Data Center Design AI
AI for data center infrastructure and layout design.
Definition
Data Center Design AI assists in designing high-performance data centers. These systems understand power distribution, cooling requirements, rack layouts, and redundancy needs. AI can optimize floor layouts for airflow, size electrical and cooling systems, and ensure data center designs meet uptime and efficiency requirements.
In Depth
Data center design requires extreme precision in power distribution, cooling, and redundancy. AI optimizes the infrastructure design to achieve the target Tier rating (I-IV) while minimizing the capital and operating costs of the mechanical and electrical systems.
Power distribution design for data centers involves complex redundancy calculations — N+1, 2N, or 2(N+1) configurations for UPS systems, generators, and distribution paths. AI evaluates the redundancy architecture against the target availability, calculating the probability of downtime for different configurations and identifying the most cost-effective way to achieve the reliability target.
Cooling optimization is the other major AI application because cooling represents 30-40% of a data center's operating cost. AI evaluates different cooling strategies — raised floor vs. overhead distribution, hot aisle/cold aisle containment, liquid cooling for high-density racks, free cooling using economizers — and optimizes the system design for the facility's specific climate, IT load density, and operating conditions.
Examples
Optimizing rack layouts for cooling efficiency
Sizing power and cooling infrastructure
Analyzing redundancy configurations
Nomic Use Cases
See how Nomic applies this in production AEC workflows:
Frequently Asked Questions
Data Center Design AI assists in designing high-performance data centers. These systems understand power distribution, cooling requirements, rack layouts, and redundancy needs. AI can optimize floor layouts for airflow, size electrical and cooling systems, and ensure data center designs meet uptime and efficiency requirements.
Optimizing rack layouts for cooling efficiency. Sizing power and cooling infrastructure. Analyzing redundancy configurations.
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