Modelon Expands Data Center Library for AI Cooling
Artificial intelligence (AI) workloads are reshaping data center design. Higher rack power densities, new cooling architectures, and increasing efficiency demands are driving the need for better ways to evaluate performance long before systems are deployed.
To help engineering teams navigate this transition, Modelon has expanded its Data Center Library for Modelon Impact with new capabilities for simulating advanced cooling systems, including two-phase direct-to-chip cooling. Developed in collaboration with the University of Maryland, the enhanced library enables users to evaluate cooling strategies, compare design alternatives, and better understand system performance across a wide range of operating conditions.
New Capabilities for Liquid Cooling and Two-Phase Cooling Analysis
- Support for evaluating emerging cooling technologies, including two-phase direct-to-chip cooling
- Dynamic reference scenarios that account for changing weather conditions and IT workloads
- Additional pre-calibrated equipment models and streamlined workflows
- A system-level approach that connects facility, rack, and chip-level cooling infrastructure
Reducing Risk Across the Data Center Lifecycle
Data center teams need to balance performance, reliability, energy efficiency, and cost while managing growing cooling complexity. Simulation helps engineers make informed decisions earlier, reduce development risk, and optimize designs before equipment is installed.
The Data Center Library supports these efforts across the data center lifecycle, from early architecture studies and equipment selection through commissioning, operational optimization, and future expansion planning.
Read the full press release to learn how Modelon is helping engineering teams evaluate next-generation cooling technologies with greater confidence.
