Nuclear Science and Techniques

《核技术》(英文版) ISSN 1001-8042 CN 31-1559/TL     2019 Impact factor 1.556

Nuclear Science and Techniques ›› 2017, Vol. 28 ›› Issue (1): 5 doi: 10.1007/s41365-016-0159-6


Particle dispersion modeling in ventilated room using artificial neural network

Athmane Gheziel 1,2, Salah Hanini 2, Brahim Mohamedi 1, Abdelrahmane Ararem 1   

  1. 1 Birine Nuclear Research Centre, P.B. 180, C.P. 17200 Ain Oussera, Algeria
    2 LBMPT Yahia Fare`s University, 26000 Me´de´a, Algeria
  • Contact: Athmane Gheziel
  • Supported by:

    This work was supported by the Algerian Atomic Energy Commission. The authors are grateful for the financial support.

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Athmane Gheziel, Salah Hanini, Brahim Mohamedi, Abdelrahmane Ararem. Particle dispersion modeling in ventilated room using artificial neural network.Nuclear Science and Techniques, 2017, 28(1): 5     doi: 10.1007/s41365-016-0159-6


Due to insufficiency of a platform based on experimental results for numerical simulation validation using computational fluid dynamic method (CFD) for different geometries and conditions, in this paper we propose a modeling approach based on the artificial neural network (ANN) to describe spatial distribution of the particles concentration in an indoor environment. This study was performed for a stationary flow regime. The database used to build the ANN model was deducted from bibliography literature and composed by 261 points of experimental measurement. Multilayer perceptron-type neural network (MLP-ANN) model was developed to map the relation between the input variables and the outputs. Several training algorithms were tested to give a choice of the Fletcher conjugate gradient algorithm (TrainCgf). The predictive ability of the results determined by simulation of the ANN model was compared with the results simulated by the CFD approach. The developed neural network was beneficial and easy to predict the particle dispersion curves compared to CFD model. The average absolute error given by the ANN model does not reach 5% against 18% by the Lagrangian model and 28% by the Euler drift-flux model of the CFD approach.

Key words: Numerical simulation, Computational fluid dynamic, Artificial neural network, Spatial distribution, Particle concentration, Indoor environment