With the rapid growth of AI and Big Data, reducing energy consumption in data centers has become a significant challenge. Small-scale server rooms, which often depend on standalone cooling units instead of specialized infrastructure, are especially vulnerable to airflow inefficiencies and localized hotspots. While traditional HVAC theory can estimate total cooling loads, it does not effectively predict the local thermal distributions that depend on equipment placement. This study employs Computational Fluid Dynamics (CFD) to address these limitations. It focuses on a server room containing five HPCs and three cooling units, comparing theoretical HVAC calculations with 3D thermal fluid analysis conducted using Ansys Fluent. The research evaluates the thermal performance of the existing layout (Model 1) and suggests an improved configuration (Model 2). The findings reveal that strategic equipment placement can eliminate hotspots and stabilize operations while reducing the necessary cooling capacity. This research provides a practical framework for enhancing energy efficiency in small-scale server environments.
A Transformer model to predict the remaining useful life of a fuel cell, which has demonstrated superior performance in analyzing time series data. The dataset was created from long-term performance evaluation experiments conducted in rated power mode, with measurements taken every 10 hours. We preprocessed the raw data using a moving average, allocating 70% for training and 30% for evaluation. The model's performance, evaluated through MAE, MSE, and MAPE, was excellent. The fuel cell's critical voltage, defined as 94.5% of its initial voltage, was measured at 0.719 V. During the experimental run, the actual critical time was 106.6 hours, while the model predicted 106.8 hours, resulting in a 0.19% error. Since the predictions were based on data collected up to 93 hours, the estimated remaining life was 13.8 hours.
In this study, the effects of repetition of assembly and disassembly of polymer electrolyte membrane fuel cells on electrochemical performance were systematically investigated. Additionally, the effects of additional activation on polymer electrolyte membrane fuel cells were evaluated. All fuel cells were measured every three days. For the disassembled polymer electrolyte membrane fuel cells, membrane electrode assemblies were stored in a vacuum desiccator. For the maintained assembly, fuel cells were stored at room temperature. The performance and electrochemical characteristics of the fuel cell were analyzed by electrochemical impedance spectroscopy. As a result, the addition of activation to maintained assembly fuel cells showed the best performance among fuel cells with other assembly and activation conditions. Repetition of assembly and disassembly, as well as insufficient activation, caused degradation of the performance of fuel cells.
Polymer electrolyte membrane fuel cells (PEMFC) require activation to maximize their performance. Thus, an appropriate activation process is essential for the performance of the fuel cell. In this study, the performance of the fuel cell was investigated by changing the voltage range during the activation process. There were three voltage ranges: 0.3-0.9 V, 0.3-0.6 V, and 0.6-0.9 V. When the fuel cell was activated in the low voltage region, the highest performance was output. On the other hand, it showed the lowest performance at high voltage. The results suggest that it is advantageous to activate the fuel cell with a high current. On the other hand, if activation is performed while outputting at a low current, the generation of water and the electrochemical reaction are insufficient, resulting in a load on the fuel cell. Through this experiment, it was confirmed that the control method greatly affects fuel cell performance when activated.
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