Selection of window sizes for optimizing occupational comfort and hygiene based on computational fluid dynamics and neural networks

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

dc.contributor.author	Stavrakakis, GM	en
dc.contributor.author	Karadimou, DP	en
dc.contributor.author	Zervas, PL	en
dc.contributor.author	Sarimveis, H	en
dc.contributor.author	Markatos, NC	en
dc.date.accessioned	2014-03-01T01:37:03Z
dc.date.available	2014-03-01T01:37:03Z
dc.date.issued	2011	en
dc.identifier.issn	0360-1323	en
dc.identifier.uri	https://dspace.lib.ntua.gr/xmlui/handle/123456789/21432
dc.subject	Artificial neural networks	en
dc.subject	Computational fluid dynamics	en
dc.subject	Indoor air quality	en
dc.subject	Thermal comfort	en
dc.subject	Window sizes optimization	en
dc.subject.classification	Construction & Building Technology	en
dc.subject.classification	Engineering, Environmental	en
dc.subject.classification	Engineering, Civil	en
dc.subject.other	Air-conditioned buildings	en
dc.subject.other	Airflow patterns	en
dc.subject.other	Artificial Neural Network	en
dc.subject.other	CFD models	en
dc.subject.other	Computational fluid	en
dc.subject.other	Computational fluid dynamics models	en
dc.subject.other	Design guidelines	en
dc.subject.other	Indoor air quality	en
dc.subject.other	Indoor environment	en
dc.subject.other	Input-output data	en
dc.subject.other	Mean values	en
dc.subject.other	Meta model	en
dc.subject.other	Naturally ventilated buildings	en
dc.subject.other	Optimization problems	en
dc.subject.other	Radial basis functions	en
dc.subject.other	Thermal comfort index	en
dc.subject.other	Ventilation effectiveness	en
dc.subject.other	Weather conditions	en
dc.subject.other	Window Size	en
dc.subject.other	Window sizes optimization	en
dc.subject.other	Air	en
dc.subject.other	Air quality	en
dc.subject.other	Architectural design	en
dc.subject.other	Carbon dioxide	en
dc.subject.other	Computational fluid dynamics	en
dc.subject.other	Computer simulation	en
dc.subject.other	Fluid dynamics	en
dc.subject.other	Indoor air pollution	en
dc.subject.other	Optimization	en
dc.subject.other	Radial basis function networks	en
dc.subject.other	Thermal comfort	en
dc.subject.other	Ventilation	en
dc.subject.other	Volatile organic compounds	en
dc.subject.other	Neural networks	en
dc.subject.other	air quality	en
dc.subject.other	airflow	en
dc.subject.other	artificial neural network	en
dc.subject.other	boundary condition	en
dc.subject.other	building	en
dc.subject.other	carbon dioxide	en
dc.subject.other	computational fluid dynamics	en
dc.subject.other	database	en
dc.subject.other	flow pattern	en
dc.subject.other	indoor air	en
dc.subject.other	optimization	en
dc.subject.other	ventilation	en
dc.subject.other	volatile organic compound	en
dc.subject.other	Papaya mosaic virus	en
dc.title	Selection of window sizes for optimizing occupational comfort and hygiene based on computational fluid dynamics and neural networks	en
heal.type	journalArticle	en
heal.identifier.primary	10.1016/j.buildenv.2010.07.021	en
heal.identifier.secondary	http://dx.doi.org/10.1016/j.buildenv.2010.07.021	en
heal.language	English	en
heal.publicationDate	2011	en
heal.abstract	The present paper presents a novel computational method to optimize window sizes for thermal comfort and indoor air quality in naturally ventilated buildings. The methodology is demonstrated by means of a prototype case, which corresponds to a single-sided naturally ventilated apartment. Initially, the airflow in and around the building is simulated using a Computational Fluid Dynamics model. Local prevailing weather conditions are imposed in the CFD model as inlet boundary conditions. The produced airflow patterns are utilized to predict thermal comfort indices, i.e. the PMV and its modifications for non-air-conditioned buildings, as well as indoor air quality indices, such as ventilation effectiveness based on carbon dioxide and volatile organic compounds removal. Mean values of these indices (output/objective variables) within the occupied zone are calculated for different window sizes (input/design variables), to generate a database of input output data pairs. The database is then used to train and validate Radial Basis Function Artificial Neural Network input-output "meta-models". The produced meta-models are used to formulate an optimization problem, which takes into account special constraints recommended by design guidelines. It is concluded that the proposed methodology determines appropriate windows architectural designs for pleasant and healthy indoor environments. (C) 2010 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.2010.07.021	en
dc.identifier.isi	ISI:000284348400002	en
dc.identifier.volume	46	en
dc.identifier.issue	2	en
dc.identifier.spage	298	en
dc.identifier.epage	314	en