The Data Center Library for Modelon Impact
The Data Center Library (DCL) offers physics-based simulation components for data center cooling. Exclusive to Modelon Impact and built on open Modelica standards, it is used for everything from single-component sizing to full MW-scale facility models with PUE monitoring.
One Connected System
AI data centers push cooling systems into a regime of high-stakes decisions on topology, control strategy, and capacity. DCL models the full cooling chain—thermal, hydraulic, and control—as one connected system, giving teams system-level visibility into how a change to chiller staging or CDU sizing plays out at the rack, early in the design process.
Facility to Chip, End to End
The Data Center Library covers the complete cooling chain end to end: facility-side infrastructure (chillers, cooling towers, pumps, waterside economizers) through coolant distribution (CDUs, CRAHs), the rack and chip level (single- and two-phase cold plates), and the supervisory control logic tying it all together. Both hybrid and fully liquid-cooled architectures are supported, matching how real deployments look, where equipment like memory and networking still needs air cooling.
Validated Hardware
Pre-calibrated CDU and CRAH vendor models simulate exact hardware from leading manufacturers at a facility’s own water temperatures, parameterized from standard ASHRAE W-class datasheet points, so teams can compare vendors for liquid, air-cooled, and hybrid systems before buying. An experimentally verified two-phase cold plate and rack model supports component-level studies of vapor quality against IT load.
Open by Design
DCL is built on the Liquid Cooling Library (LCL) and is compatible with the Vapor Cycle Library (VCL) for chiller modeling. Open Modelica foundations give teams transparency and extensibility as their systems and requirements evolve. It is a product of Modelon, developed exclusively for the Modelon Impact software platform.
Example: Reference Models
The library includes system-level reference models at 1 MW and 7 MW scale, including a hybrid-cooled 1 MW model based on the Schneider Electric EcoStruxure Modular Data Centre AI Reference Design. These examples demonstrate how empirical performance data can be integrated into a comprehensive data center cooling system model to support design analysis, optimization, and control strategy evaluation.
APPLICATION
Data Center Cooling System Design
This library supports end-to-end system design and virtual prototyping for hyperscale, colocation, and enterprise facilities. Components are rapidly assembled to evaluate alternatives, including standard chiller plants, chillers with waterside economizers, multi-chiller configurations, and hybrid rack arrangements. Teams can test different topologies and site locations before committing to hardware.
Component and Hardware Model Validation
Cooling equipment manufacturers can calibrate performance models to their own CDU, CRAH, or cold plate test data, including next-generation two-phase components, so their hardware can be evaluated under a customer’s exact facility conditions with verified performance rather than datasheet claims alone.
CDU and CRAH Vendor Evaluation and Procurement Support
Committing to expensive CDU or CRAH hardware without understanding how it will perform in a specific facility is a key procurement risk. The library’s pre-calibrated vendor models records allow engineering teams to simulate exact hardware under actual facility water conditions and compare options side by side before purchase. This approach provides quantitative performance insights that test rigs cannot deliver prior to procurement.
AI Cluster Cooling and Capacity Planning
Evaluate cooling capacity, supply temperature setpoints, and headroom for GPU-dense workloads, starting from the included 1 MW liquid-cooled AI cluster reference model. Use offline studies to assess how new racks, changing IT loads, or evolving cooling strategies affect an existing plant’s behavior and efficiency before making physical changes.
Control Strategy, Setpoint Optimization, and Sustainability
The library models supervisory plant controls, including chiller staging, waterside economizer mode switching, and CWST-based setpoint logic, within the same environment used for system design. Teams can evaluate free-cooling utilization, energy efficiency, and control behavior under varying IT loads and ambient conditions before deployment or during operational optimization. This provides a quantitative basis for achieving both performance and sustainability goals.
