基于多参数耦合的蓄冷温控箱冷板对流换热参数优化

蓄冷温控箱利用低温相变材料储存冷量，通过缓慢释放调节并保持箱内温度，目前仍存在冷量释放速率无法控制、剩余冷量预测难等问题，而蓄冷板表面对流换热系数直接影响冷量的释放速率。针对以上问题，搭建了蓄冷板表面对流换热系数测量试验平台，研究不同环境及蓄冷板参数对表面对流换热系数的影响。采用二次回归正交试验设计方案，探究了蓄冷区进口空气流速、进口空气温度、蓄冷板传热面积以及蓄冷板间距对表面对流换热系数的影响，并对结果进行分析，建立了表面对流换热系数二阶预测模型，获得影响表面对流换热系数大小较显著的因素及较优的参数组合。试验结果表明：进口空气温度和蓄冷板传热面积的交互效应最大；通过响应曲面法建立的表面对流换热系数预测模型，得到最优参数组合为：进口空气流速4 m/s，进口空气温度25 ℃，蓄冷板传热面积0.455 m2，蓄冷板间距0.04 m，模型决定系数值为0.927 4，变异系数为5.78%。回归模型计算结果与试验结果吻合，最大误差为3.58%，平均相对误差为2.69%，表明该模型可以快速、准确地预测不同条件下的蓄冷板表面对流换热系数。试验结果为蓄冷温控箱冷量释放速率精准调控及剩余冷量预测提供参考。

Parameter optimization on convective heat transfer of cold plate for cold storage temperature control box based on multi-parameter coupling

A transport container with a controlled temperature was developed, where the temperature was regulated using low-temperature phase-change materials. The cold energy was first stored in low-temperature phase-change materials and then released when the temperature in the container was out of target range under an intelligent control system. However, there were still some issues that need to be solved, such as the difficulties in controlling the release rate of cold energy, the prediction of remaining cold energy during the transportation work. The release rate of cold energy depended directly on the convective heat transfer coefficient between the surface of the cold storage plate and the ambient air. In this study, an experimental platform was developed to investigate the influence of different environments and parameters of cold storage plates on the convective heat transfer coefficient between the cold storage plate surface and the ambient air. A quadratic regression orthogonal experiment was adopted to clarify the coupling effects among the factors, including the air velocity and temperature at the entrance of the cool storage area, heat transfer area of the cold storage plate, and the space between them on the surface convective heat transfer coefficient. After that, the experimental data were analyzed. A second-order prediction model of surface convective heat transfer coefficient was built that the relationships between the influence factors and the surface convective heat transfer coefficient and the factors with significant effects were obtained, as well as the optimal values of such factors. Consequently, there was the most significant interaction between the entrance air temperature and the heat transfer area of the cold storage plate. The prediction model of surface convective heat transfer coefficient built by response surface method presented a higher accuracy, where the best combination of parameters was velocity=4 m/s, temperature=25 ℃, area=0.455 m2, spacing=0.04 m, and the determination coefficient value was 0.927 4 and the coefficient of variation was 5.78%. The calculated results of such regression model were in good agreement with the experimental, with the maximum error of 3.58% and an average relative error of 2.69%, indicating that such model can be used to quickly and accurately predict the convective heat transfer coefficient between the surface of cold storage plate and the ambient air under different conditions. The finding can provide accurate control on the release rate of cold energy in the temperature phase-change materials, and the prediction of remaining cooling energy for transport containers with controlled temperature.