2017
DOI: 10.24200/sci.2017.4520
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A Combination of Computational Fluid Dynamics, Artificial Neural Network and Support Vectors Machines Model to Predict Flow Variables in Curved Channel

Abstract: This study show the combination of computational fluid dynamics (CFD) and soft computing techniques to make viewpoint for two-phase flow modelling and accuracy evaluation of soft computing methods in the three-dimensional flow variables prediction in curved channels.

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Cited by 10 publications
(16 citation statements)
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“…In these equations x  and y  are the mesh dimensions in plan; P is the center of u or v control volumes and W , E , S and N are the grid points surrounding it (Figure 2a); superscripts u and v specify the momentum equation for x and y directions, respectively; and u Sr and v Sr are the source terms which includes the discretized state of all of the terms given in Equations ( 10) and (11). These terms are calculated in each grid point, by interpolating the values of the variables neighboring it from the last iteration (Figure 2b); a is coefficient of discretization and P a is as follows:…”
Section: Discretization Methods and The Solution Algorithmmentioning
confidence: 99%
“…In these equations x  and y  are the mesh dimensions in plan; P is the center of u or v control volumes and W , E , S and N are the grid points surrounding it (Figure 2a); superscripts u and v specify the momentum equation for x and y directions, respectively; and u Sr and v Sr are the source terms which includes the discretized state of all of the terms given in Equations ( 10) and (11). These terms are calculated in each grid point, by interpolating the values of the variables neighboring it from the last iteration (Figure 2b); a is coefficient of discretization and P a is as follows:…”
Section: Discretization Methods and The Solution Algorithmmentioning
confidence: 99%
“…Many researchers from the past so far have been trying to study the structure of the flow around the bridge piers with numerical and experimental methods. These include Dey and Raikar (2007), Kirkil et al (2008), Rodriguez and Garcia (2008), Belcher and Fox (2009), Beheshti and Ataie-Ashtiani (2010), Kirkil and Constantinescu (2010), Şarlak and Tiğrek (2011), Kumar et al (2012), Ataie-Ashtiani and Aslani-Kordkandi (2012, 2013, Das et al (2013aDas et al ( , 2013b, Das and Mazumdar (2015), Ma et al (2015), Beheshti and Ataie-Ashtiani (2016), , Ma et al (2016), Karimi et al (2017), Khan et al (2017) in the straight path and also Naji Abhari et al (2010), Barbhuiya and Talukdar (2010), Constantinescu et al (2011), Uddin and Rahman (2012), Tang and Knight (2014), Vaghefi et al (2015), Vaghefi et al (2016), Ben Mohammad Khajeh et al (2017, Dey et al (2017), Vaghefi et al (2017), Abdi Chooplou et al (2018), Gholami et al (2019) in the bend. Considering that previous studies have shown that there was no comprehensive study on flow pattern around the bridge pier with collar in the case of placement in bend, it is important to understand the pattern of the flow around the bridge pier protected by collar.…”
Section: Introductionmentioning
confidence: 99%
“…For instance, Dinar et al [22] used a cavity ring-down aerosol spectrometer to obtain the complex refractive index of the atmosphere; Xie et al [23] used one-dimensional variational assimilation algorithm to obtain atmospheric refractive index from groundbased Global Positioning System (GPS) phase delay. Besides, with the development of computer, machine learning is applied more and more in the field of meteorology [24][25][26][27][28][29][30][31][32][33]. Machine learning is a process of using computers to summarize the existing data, get its general rules, and establish the corresponding mapping relationship [34].…”
Section: Introductionmentioning
confidence: 99%