1. ZHAW digitalcollection
  2. School of Engineering
  3. Publikationen
  4. Publikationen School of Engineering
Full metadata record
DC FieldValueLanguage
dc.contributor.authorDe Lorenzi, Flavio-
dc.contributor.authorVömel, Christof-
dc.date.accessioned2018-11-30T13:55:08Z-
dc.date.available2018-11-30T13:55:08Z-
dc.date.issued2012-
dc.identifier.issn1948-5085de_CH
dc.identifier.issn1948-5093de_CH
dc.identifier.urihttps://digitalcollection.zhaw.ch/handle/11475/13434-
dc.description.abstractAs modern data centers continue to grow in power, size, and numbers, there is an urgent need to reduce energy consumption by optimized cooling strategies. In this paper, we present a neural network-based prediction of air flow in a data center that is cooled through perforated floor tiles. With a significantly smaller execution time than computational fluid dynamics, it predicts in real-time server inlet temperatures and can detect whether prevalent air flow cools the servers sufficiently to guarantee safe operation. Combined with a cooling system model, we obtain a temperature and air flow control algorithm that is fast and accurate enough to find an optimal operating point of the data center cooling system in real-time. We also demonstrate the performance of our algorithm on a reference data center and show that energy consumption can be reduced by up to 30%.de_CH
dc.language.isoende_CH
dc.publisherThe American Society of Mechanical Engineersde_CH
dc.relation.ispartofJournal of Thermal Science and Engineering Applicationsde_CH
dc.rightsLicence according to publishing contractde_CH
dc.subject.ddc690: Hausbau und Bauhandwerkde_CH
dc.titleNeural network-based prediction and control of air flow in a data centerde_CH
dc.typeBeitrag in wissenschaftlicher Zeitschriftde_CH
dcterms.typeTextde_CH
zhaw.departementSchool of Engineeringde_CH
zhaw.organisationalunitInstitut für Angewandte Mathematik und Physik (IAMP)de_CH
dc.identifier.doi10.1115/1.4005605de_CH
zhaw.funding.euNode_CH
zhaw.issue2de_CH
zhaw.originated.zhawYesde_CH
zhaw.pages.start021005de_CH
zhaw.publication.statuspublishedVersionde_CH
zhaw.volume4de_CH
zhaw.publication.reviewPeer review (Publikation)de_CH
Appears in collections:Publikationen School of Engineering

Files in This Item:
There are no files associated with this item.
Show simple item record
De Lorenzi, F., & Vömel, C. (2012). Neural network-based prediction and control of air flow in a data center. Journal of Thermal Science and Engineering Applications, 4(2), 21005. https://doi.org/10.1115/1.4005605
De Lorenzi, F. and Vömel, C. (2012) ‘Neural network-based prediction and control of air flow in a data center’, Journal of Thermal Science and Engineering Applications, 4(2), p. 021005. Available at: https://doi.org/10.1115/1.4005605.
F. De Lorenzi and C. Vömel, “Neural network-based prediction and control of air flow in a data center,” Journal of Thermal Science and Engineering Applications, vol. 4, no. 2, p. 021005, 2012, doi: 10.1115/1.4005605.
DE LORENZI, Flavio und Christof VÖMEL, 2012. Neural network-based prediction and control of air flow in a data center. Journal of Thermal Science and Engineering Applications. 2012. Bd. 4, Nr. 2, S. 021005. DOI 10.1115/1.4005605
De Lorenzi, Flavio, and Christof Vömel. 2012. “Neural Network-Based Prediction and Control of Air Flow in a Data Center.” Journal of Thermal Science and Engineering Applications 4 (2): 21005. https://doi.org/10.1115/1.4005605.
De Lorenzi, Flavio, and Christof Vömel. “Neural Network-Based Prediction and Control of Air Flow in a Data Center.” Journal of Thermal Science and Engineering Applications, vol. 4, no. 2, 2012, p. 21005, https://doi.org/10.1115/1.4005605.


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.