eprintid: 2263
rev_number: 38
eprint_status: archive
userid: 124
dir: disk0/00/00/22/63
datestamp: 2007-01-03 12:00:00
lastmod: 2022-01-17 00:04:18
status_changed: 2008-01-09 13:36:59
type: article
metadata_visibility: show
item_issues_count: 0
creators_name: Oreszczyn, T
creators_name: Pretlove, SEC
title: Condensation Targeter II: Modelling surface relative humidity to predict mould growth in dwellings
ispublished: pub
subjects: 2900
divisions: UCL
divisions: B04
divisions: C04
divisions: F34
abstract: Condensation and mould growth are reported as being problems in an estimated 4.2 million dwellings in England, resulting in poor health for the occupants and substantial damage to the fabric of the building. This paper examines the development of an algorithm, Condensation Targeter, to predict the relative humidity of internal surfaces and risk of mould growth in dwellings. The impacts of cold bridging, seasonal variations, variable moisture production and hygroscopic materials are reviewed (but not of interstitial condensation) and a comparison between modelled and measured data for 36 dwellings is carried out. Results indicate that a steady-state model utilizing Bredem-8 to predict internal temperatures and Loudon's condensation model to predict moisture shows good (±10%) agreement with monitored data. A model sensitivity study shows that variations in occupant heating and density can be as important as, or even more important than, ventilation in determining mould.
date: 1999-01-01
date_type: published
oa_status: green
primo: open
primo_central: open_green
article_type_text: Journal Article
elements_source: Manually entered
elements_id: 79029
doi: 10.1177/014362449902000307
lyricists_name: Oreszczyn, Tadeusz
lyricists_id: TORES56
full_text_status: public
publication: Building Services Engineering Research and Technology
volume: 20
number: 3
pagerange: 143-153
refereed: TRUE
issn: 0143-6244
citation:        Oreszczyn, T;    Pretlove, SEC;      (1999)    Condensation Targeter II: Modelling surface relative humidity to predict mould growth in dwellings.                   Building Services Engineering Research and Technology , 20  (3)   pp. 143-153.    10.1177/014362449902000307 <https://doi.org/10.1177/014362449902000307>.       Green open access   
 
document_url: https://discovery-pp.ucl.ac.uk/id/eprint/2263/1/2263.pdf