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Abstract: Although the thermal bridge effects on the building energy performance have been presented in the literature, the multidimensional hygrothermal analysis of the building envelope is still a challenge due to many difficulties such as modeling complexity, computer run time, numerical convergence and highly moisture-dependent properties. However, their effects are of paramount importance due to the local increase of heat and mass flux densities so that moisture can be easily accumulated around internal corners, increasing mold growth risk and causing structural damage. Therefore, for analyzing the effects of building lower and upper corners, a multidimensional model has been developed to calculate the coupled heat, air and moisture transfer through building envelopes. The algebraic equations are simultaneously solved for the three driving potentials — temperature, vapor pressure and gas pressure gradients — to improve the numerical stability of the discretized model. In the Results section, the coupling of the upper corner, wall, lower corner (with different types of foundations), ground and floor are analyzed in terms of temperature and relative humidity profiles, vapor flow and heat flux, showing the importance of a detailed hygrothermal analysis for accurately predicting building energy consumption, mold growth and structural damage risks. [Copyright &y& Elsevier]

Abstract: The present study aims to evaluate the importance of wind turbulence when performing droplet trajectory numerical simulations. Turbulence effect on Wind-Driven Rain is studied by calculating the specific catch ratio of 1 mm droplets for an isolated low-rise building windward surface under a 5 m/s normal wind. An Eulerian Second Moment Closure and a Lagrangian one-way coupling models are used to calculate the airflow field and the droplet trajectories respectively. Results show that turbulence has a negligible effect considering the whole windward wall but can substantially modify the value of the specific catch ratio in the case of Wind-Driven Rain gauges. [Copyright &y& Elsevier]

Abstract: Studies on the dynamics of temperature and moisture content distributions in porous soils have provided important insight on their effect on the building hygrothermal behavior, where the interaction between both building and soil can contribute to reduce building thermal gains or looses. Hygrothermal aspects can be related to many attributes such as energy consumption, occupants'' thermal comfort and health, and material deterioration. Recently, a great variety of mathematical models to predict thermal and moisture content profiles in porous media have been presented in the literature. Most of those models are based on analysis of multilayer measurements or on Fourier analysis. The development and validation of such mathematical models facilitate the understanding of heat and moisture flows at different soil depths. In this research, a radial basis function neural network (RBF-NN) approach, combined with Gath–Geva clustering method in order to predict the temperature and moisture content profiles in soils, has been presented. A set of data obtained from the computation of the coupled heat and moisture transfer in porous soils for the Curitiba city (Paraná State, Brazil) weather data file has been used by the RBF-NN modeling method. Simulation results indicate the potentialities of the RBF-NNs to learn, for the one step ahead identification, the behavior of temperature and moisture content profiles in the media at various depths. [Copyright &y& Elsevier]