Flow prediction in two-dimensional asymmetric diffuser by neural network and comparison with three turbulence models and experimental data

علوم کاربردی و محاسباتی در مکانیک

Article 4, Volume 34, Issue 2 - Serial Number 28, August 2022, Pages 45-60   

Document Type: Original Article

DOI: 10.22067/jacsm.2022.74041.1076

Authors

Mostafa Zamani mohiabadi^{* 1}; Farid Soltani¹; Ahmad Reza Boroomandpour²; Ghanbar Ali Sheikhzadeh¹

¹Department of Mechanical Engineering, University of Kashan, Kashan, Iran

²Department of Mechanical Engineering, Khomeinishahr Branch, Islamic Azad University, Khomeinishahr, Iran

Abstract

In this paper, turbulent flow in an asymmetric two-dimensional diffuser is investigated. In many applications, it is important to know whether the boundary layer separates from the surface or inside a particular object, it is also important to know exactly where the flow separation occurs. Combining turbulence data with artificial intelligence is currently an active research topic for studying turbulence. This research makes it possible to replace traditional turbulent models with artificial neural networks (ANN). In this study, to predict flow separation in an asymmetric two-dimensional diffuser, three turbulence models, standard k-, standard k-and SST k-, and intelligent neural network model with reverse pressure gradient were investigated. Fluent software was used to solve the Navier-Stokes-Reynolds equations. Three types of networking are designed and at the end, the second type is used to analyze the flow. 21, 29, 39 and 49 cm distances from the edge of the diffuser were analyzed and compared with experimental data. x and y/H are considered as the input point and U/U0 is the velocity at that point as the output of the neural network model. RMSE, MBE, t-test statistical indices have been calculated and reported for the desired points, The ANN had a better prediction of separation than the other three standard models, and the standard k- had a lower prediction than the other models. This research shows the perspective of chaotic modeling with machine learning methods, especially neural networks.

Keywords

Neural network; turbulence; diffuser

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