Modelon Launches Data Center Library to Help AI Infrastructure Teams Speed Up Cooling Design, Reduce Risk, and Improve Efficiency

As AI workloads drive denser racks and tougher cooling requirements, data center teams are under pressure to move faster without overbuilding infrastructure or increasing operating costs. Many still rely on spreadsheets, isolated analysis tools, or late-stage testing that cannot show how components of the cooling system behave together. Modelon’s Data Center Library addresses that gap by helping teams model the complete cooling chain—from plant to rack—and test how designs and control strategies perform under real operating conditions.

The result is faster engineering studies, confident design decisions, and clearer insight into energy use, water use, resilience, and total cost of ownership. Teams can compare architectures, evaluate setpoints, and validate control strategies before equipment is ordered or deployed, helping reduce delays, avoid overdesign, and accelerate time to delivery. Using Modelon’s system-level simulation approach, the Data Center Library supports the full data center lifecycle—from design and validation through commissioning and optimization. The same models can also serve as the foundation for digital twins that help operators monitor performance, test changes virtually, and optimize cooling efficiency in live facilities.

With the new library, organizations can:

• Compare air, liquid, hybrid, and two-phase cooling strategies before physical deployment.
• Reduce engineering cycle time with reusable models, reference system designs, and workflows instead of rebuilding each study from scratch.
• Improve energy and water efficiency by analyzing system behavior across loads and environmental conditions.
• Lower risk by validating equipment choices and control strategies earlier in the design process.

The Data Center Library includes configurable components for cooling plants, distribution systems, coolant distribution units (CDUs), computer room air handler (CRAH) units, chillers, cooling towers, racks, and supervisory controls, along with reference system designs and calibrated vendor components that help teams start studies faster and increase confidence in results.

Running in Modelon Impact’s cloud-based environment, the solution helps distributed teams collaborate in one workspace, run studies at scale, and move from concept to design faster. Modelon Impact also brings AI-assisted simulation to the Data Center Library. The AI Assistant is trained on the library and helps simulation experts move faster during design while making it easier for engineers to work with models during development and operations—from guided workflows in the interface to expert, agentic work at the code level.

For business leaders, that means shorter engineering schedules, fewer late changes, supplier coordination, and a faster path to efficient, scalable AI infrastructure.

The release also expands interoperability across the engineering ecosystem. FMI, the open standard maintained by the Modelica Association, is the native execution format in Modelon Impact. Models can be packaged as FMUs and used across 280+ simulation tools, helping customers protect model investments and collaborate across the supply chain. With NVIDIA joining the FMI Advisory Committee in 2024, this model-exchange approach is gaining momentum in data center cooling and digital twin workflows, positioning Modelon Impact well for emerging Omniverse-based environments.

“AI infrastructure is compressing engineering timelines while raising the cost of getting cooling decisions wrong,” said Modelon’s Chief Executive Officer Jan Häglund. “With the Data Center Library in Modelon Impact, our customers can evaluate options faster, improve design accuracy, and make smarter investment decisions earlier before delays and costs multiply.”