Power Density and Thermal Design Load

Rack power density is the primary parameter for cooling selection. Traditional office-type data centers typically operate between 3–8 kW per rack, while AI or HPC centers often exceed 30 kW per rack. Air cooling efficiency drops sharply above 20 kW, making in-row or liquid cooling systems necessary.
Accurately forecasting power growth for the next three years can help avoid costly redesigns later.

Building Structure and Airflow Management

Many facilities overlook airflow direction and spatial constraints during early design. For example, low ceiling height or insufficient raised-floor pressure can greatly reduce air delivery efficiency.
If using in-row or rack-level cooling, consider supply and return air paths, airflow containment, and cold-aisle enclosure. For high-density areas, CFD (Computational Fluid Dynamics) simulations are highly recommended to validate design effectiveness.

Energy Efficiency and Lifecycle Cost

Cooling systems are not just a one-time investment. Efficiency, compressor load control, and free-cooling capability all determine long-term energy use.
In large data centers, cooling systems can account for up to 40% of total O&M electricity costs. Through intelligent control strategies such as variable-speed compressors and dynamic load management, annual energy consumption can be reduced by 15–20%.

Redundancy and Reliability

For Tier III or Tier IV data centers, cooling systems must include N+1 or 2N redundancy. During design, consider backup strategies for chillers, water loops, cooling towers, and pumps to ensure that no single failure disrupts operation.

Maintenance Accessibility

Maintenance challenges often arise in “blind spots” — tight equipment spacing, dense piping, or limited overhead clearance. Good maintainability minimizes downtime and reduces operational costs.
Engineers typically require front-access or hot-swappable components to enable maintenance without interrupting cooling operations.

Geographic and Climate Conditions

Regional climate directly affects cooling choices.

• Northern regions can benefit from natural (free) cooling.

• Southern humid areas must emphasize condensation control and dehumidification.

• High-altitude or extreme environments favor liquid cooling due to its stability and minimal dependency on ambient temperature.

Scalability and Future Expansion

Many modern projects adopt modular cooling architectures, allowing capacity to scale by simply adding modules instead of redesigning the entire system. This “progressive build” approach is especially valuable for AI computing centers.

Define the business scenario and workload type

Identify whether the facility serves enterprise IT, cloud computing, or AI/HPC clusters — each has distinct thermal and cooling requirements.

Assess existing infrastructure and building conditions

Evaluate room height, floor loading, power capacity, and outdoor cooling resources. For retrofit projects, check system compatibility.

Conduct thermal design and simulation

Use CFD modeling to identify hot spots, low-velocity zones, or short-circuit airflow paths to guide system selection.

Perform energy and economic analysis

Apply LCC (Life-Cycle Cost) analysis to compare long-term value. A system that costs 10% more upfront may recover that through energy savings within three years.

Design redundancy and control systems

Ensure every cooling unit has a backup path. Intelligent control systems should support multi-unit coordination, automatic failover, and energy balancing.

Integrate monitoring and automation

Before commissioning, connect the system to a DCIM or BMS platform for real-time monitoring of temperature, humidity, and power usage. Early detection of anomalies helps prevent downtime.