Hygrothermal performance of novel internal and external super-insulating systems: In-situ experimental study and 1D/2D numerical modeling

•We present in-situ real-scale hygrothermal assessment of internal and external aerogel-based composite insulation systems.•In-situ U value, thermal and moisture performance are examined.•Numerical models for heat and moisture transfer are developed and calibrated against experimental data.•The most influential parameters on the model calibration are determined. The growth in population, enhancement of building services and comfort levels, together with the rise in time spent inside buildings, have raised building energy consumption. The building sector offers significant potential for improved energy efficiency through the use of high-performance insulation. In these past years, a lot of research has been carried out on super insulating insulation, particularly silica-aerogels for building application. In this study, we present an in-situ experimental assessment and a numerical modelling analysis of newly developed silica-aerogel-based insulating composite systems. The aerogel blankets, which are the core component of these systems, are obtained thanks to an innovative ambient drying process. Based on these blankets, two systems were developed: one as an external insulation and one as an internal insulation. For the internal insulation composite system, a synthetic rubber layer is present representing an integrated vapour barrier. The results of the in-field hygrothermal testing during summer and winter seasons are presented and discussed. In addition, 1D and 2D numerical models for the heat and moisture transfer across these advanced envelopes are developed and calibrated, with the identification of the most influential parameters on the model calibration. It is concluded that the models constructed using lab-scale measured data can lead to significant errors in the results compared to the in-situ real performance, mainly for moisture transfer. A calibration step is necessary through parametric analysis. For better and easier calibration, the numerical models should be coupled to an optimization algorithm.