Computational Engineering based approach on Artificial Intelligence and Machine learning-Driven Robust Data Centre for Safe Management

Abstract

This research explores the integration of Artificial Intelligence (AI), specifically the Recurrent Neural Network (RNN) model, into the optimization of data center cooling systems through Computational Engineering. Utilizing Computational Fluid Dynamics (CFD) simulations as a foundational data source, the study aimed to enhance operational efficiency and sustainability in data centers through predictive modeling. The findings revealed that the RNN model, trained on CFD datasets, proficiently forecasted key data center conditions, including temperature variations and airflow dynamics. This AI-driven approach demonstrated marked advantages over traditional methods, significantly minimizing energy wastage commonly incurred through overcooling. Additionally, the proactive nature of the model allowed for the timely identification and mitigation of potential equipment challenges or heat hotspots, ensuring uninterrupted operations and equipment longevity. While the research showcased the transformative potential of merging AI with data center operations, it also indicated areas for further refinement, including the model's adaptability to diverse real-world scenarios and its management of long-term dependencies. In conclusion, the study illuminates a promising avenue for enhancing data center operations, highlighting the significant benefits of an AI-driven approach in achieving efficiency, cost reduction, and environmental sustainability.