Reconstruction of the Three-Dimensional Temperature Field of Air Ditribution Based on the Machine Learning Method
It is of important research significance for energy conservation and carbon conservation in the computer rooms of the data center to reconstruct global flow field information from local discrete flow field data points,and the rapid reconstruction of the temperature field of air distribution in the data center based on local scatter points can effectively reduce the total energy consumption of the data center.This article mainly develops a three-dimensional U-net neural network architecture based on multi-feature input,which utilizes the data value of tem-perature sensors deployed in the computer rooms of the data center to reconstruct the temperature field of air distri-bution,and studies the training prediction ability of the machine learning model under the setting of different learn-ing rates and dataset sizes.The comparison results indicate that different learning rates have great impacts on the training results of the model,and the optimal learning rate should be selected through pre-experiments,and that under the same conditions,priority should be given to selecting a larger dataset for training,in order to extract high-dimensional physical features.
Cloud computingMachine learning calculation of flow dynamicsAirflow organizationTemperature field
万方数据
维普
