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arXiv Machine Learning By Yun-Dam Ko, Dipashreya A. Sur, Cade Napier, Carlos D. D\'iaz-Mar\'in, Rishee K. Jain

Modeling Liquid-Cooled Data Centers in EnergyPlus

Read the original on arXiv Machine Learning →

The paper introduces a new EnergyPlus object, ElectricEquipment:ITE:LiquidCooled, that models direct-to-chip liquid cooling for both chilled‑water and warm‑water data center architectures. Validation against a Modelica implementation shows temperature errors below 0.021 K across 21 operating conditions, meeting the 0.1 K threshold. A case study indicates that warm‑water cooling can lower annual PUE from 1.43 to 1.25, eliminate on‑site water use, and cut total facility energy by 37.9% compared to an air‑cooled baseline.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

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