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Detecting and modelling air flow overprovisioning / underprovisioning in air-cooled datacenters
University of Padova, Italy.
KTH Royal Institute of Technology, Sweden.
LuleƄ University of Technology, Sweden.
RISE - Research Institutes of Sweden, ICT, SICS.
Show others and affiliations
2018 (English)In: Proceedings: IECON 2018 - 44th Annual Conference of the IEEE Industrial Electronics Society, 2018, p. 4893-4900Conference paper, Published paper (Refereed)
Abstract [en]

When cooling and exhaust air flows in air-cooled datacenters mix, the energetic efficiency of the cooling operations drops. One way to prevent this mixing of happening is by augmenting the air tightness of the hot and cold aisles; this, however, requires installing opportune hardware that may be expensive and require time consuming installations. Alternatively, one may try to minimize cooling and exhaust air flows mixing by opportunely controlling the speeds of the fans of the Computer Room Air Handling (CRAH) units so that the distribution of the air pressure field within the computer room is favorable. Implementing this type of flow control requires both detecting when there actually is some type of flow mixing somewhere, plus understanding how to operate the cooling infrastructure so that these mixings do not happen. To this aim, there is the need for models that can both help deciding whether these mixing events occur, plus designing automatic control strategies for reducing the risks that they will happen. In this manuscript, we propose an ad-hoc methodology for the data-driven derivation of control-oriented models that serve the purposes above. The methodology is built on classical Prediction Error Method (PEM) approaches to the system identification problem, and on laddering on the peculiarities of the physics of the phenomena under consideration. Moreover, we test and assess the methodology on a industrial-scale air-cooled datacenter with an installed capacity of 240 kW, and verify that the obtained models are able to capture the dynamics of the system in all its potential regimes.

Place, publisher, year, edition, pages
2018. p. 4893-4900
Keywords [en]
Datacenters cooling, Energy efficiency, Statistical learning, Switching systems, Air, Automation, Cooling, Error analysis, Green computing, Industrial electronics, Mixing, Classical predictions, Control oriented models, Control strategies, Data centers, Energetic efficiency, Installed capacity, System identification problems, Cooling systems
National Category
Natural Sciences
Identifiers
URN: urn:nbn:se:ri:diva-38156DOI: 10.1109/IECON.2018.8592930Scopus ID: 2-s2.0-85061557374ISBN: 9781509066841 (print)OAI: oai:DiVA.org:ri-38156DiVA, id: diva2:1301545
Conference
44th Annual Conference of the IEEE Industrial Electronics Society, IECON 2018, 20 October 2018 through 23 October 2018
Available from: 2019-04-02 Created: 2019-04-02 Last updated: 2019-04-30Bibliographically approved

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CiteExportLink to record
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