Year: 2010
Type: conference-paper
Abstract: Modelling heat, air and moisture transport in buildings accurately is of great importance when studying not only energy efficiency (for example heat losses and gains through the building envelope) but also damage mechanism (due to for example moisture accumulation or interstitial condensation). This is why a lot of effort has been put into the development of HAM models (Heat, Air and Moisture). Nevertheless these models still show some shortcomings. One of the mayor defects of classic HAM models is the poor air flow modelling. Often these models use transfer coefficients to model transport from air to porous material. To overcome this shortcoming, a coupled CFD-HAM approach is proposed. CFD (Computational Fluid Dynamics) is currently used to study air flow in and around buildings. By combining CFD with HAM, air flow around a porous material is simulated together with heat and moisture transport in the air and the porous material. This paper shortly discusses a newly developed coupled CFD-HAM model. More specifically the sensitivity of the model to the necessary input data is investigated. Recent studies have shown that measured material property data can differ a lot when measured by different laboratories. Especially hygrothermal material properties like sorption isotherm and vapour permeability show large discrepancies. The objective of this paper is to investigate how changes in material properties affect the model outcome. Also the difference between modelling a building material as homogeneous or made up out of layers is studied. From this sensitivity analysis it can be concluded that not all material properties are equally important. Variations in density, heat capacity and heat conductivity have almost no effect on the model. Hygrothermal properties like sorption isotherm and permeability on the other hand have a more severe impact, although this impact can still be considered acceptable in some cases. Nevertheless a high uncertainty on measured material properties combined with a high sensitivity for these parameters increase the uncertainty of the model outcome.
Language: English
Subfield: Building and Construction
Field: Engineering
Domain: Physical Sciences
SDG: Climate action
Open Access status: green
Funder: Vlaamse regering
OpenAlex ID: W2248435204