This paper investigates the influence of a stochastic variation of both energy and economic parameters in an optimization loop applied to a refurbished social housing building. Usually, energy and economic optimization procedures rely on the results of an underlying numerical deterministic model which influences both energy gains and economic figures. However, an analyst must always face the random variation of input and parameter data. The unknown data can represent poor initial information or data that can change in a long time; this is the case of fuel cost and economic indexes in particular. This paper deals with both problems for building refurbishment optimization, the former related to the initial state of a building, and the latter to the energy cost variability. Reliability analysis considers a stochastic variation of parameters looking for solutions that incorporate a risk level; in this case, it deals with optimization objectives related to different impacts on economic, environmental and health aspects. The considered building represents a social house, and the energy reduction measures involve the application of internal insulation layers to the walls and the replacement of existing windows with more efficient ones.

Because of the recent COVID-19 pandemic, the problem of preventing and containing the diffusion of pathogens spread through air has become a main topic of research. The problem is particularly important for specific environments, such as dental or other medical practices, where the aerosol treatments in open-mouth patients, combined with closed and crowded rooms, raise the risk of infection. As an efficient countermeasure, in this study we propose a solution that is able to remove the risk at the source, through the aspiration of the aerosol and the neutralization of the bacterial load by means of a UV-C LED filter, which releases the sterilized air in the environment. To maximize the efficiency of the solution, in this study we performed a numerical multi-disciplinary optimization (MDO) of the filter, coupling numerical simulations of multiple disciplines (CFD and electromagnetics) by the process automation and optimization environment modeFRONTIER of ESTECO. Geometrical parameters of the filter are updated for each candidate solution proposed by the optimization algorithm, and their performance in terms of viral neutralization efficiency and air mass flow rate are evaluated by the simulations, until the optimal solution is found. The methodology and results of the study are presented.