Development of a computational tool to quantify architectural-design effects on thermal comfort in naturally ventilated rural houses

Stavrakakis, GM; Zervas, PL; Sarimveis, H; Markatos, NC

dc.contributor.author	Stavrakakis, GM	en
dc.contributor.author	Zervas, PL	en
dc.contributor.author	Sarimveis, H	en
dc.contributor.author	Markatos, NC	en
dc.date.accessioned	2014-03-01T01:33:09Z
dc.date.available	2014-03-01T01:33:09Z
dc.date.issued	2010	en
dc.identifier.issn	0360-1323	en
dc.identifier.uri	https://dspace.lib.ntua.gr/xmlui/handle/123456789/20345
dc.subject	Architectural design	en
dc.subject	Artificial Neural Networks	en
dc.subject	Computational fluid dynamics	en
dc.subject	Meta-modelling	en
dc.subject	Natural ventilation	en
dc.subject	Thermal comfort	en
dc.subject.classification	Construction & Building Technology	en
dc.subject.classification	Engineering, Environmental	en
dc.subject.classification	Engineering, Civil	en
dc.subject.other	Airflow patterns	en
dc.subject.other	Artificial Neural Network	en
dc.subject.other	Artificial Neural Networks	en
dc.subject.other	CFD models	en
dc.subject.other	Computational tools	en
dc.subject.other	Design effects	en
dc.subject.other	Indoor thermal comfort	en
dc.subject.other	Inlet boundary	en
dc.subject.other	Input variables	en
dc.subject.other	Input-output	en
dc.subject.other	Local climate	en
dc.subject.other	Mean values	en
dc.subject.other	Meta model	en
dc.subject.other	Meta-modelling	en
dc.subject.other	Natural ventilation	en
dc.subject.other	Opening sizes	en
dc.subject.other	Optimum designs	en
dc.subject.other	Output variables	en
dc.subject.other	Predicted mean vote	en
dc.subject.other	Radial basis functions	en
dc.subject.other	Temperature and relative humidity	en
dc.subject.other	Thermal comfort index	en
dc.subject.other	Weather stations	en
dc.subject.other	Wind velocity and direction	en
dc.subject.other	Architectural design	en
dc.subject.other	Atmospheric humidity	en
dc.subject.other	Backpropagation	en
dc.subject.other	Fluid dynamics	en
dc.subject.other	Neural networks	en
dc.subject.other	Radial basis function networks	en
dc.subject.other	Thermal comfort	en
dc.subject.other	Ventilation	en
dc.subject.other	Computational fluid dynamics	en
dc.subject.other	air temperature	en
dc.subject.other	airflow	en
dc.subject.other	architectural design	en
dc.subject.other	artificial neural network	en
dc.subject.other	computational fluid dynamics	en
dc.subject.other	modeling	en
dc.subject.other	relative humidity	en
dc.subject.other	rural area	en
dc.subject.other	ventilation	en
dc.title	Development of a computational tool to quantify architectural-design effects on thermal comfort in naturally ventilated rural houses	en
heal.type	journalArticle	en
heal.identifier.primary	10.1016/j.buildenv.2009.05.006	en
heal.identifier.secondary	http://dx.doi.org/10.1016/j.buildenv.2009.05.006	en
heal.language	English	en
heal.publicationDate	2010	en
heal.abstract	In the present study, the effect of the opening size and building direction on night hours thermal comfort in a naturally ventilated rural house is investigated. Initially, the airflow in and around the building is simulated using a validated computational fluid dynamics (CFD) model. Local climate night-time data (wind velocity and direction, temperature and relative humidity) are recorded in a weather station and the prevailing conditions are imposed in the CFD model as inlet boundary conditions. The produced airflow patterns are then used to evaluate indoor thermal comfort. For this reason, special thermal comfort indices, i.e. the well-known predicted mean vote (PMV) index and its modifications especially for natural ventilation, are calculated with respect to various residential activities. Mean values of these indices (output variables) within the occupied zone are calculated for different combinations of opening sizes and building directions (input variables), to generate a database of input-output pairs. Finally, the database is used to train and validate Radial Basis Function Artificial Neural Network (RBF ANN) input-output ""meta-models"". It is demonstrated that the proposed methodology leads to reliable thermal comfort predictions, while the optimum design variables are easily recognized. © 2009 Elsevier Ltd. All rights reserved.	en
heal.publisher	PERGAMON-ELSEVIER SCIENCE LTD	en
heal.journalName	Building and Environment	en
dc.identifier.doi	10.1016/j.buildenv.2009.05.006	en
dc.identifier.isi	ISI:000271350500012	en
dc.identifier.volume	45	en
dc.identifier.issue	1	en
dc.identifier.spage	65	en
dc.identifier.epage	80	en