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

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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.

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Authors and Affiliations

  1. Faculty of Mechanical Engineering, RWTH Aachen University, Aachen, Germany

    Kasra Amini

  2. Institute of Aircraft Propulsion Systems (Institut für Luftfahrtantriebe, ILA), University of Stuttgart, Stuttgart, Germany

    Kasra Amini

  3. Max Planck Institute for Intelligent Systems, Tübingen, Germany

    Arash Mehrjou

  4. ETH Zürich, Zürich, Switzerland

    Arash Mehrjou

  5. Center of Excellence for Computational Aerospace Engineering, Amirkabir University of Technology (Tehran Polytechnic), Tehran, Iran

    Mahmoud Mani

Authors
  1. Kasra Amini
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  2. Arash Mehrjou
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  3. Mahmoud Mani
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Correspondence to Kasra Amini.

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Note to practitioners

This research was motivated by recent growth of attention to smart buildings with efficient use of energy. In the context of the global energy crisis, this falls in the realm of efficient and sustainable design. Controlling boundary layers around the house is a solution for managing energy loss through the walls. The boundary layer separation should be controllable to react to the atmospheric changes at the location of the building. Controllability means the orientation of the flow controlling blades must be decided based on the direction of wind around the house. Computer simulation gives rough estimate of the proper orientation of the components for a number of wind directions. However, due to the immense cost of simulations, the orientation of the FCBs for many wind directions will be left undecided. To enhance the resolution of the controller, we make use of an approximator that predicts a decent value of the FCB orientation for wind directions between two simulated conditions. This can allow the building controller to decide for every direction of wind which has not been simulated in advance. In this research, we rely merely on the simulation to develop the predictor of the FCB orientations. In the future, it would be fruitful to incorporate the knowledge of the field engineers into the approximator. This can reduce the need for simulation data and decrease the computational burden of the method.

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Amini, K., Mehrjou, A. & Mani, M. Enhancing the resolution of the angular orientations of the flow controlling blades on a sustainable house by training an artificial neural network. Energy Efficiency 14, 25 (2021). https://doi.org/10.1007/s12053-021-09931-6

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