An ANN–CNN Hybrid Surrogate Model for Fast Prediction of 3D Temperature Fields in Large Datacenter Rooms

Abstract

The increasing energy consumption of large datacenters, with cooling systems constituting a significant portion, calls for efficient thermal management strategies. Conventional computational fluid dynamics (CFD) methods, although accurate, are time-consuming for supporting real-time tasks in dynamic datacenter environments. Machine learning (ML)-based methods, particularly artificial neural network (ANN)-based surrogate models, have emerged as potential alternatives, but they struggle with generalization across diverse working conditions. Meanwhile, ML models’ performance in large datacenters still remains unclear. This research introduces a hybrid surrogate model combining ANNs and CNNs for the precise and rapid prediction of 3D temperature distributions in large datacenters. The proposed method incorporates an ANN for feature processing and a CNN for decoding spatial features, leveraging both to capture complex airflow patterns and temperature distributions under varying conditions. A dataset of 500 CFD-simulated temperature fields based on a real datacenter is established for model training and validation. The CFD method is evaluated by comparing the simulation results with experimental data. Results of the ML models’ performance indicate that the proposed hybrid surrogate model outperforms the conventional ANN model, reducing mean absolute error (MAE) by 87.44%. Additionally, the model is 300,000 times faster than CFD simulations, offering an efficient solution for further supporting real-time thermal management.