Thermal Environment Assessment for Energy-Efficient Cooling in Air-Cooled Data Centers: Measurement, CFD, and Predictive Modeling

Maintaining a stable thermal environment is essential for ensuring data center thermal safety and reliable operation. However, existing studies lack a systematic synthesis of thermal environment measurement, modeling approaches, and their contributions to energy-efficient cooling. This review systematically analyzes 77 studies published since 2010 on experimental measurement, CFD simulation, and predictive modeling of data center thermal environments. The review identifies representative measurement strategies at rack and room scales, analyzes factors affecting CFD reliability, and evaluates the applicability of predictive models based on experimental and simulation data. Most experimental studies focus on temperature and airflow, while only about 30.7% of the 39 experimental studies include power measurements, limiting quantitative evidence linking thermal optimization with energy performance. Among the 58 CFD studies, Fluent was used in 41.4% of cases, while 70.6% of the 17 predictive-model studies relied on CFD data for model training. CFD reliability remained sensitive to boundary conditions, model simplifications, and validation strategies. Predictive modeling was dominated by machine learning and deep learning, while reduced-order and surrogate models were also applied, enabling rapid temperature-field and thermal-risk assessment but remaining constrained by training data quality and operating condition coverage. These findings establish an integrated framework in which measurements provide empirical benchmarks, CFD extends spatial and operational understanding, and predictive models enable rapid assessment for thermal environment management and cooling energy optimization.