Changes in the Number of Membership Functions for Predicting the Gas Volume Fraction in Two-Phase Flow Using Grid Partition Clustering of the ANFIS Method

被引：38
作者：
Babanezhad, Meisam [3 ,4 ]
Nakhjiri, Ali Taghvaie [5 ]
Shirazian, Saeed [1 ,2 ]
机构：
[1] Ton Duc Thang Univ, Dept Management Sci & Technol Dev, Ho Chi Minh City, Vietnam
[2] Ton Duc Thang Univ, Fac Appl Sci, Ho Chi Minh City, Vietnam
[3] Duy Tan Univ, Inst Res & Dev, Da Nang 550000, Vietnam
[4] Duy Tan Univ, Fac Elect Elect Engn, Da Nang 550000, Vietnam
[5] Islamic Azad Univ, Dept Petr & Chem Engn, Sci & Res Branch, Tehran 1477893855, Iran
来源：
ACS OMEGA | 2020年 / 5卷 / 26期
关键词：
LARGE-EDDY SIMULATION; EPSILON TURBULENCE MODEL; BUBBLE-COLUMN; NUMERICAL-SIMULATION; DYNAMIC SIMULATION; LIQUID FLOW; HEAT-TRANSFER; CFD MODELS; 3-DIMENSIONAL SIMULATION; INTERFACIAL FORCES;
D O I：
10.1021/acsomega.0c02117
中图分类号：
O6 [化学];
学科分类号：
0703 ;
摘要：
A 2D-bubble column reactor (BCR) including gas and liquid phases is simulated, and fluid characteristics such as gas-phase volume fraction and gas-phase turbulence are extracted from the CFD simulations. A type of heuristic algorithm called adaptive network-based fuzzy inference system (ANFIS) is applied here to simulate the gas-phase volume fraction in a physical system. Indeed, the x direction, the y direction, and gas-phase turbulence are considered as the ANFIS inputs. Changes in the number of inputs as well as membership functions are evaluated and studied to obtain a high level of ANFIS intelligence. By implementing the highest ANFIS intelligence, a surface is predicted, which suggests that the gas-phase volume fraction is based on x and y directions. It provides capability to achieve the amount of gas-phase volume fraction in different points of a 2D-BCR.
引用
收藏
页码：16284 / 16291
页数：8
相关论文
共 74 条
[1]   The application of ANFIS prediction models for thermal error compensation on CNC machine tools [J].
Abdulshahed, Ali M. ;
Longstaff, Andrew P. ;
Fletcher, Simon .
APPLIED SOFT COMPUTING, 2015, 27 :158-168
[2]   Inverse fuzzy-process-model based direct adaptive control [J].
Abonyi, J ;
Andersen, H ;
Nagy, L ;
Szeifert, F .
MATHEMATICS AND COMPUTERS IN SIMULATION, 1999, 51 (1-2) :119-132
[3]   Comparison of finite difference methods for the numerical simulation of reacting flow [J].
Alhumaizi, K .
COMPUTERS & CHEMICAL ENGINEERING, 2004, 28 (09) :1759-1769
[4]  
[Anonymous], 2013, INT J MECH MAT ENG
[5]   Adaptive-Network-Based Fuzzy Inference System Analysis to Predict the Temperature and Flow Fields in a Lid-Driven Cavity [J].
Azwadi, Che Sidik Nor ;
Zeinali, Mohammadjavad ;
Safdari, Arman ;
Kazemi, Alieh .
NUMERICAL HEAT TRANSFER PART A-APPLICATIONS, 2013, 63 (12) :906-920
[6]   Liquid-phase chemical reactors: Development of 3D hybrid model based on CFD-adaptive network-based fuzzy inference system [J].
Babanezhad, Meisam ;
Rezakazemi, Mashallah ;
Hajilary, Nasibeh ;
Shirazian, Saeed .
CANADIAN JOURNAL OF CHEMICAL ENGINEERING, 2019, 97 :1676-1684
[7]   Dynamic simulation of a 2D bubble column [J].
Bech, K .
CHEMICAL ENGINEERING SCIENCE, 2005, 60 (19) :5294-5304
[8]   CFD models comparative study on nanofluids subcooled flow boiling in a vertical pipe [J].
Behroyan, I. ;
Ganesan, P. ;
He, S. ;
Sivasankaran, S. .
NUMERICAL HEAT TRANSFER PART A-APPLICATIONS, 2018, 73 (01) :55-74
[9]   A comprehensive comparison of various CFD models for convective heat transfer of Al2O3 nanofluid inside a heated tube [J].
Behroyan, I. ;
Vanaki, Sh M. ;
Ganesan, P. ;
Saidur, R. .
INTERNATIONAL COMMUNICATIONS IN HEAT AND MASS TRANSFER, 2016, 70 :27-37
[10]   Turbulent forced convection of Cu-water nanofluid: CFD model comparison [J].
Behroyan, I. ;
Ganesan, P. ;
He, S. ;
Sivasankaran, S. .
INTERNATIONAL COMMUNICATIONS IN HEAT AND MASS TRANSFER, 2015, 67 :163-172
12345678