Generative reverse-modelling approach to hygrothermal material characterization

Paul Klõšeiko,Peggy Freudenberg,R Černý,J Kočí,V Kočí

doi:10.1051/matecconf/201928202088

Paul Klõšeiko, Peggy Freudenberg + Show 3 more

Open Access

https://doi.org/10.1051/matecconf/201928202088

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Abstract

Reliable hygrothermal modelling depends on the quality of material characterization, especially so when higher moisture contents are concerned. Previous research has shown that adding additional material tests (e.g. capillary condensation redistribution (CCR) test) to the experimental dataset brings improvements to the modelling accuracy, but also adds to the workload of characterization process. This paper discusses a generative optimization workflow to increase the speed of the characterization and quality of the result. The proposed workflow incorporates optimization tool GenOpt and hygrothermal modelling software IBK Delphin to search for best fit of the water vapour and liquid conductivity curves of interior insulation materials based on modelling the CCR, drying and wet cup tests. Finally, models using material data from the proposed workflow and from the software database are compared to measurement results from two studies on interior thermal insulation. The results suggest that the generative optimization shows promise on the grounds of reducing tedious work analysing material tests. Also, a wider experimental dataset is shown to be useful when characterizing the vapour and liquid conductivity functions in over-hygroscopic region.

Highlights

Interest in interior insulation has grown year-by-year in the Nordic countries as the need to combat high heating costs and mould risk in historic buildings increases
The proposed workflow incorporates optimization tool GenOpt and hygrothermal modelling software IBK Delphin to search for best fit of the water vapour and liquid conductivity curves of interior insulation materials based on modelling the capillary condensation redistribution (CCR), drying and wet cup tests
The results suggest that the generative optimization shows promise on the grounds of reducing tedious work analysing material tests

Summary

Introduction

Interest in interior insulation has grown year-by-year in the Nordic countries as the need to combat high heating costs and mould risk in historic buildings increases. Hygrothermal modelling tools and material data have shown good correlation in this range and in experienced hands, the results can be considered reliable. These limit values restrict the thickness of interior insulation that can be applied, especially so in colder climates on walls with high thermal transmittance and where the need for thicker insulation is more pressing. There, a condensation plane inside a material sample is caused by temperature difference while mass change and moisture profile are monitored

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