7 The simulation enables the realistic reproduction of a process by means of physical or mathematical models, in which boundary conditions and reliable input parameters are defined.
15 to the Legislative Decree No 42 of 24 January 2004 (D. Lgs. 42/2004), i.e. they are historic buildings (De Santoli 2015; Filippi 2015; Mazzarella 2015). Since the Italian Legislative Decree No 192 of 19 August 2005 (D. Lgs. 192/2005) claims that in the case of historic buildings the conservation requirements have priority with respect to the energy retrofit, some studies were focused on the use of simulation as an effective tool to know in advance the impact of the refurbishment on the esthetical and architectural features of such buildings. Thus, several approaches were proposed with the aim to balance architectural conservation and energy efficiency (Ascione et al. 2015; Bellia et al. 2015;
De Berardinis et al. 2014; Cornaro et al. 2016; Franco et al. 2015; López and Frontini 2014;
Tronchin and Fabbri 2017). The attention paid to this topic is also revealed by the several projects funded by the European programmes in the last two decades. The Intelligent Energy Europe (IEE) programme funded New4Old – New energy for old buildings (2007-2010) for integrating renewable energy and energy efficiency technologies into historic buildings protecting their values, and SECHURBA – Sustainable Energy Communities in Historic Urban Areas (2008-2011) so to encourage energy efficiency practices and renewable energy systems in historical buildings. Within the Baltic Sea Region Programme (2007-2013), the Co2ol Bricks – Climate Change, Cultural Heritage & Energy Efficient Monuments (2010-2013) was funded with the purpose to reduce the energy consumption of historical buildings without destroying their cultural value and identity.
The 7th Framework Programme (EU FP7) funded 3ENcult – Energy Efficiency for EU cultural heritage (2010-2014) in order to use energy efficient retrofit for structural protection as well as for comfort and conservation requirements, and the EFFESUS – Energy Efficiency for EU Historic Districts´ Sustainability (2012-2016), with the aim of developing technologies and systems for energy efficiency in European historic urban districts.
Even though the whole building dynamic simulation is mainly used for the energy performance evaluation, it can be used as a diagnostic tool for achieving a comprehensive assessment of the current indoor climate (Janssen and Christensen 2013).
Another potential use is related to the assessment of the impact of climate change on the indoor climate, especially the global warming, as already demonstrated in the CIBSE TM 36:2005, in which a building of 19th century was simulated to suggest adaptation to avoid overheating. This is important since any change in the heat and moisture exchange between indoor and outdoor has a direct influence not only on the energy performance of the building but also on the conservation of artworks (Cassar and Pender 2003). Some relevant publications over the last 5 years demonstrated the potentiality of the dynamic simulation as a tool for conservation risk assessment (Huijbregts et al. 2012;
Kompatscher et al. 2017; Kramer et al. 2013; Kramer et al. 2015; Muñoz-González et al.
2016; Sciurpi et al. 2015; Schito and Testi 2017).
This issue was the main topic of the European project Climate for Culture (CfC – 2009-2014) funded within the 7th Framework Programme (EU FP7). The project was based on a multidisciplinary research team with the aim to identify the damage potential of the cultural heritage at risk and to encourage the development of strategies to mitigate the effects of climate change. This project, indeed, reflected the interest of scientific
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community to take advantages from the dynamic simulation (Leissner et al. 2013). It used high-resolution climate change evolution scenarios (derived by REMO simulations) with whole building simulation models to give the risk assessment on artworks at near future (2021–2050) and far future (2071–2100) with respect to the reference period (1961–1990). However, the prediction capability of such a method is particularly complex, since it should consider at least the uncertainty related to (1) the future outdoor climate, (2) the building model and (3) the damage functions (Leijonhufvud et al. 2012).
The uncertainty in the future outdoor climate depends on the climate models, that resolve in a simplified way all relevant processes.
The efficacy of the dynamic simulation strongly depends on the accuracy of the building model, that should be able to detect short- and long-term fluctuations of the indoor climate variables, especially the relative humidity (Bilchmair et al. 2012; Antretter et al. 2013; Kupczak et al. 2018). This variable is particularly complex to simulate, since many factors should be simultaneously considered. Most of simulation codes were developed to model moisture exchanges between indoor and outdoor environments setting a specific moisture storage capacity to the interior of the building (Holm et al.
2003) and not to model the moisture flow between the air and porous surfaces (Rode and Woloszyn 2007). For this reason, in the last 30 years, some dynamic simulation tools were developed to model moisture exchanges also through porous materials (Delgado et al.
2012), allowing to study issues related to uncontrolled condensation typical of old masonries (O’Leary et al. 2015). Furthermore, in the case of old buildings, the complexity in geometry and the heterogeneity in materials make extremely complicated and time consuming the model building setting (Coakley et al. 2014; Coelho et al. 2018). Thus, the calibration of the building model becomes of essential importance to solve such an issue.
As opposed to manual calibration, Caucheteux et al. (2013) and O’Neill and Eisenhower (2013) demonstrated the effectiveness of the semi-automatic calibration by means of the Sensitivity Analysis for identifying the most influential input parameters to be considered in order to minimise the discrepancy between modelled and measured energy data. Indeed, most of the calibration procedures is based on matching of energy data at different time scale (Ascione et al. 2011) or indoor air/surface temperature at hourly scale (Pernetti et al. 2013; Roberti et al. 2015) and few studies use relative humidity data.
The efficacy of damage functions relies on three kind of uncertainty: epistemic, aleatory and ambiguous (Refsgaard et al. 2013). The first depends on the input data and the lack of knowledge about processes, especially those related to the hygroscopic materials subject to the moisture content exchange. The second regards the randomness of mechanisms and the synergetic effects that cannot be included in the functions. The third, finally, relates to the interpretation of the output, that must be used as a relative predictor.
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