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dc.contributor.authorMorozova, Nina
dc.contributor.authorTrias Miquel, Francesc Xavier
dc.contributor.authorCapdevila Paramio, Roser
dc.contributor.authorSchillaci, Eugenio
dc.contributor.authorOliva Llena, Asensio
dc.contributor.otherUniversitat Politècnica de Catalunya. Doctorat en Enginyeria Tèrmica
dc.contributor.otherUniversitat Politècnica de Catalunya. Departament de Màquines i Motors Tèrmics
dc.date.accessioned2022-05-20T12:43:00Z
dc.date.issued2022-07-01
dc.identifier.citationMorozova, N. [et al.]. A CFD-based surrogate model for predicting flow parameters in a ventilated room using sensor readings. "Energy and buildings", 1 Juliol 2022, vol. 266, p. 112146:1-112146:15.
dc.identifier.issn0378-7788
dc.identifier.urihttp://hdl.handle.net/2117/367590
dc.description.abstractIn this work, we develop a computational fluid dynamics (CFD)-based surrogate model, which predicts flow parameters under different geometrical configurations and boundary conditions in a benchmark case of a mechanically ventilated room with mixed convection. The model inputs are the temperature and velocity values in different locations, which act as a surrogate of the sensor readings. The model’s output is a set of comfort-related flow parameters, such as the average Nusselt number on the hot wall, jet separation point, average kinetic energy, average enstrophy, and average temperature. We tested four different machine learning methods, among which we chose the gradient boosting regression due to its accurate performance. We also adapted the developed model for indoor environment control applications by determining the optimal combinations of sensor positions which minimize the prediction error. This model does not require the repetition of CFD simulations in order to be applied since the structure of the input data imitates sensor readings. Furthermore, the low computational cost of the model execution and good accuracy makes it an effective alternative to CFD for applications where rapid predictions of complex flow configurations are required, such as model predictive control.
dc.language.isoeng
dc.rightsAttribution-NonCommercial-NoDerivs 4.0
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/
dc.subjectÀrees temàtiques de la UPC::Enginyeria mecànica::Mecànica de fluids
dc.subject.lcshComputational fluid dynamics
dc.subject.lcshIndoor air pollution
dc.subject.lcshVentilation
dc.subject.otherComputational fluid dynamics
dc.subject.otherSurrogate models
dc.subject.otherIndoor airflow prediction
dc.subject.otherMachine learning
dc.subject.otherMixed convection
dc.titleA CFD-based surrogate model for predicting flow parameters in a ventilated room using sensor readings
dc.typeArticle
dc.subject.lemacDinàmica de fluids computacional
dc.subject.lemacContaminació de l'ambient interior
dc.subject.lemacVentilació
dc.contributor.groupUniversitat Politècnica de Catalunya. CTTC - Centre Tecnològic de la Transferència de Calor
dc.identifier.doi10.1016/j.enbuild.2022.112146
dc.description.peerreviewedPeer Reviewed
dc.relation.publisherversionhttps://www.sciencedirect.com/science/article/abs/pii/S0378778822003176
dc.rights.accessRestricted access - publisher's policy
local.identifier.drac33741492
dc.description.versionPostprint (author's final draft)
dc.relation.projectidinfo:eu-repo/grantAgreement/MICIN/2PE/PDC2021-120970-I00
dc.date.lift2024-06-01
local.citation.authorMorozova, N.; Trias, F. X.; Capdevila, R.; Schillaci, E.; Oliva, A.
local.citation.publicationNameEnergy and buildings
local.citation.volume266
local.citation.startingPage112146:1
local.citation.endingPage112146:15


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