Modeling of mixed convection in an enclosure using multiple regression, artificial neural network, and adaptive neuro-fuzzy interface system models

2020 ◽  
Vol 234 (15) ◽  
pp. 3078-3093
Author(s):  
Ece Aylı
Keyword(s):  
Neural Network ◽  
Heat Transfer ◽  
Artificial Neural Network ◽  
Nusselt Number ◽  
Forced Convection ◽  
Number Distribution ◽  
Neuro Fuzzy ◽  
Interface System ◽  
Nusselt Number Distribution ◽  
Artificial Neural

In this study, the heat transfer characteristics of laminar combined forced convection through a horizontal duct are obtained with the help of the numerical methods. The effect of the geometrical parameters of the cavity and Reynolds number on the heat transfer is investigated. New heat transfer correlation for hydrodynamically fully developed, laminar combined forced convection through a horizontal duct is proposed with an average error of 6.98% and R2 of 0.8625. The obtained correlation results are compared with the artificial neural network and adaptive neuro-fuzzy interface system models. Due to the obtained results, good agreement is identified between the numerical results and predicted adaptive neuro-fuzzy interface system results. In conclusion, it is seen that adaptive neuro-fuzzy interface system can predict the Nusselt number distribution with a higher accuracy than the developed correlation and the artificial neural network model. The developed adaptive neuro-fuzzy interface system model predicts the Nusselt number with 1.07% mean average percentage error and 0.9983 R2 value. The effect of the different training algorithms and their ability to predict Nusselt number distribution are examined. According to the results, the Bayesian regulation algorithm gives the best approach with a 2.235% error. According to the examination that is performed in this study, the adaptive neuro-fuzzy interface system is a powerful, robust tool that can be used with confidence for predicting the thermal performance.

Prediction of Post-Dryout Heat Transfer in Vertical Annular Channels Using Artificial Neural Network Method

2008 ◽  
Author(s):  
D. W. Zhao ◽  
G. H. Su ◽  
S. Z. Qiu ◽  
W. X. Tian
Keyword(s):  
Neural Network ◽  
Heat Transfer ◽  
Artificial Neural Network ◽  
Nusselt Number ◽  
Annular Channel ◽  
Experimental Investigations ◽  
Gap Size ◽  
Annular Channels ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

Experimental investigations on post-dryout heat transfer in 10×8.1, 10×7 and 10×6mm annular test sections have been carried out under low-pressure and low mass flow rate conditions. An Artificial Neural Network (ANN) was trained successfully based on the experimental data for predicting the average post-dryout Nusselt number. Based on the ANN, the effects of gap size, pressure, steam Reynolds number, Reg, inlet quality, xi, Prandtl number, (Prg)W, and the ratio of heat flux of inner-tube to that of outer-tube, qi/qo, on post-dryout heat transfer were analyzed, respectively. In present study, Nusselt number in annular channels with big gap size is larger than that in annular channels with small gap size. Nusselt number increases significantly in 1.5mm and 2.0mm annular channels while it is almost constant in 0.95mm annular channel with increasing pressure or qi/qo. Nusselt number increases with Reg in case of 0.95mm and 1.5mm gap sizes. However, Nusselt number in 2.0mm annular channel firstly increases and then decreases with increasing Reg. Nusselt number decreases with increasing inlet quality under all three annular channels condition. Nusselt number decreases significantly with increasing (Prg)W when (Prg)W is less than 1.5. The changes of Nusselt number in 1.5mm or 2.0mm annular channels are larger than that in 0.95mm annular channel.

A COMPARATIVE STUDY OF MULTIPLE REGRESSION AND MACHINE LEARNING TECHNIQUES FOR PREDICTION OF NANOFLUID HEAT TRANSFER

2021 ◽  
pp. 1-22
Author(s):  
Eyup Kocak ◽  
Ulku Ece Ayli ◽  
Hasmet Turkoglu
Keyword(s):  
Heat Transfer ◽  
Nusselt Number ◽  
Multiple Regression ◽  
Volume Fraction ◽  
Particle Diameter ◽  
Machine Learning Techniques ◽  
Exponential Factor ◽  
Nano Fluid ◽  
Neuro Fuzzy ◽  
Interface System

Abstract The aim of this paper is to introduce and discuss prediction power of the multiple regression technique, Artificial Neural Network (ANN) and Adaptive Neuro-Fuzzy Interface System (ANFIS) methods for predicting the forced convection heat transfer characteristics of a turbulent nano fluid flow a pipe. Water and Al2O3 mixture is used as the nano fluid. Utilizing FLUENT software, numerical computations were performed with volume fraction ranging between 0.3% and 5%, particle diameter ranging between 20 and 140 nm and Reynolds number ranging between 7000 and 21000. Based on the computationally obtained results, a correlation is developed for Nusselt number using the multiple regression method. Also, based on the CFD results different ANN architectures with different number of neurons in the hidden layers and several training algorithms (Levenberg-Marquardt, Bayesian Regularization, Scaled Conjugate Gradient) are tested to find the best ANN architecture. In addition, Adaptive Neuro-fuzzy Interface System (ANFIS) is also used to predict the Nusselt number. In the ANFIS, number of clusters, exponential factor and Membership Function (MF) type are optimized. The results obtained from multiple regression correlation, ANN and ANFIS were compared. According to the obtained results, ANFIS is a powerful tool with a R2 of 0.9987 for predictions.

Comparison of different modeling methods toward predictive capability evaluation of the bonding strength of wood laminated products

2021 ◽  
pp. 095440892110530
Author(s):  
Morteza Nazerian ◽  
Seyed Ali Razavi ◽  
Ali Partovinia ◽  
Elham Vatankhah ◽  
Zahra Razmpour
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Response Surface Methodology ◽  
Response Surface ◽  
Bonding Strength ◽  
Fuzzy Inference ◽  
Molar Ratio ◽  
Inference System ◽  
Neuro Fuzzy ◽  
Artificial Neural

The main aim of this study is usability evaluation of different approaches, including response surface methodoloy, adaptive neuro-fuzzy inference system, and artificial neural network models to predict and evaluate the bonding strength of glued laminated timber (glulam) manufactured using walnut wood layers and a natural adhesive (oxidized starch adhesive), with respect to this fact that using the modified starch can decrease the formaldehyde emission. In this survey, four variables taken as the input data include the molar ratio of formaldehyde to urea (1.12–1.52), nanocellulose content (0%–4%, based on the dry weight of the adhesive), the mass ratio of the oxidized starch adhesive to the urea formaldehyde resin (30:70–70:30), and the press time (4–8 min). In order to find the best predictive performance of each selected evaluation approach, different membership functions were used. The optimal results were obtained when the molar ratio, nanocellulose content, mass ratio of the oxidised starch, and press time were set at 1.22, 3%, 70:30, and 7 min, respectively. Based on the performance criteria including root mean square error (RMSE) and mean absolute percentage error (MAPE) obtained from the modeling of response surface methodology, adaptive neuro-fuzzy inference network, and artificial neural network, it became evident that response surface methodology could offer a better prediction of the response with the lowest level of errors. Beside, artificial neural network and adaptive neuro-fuzzy inference system support the response surface methodology approach to evaluate bonding strength response with high precision as well as to determine the optimal point in fabrication of laminated products.

Sign in / Sign up

Export Citation Format

Share Document