这些数据对构建的BP神经网络进行训练。由BP神经网络预测“工况-流量分配-温度”映射关系。进一步利用遗传算法优化最优流量分配策略，以适应功率的动态变化。与常用的均匀流量分配方法相比，智能优化的非均匀流量分配方法能够进一步降低芯片温度，提高芯片温度均匀性。
