  Reconstruction and fast prediction of flow fields are important for the
improvement of data center operations and energy savings. In this study, an
artificial neural network (ANN) and variational autoencoder (VAE) composite
model is proposed for the reconstruction and prediction of 3D flowfields with
high accuracy and efficiency. The VAE model is trained to extract features of
the problem and to realize 3D physical field reconstruction. The ANN is
employed to achieve the constructability of the extracted features. A dataset
of steady temperature/velocity fields is acquired by computational fluid
dynamics and heat transfer (CFD/HT) and fed to train the deep learning model.
The proposed ANN-VAE model is experimentally proven to achieve promising field
prediction accuracy with a significantly reduced computational cost. Compared
to the CFD/HT method, the ANN-VAE method speeds up the physical field
prediction by approximately 380,000 times, with mean accuracies of 97.3% for
temperature field prediction and 97.9% for velocity field prediction, making it
feasible for real-time physical field acquisition.
