The reliability of hygrothermal simulations of building components is key for designing energy efficiency measures, assessing living comfort, and preventing building damage. The model accuracy is related to the reliability of the selection of input parameters. Due to the high uncertainty, the selection of the input values is challenging. This work aims to calibrate a hygrothermal simulation model exploiting monitored values recorded in a case study located in Settequerce (Italy), to understand how close to reality a numerical model can be. Moreover, a sensitivity analysis, based on the Morris method together with a Latin Hypercube sampling, is applied to identify the input parameters that affect most significantly the simulation. The results of the calibration indicated that is possible to obtain reliable outputs by appropriately selecting materials within the database. The sensitivity analysis showed that the relative humidity under the insulation is largely influenced by the water vapor diffusion resistance factor of the plaster, applied during the renovation phase both on the internal and external side. Among the coefficients describing the coupling with the boundary conditions, only the external convective heat coefficient and the coefficient of short-wave solar radiation influence slightly the objective function.
mehrTitel | Identifying key parameters through a sensitivity analysis for realistic hygrothermal simulations at wall level supported by monitored data |
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Medien | Building and Environment, 229, 109969 |
Verlag | Elsevier |
Heft | --- |
Band | 2023 |
ISBN | --- |
Verfasser/Herausgeber | Simone Panico, Marco Larcher, Valentina Marincioni, Prof. Dr. Alexandra Troi, Cristina Baglivo, Maria Paolo Congedo |
Seiten | --- |
Veröffentlichungsdatum | 01.02.2023 |
Projekttitel | --- |
Zitation | Panico, Simone; Larcher, Marco; Marincioni, Valentina; Troi, Alexandra; Baglivo, Cristina; Congedo, Maria Paolo (2023): Identifying key parameters through a sensitivity analysis for realistic hygrothermal simulations at wall level supported by monitored data. Building and Environment, 229, 109969 2023, 109969. DOI: 10.3390/buildings12081258 |