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The emergence of building performance optimization is recognized as a way to achieve sustainable building designs. In this paper, the problem consists in minimizing simultaneously the emissions of greenhouse gases (GHG) related to building energy consumption and those related to building materials. This multi-objective optimization problem involves variables with different hierarchical levels, i.e. variables that can become obsolete depending on the value of the other variables. To solve it, NSGA-II is compared with an algorithm designed specifically to deal with hierarchical variables, namely sNSGA.Evaluation metrics such as convergence, diversity and hypervolume show that both algorithms handle hierarchical variables, but the analysis of the Pareto front confirms that in the present case, NSGA-II is better to identify optimal solutions than sNSGA. All the optimal solutions are made of buildings with wooden envelopes and relied either on heat pumps or on electrical heaters for proving heating.

In many industrial multi-physics engineering applications, models need to capture the heat transfer effects of spatial and temporal changes in conditions around the human body. For thermal comfort assessment, convection heat transfer coefficients (hc) form part of the heat balance equation of the human body. In many non-uniform flow conditions, due to the turbulently mixed or stratified environment, convection heat transfer varies significantly on the human body. Parametric, segment-wise applicable convection heat transfer correlations are seen as an alternative in order to bridge these scales and levels in space and time. Therefore, robust reduced-order convective heat transfer models are needed for predicting heat transfer between the human body and its surroundings. The main goal of this research is to develop a reduced order model database that provides the segment-wise convective heat transfer coefficients (hc) for typical indoor flow responses in multiple applicat ions. In this article, a new parametric approach was detailed for estimating segment-wise body convection heat transfer coefficients for sitting posture in vehicles. The methodology follows a new strategy, i.e., in this application domain, here a car cabin, primarily relevant parameters are identified which affect the convective heat exchange. Following the sensitivity analysis of numerous computational fluid dynamics simulations with varying conditions, we identify relevant primary variables and heat transfer coefficients correlations and test the model robustness accordingly. A database-driven approach is developed in order to make correlations accessible during simulations, for example addressing energy performance. Finally, the experimentally investigated heat transfer analysis around the human body is presented and later compared with numerically reproduced data

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