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Article

Design of Cyclone Separator Critical Diameter Model Based on Machine Learning and CFD

1
Department of Advanced Materials and Parts of Transportation Systems, Pusan National University, Busan 46241, Korea
2
School of Mechanical Engineering, Pusan National University, Busan 46241, Korea
*
Author to whom correspondence should be addressed.
Processes 2020, 8(11), 1521; https://doi.org/10.3390/pr8111521
Received: 3 November 2020 / Revised: 19 November 2020 / Accepted: 21 November 2020 / Published: 23 November 2020
(This article belongs to the Special Issue Applied Computational Fluid Dynamics (CFD))
In this paper, the characteristics of the cyclone separator was analyzed from the Lagrangian perspective for designing the important dependent variables. The neural network network model was developed for predicting the separation performance parameter. Further, the predictive performances were compared between the traditional surrogate model and the developed neural network model. In order to design the important parameters of the cyclone separator based on the particle separation theory, the force acting until the particles are separated was calculated using the Lagrangian-based computational fluid dynamics (CFD) methodology. As a result, it was proved that the centrifugal force and drag acting on the critical diameter having a separation efficiency of 50% were similar, and the particle separation phenomenon in the cyclone occurred from the critical diameter, and it was set as an important dependent variable. For developing a critical diameter prediction model based on machine learning and multiple regression methods, unsteady-Reynolds averaged Navier-Stokes analyzes according to shape dimensions were performed. The input design variables for predicting the critical diameter were selected as four geometry parameters that affect the turbulent flow inside the cyclone. As a result of comparing the model prediction performances, the machine learning (ML) model, which takes into account the critical diameter and the nonlinear relationship of cyclone design variables, showed a 32.5% improvement in R-square compared to multi linear regression (MLR). The proposed techniques have proven to be fast and practical tools for cyclone design. View Full-Text
Keywords: cyclone separator; computational fluid dynamics (CFD); machine learning; unsteady RANS; critical diameter cyclone separator; computational fluid dynamics (CFD); machine learning; unsteady RANS; critical diameter
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MDPI and ACS Style

Park, D.; Go, J.S. Design of Cyclone Separator Critical Diameter Model Based on Machine Learning and CFD. Processes 2020, 8, 1521. https://doi.org/10.3390/pr8111521

AMA Style

Park D, Go JS. Design of Cyclone Separator Critical Diameter Model Based on Machine Learning and CFD. Processes. 2020; 8(11):1521. https://doi.org/10.3390/pr8111521

Chicago/Turabian Style

Park, Donggeun; Go, Jeung S. 2020. "Design of Cyclone Separator Critical Diameter Model Based on Machine Learning and CFD" Processes 8, no. 11: 1521. https://doi.org/10.3390/pr8111521

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