Enhancing the resolution of the angular orientations of the flow controlling blades on a sustainable house by training an artificial neural network

Abstract

Among all its effects, the development of the boundary layer, its separation, and formation of the wake region could lead to higher convective heat transfer over the body, if the flow conditions cause high gradient velocity profiles in the surface vicinities of the field. And also, a low-pressure region in the downstream of the geometry is formed, which increases the pressure drag exerted on it. The influence of the aforementioned issue on the zero energy house design has been tackled by introducing a new flow control mechanism. The so-called flow controlling blades (FCBs) were recently designed and investigated on a smart sustainable house, in order to control the flow field around the house, prevent the separation, and decrease the wake intensity, targeting a lower level of convective heat loss and drag force exerted on the body. The angular orientation of these FCBs was formerly determined for 12 different free wind directions (30° increments), as a look-up table for the main control system of the house. To increase the resolution of the orientations, we make use of a recently successful tool in machine learning called neural networks to estimate the desired orientation of the blades for the wind directions that do not exist in the said look-up table. Consequently, all the sample investigated sub-intervals not originally covered by the CFD data, showing great coincidence with the data driven from the neural network utilized in this study.