Corrigendum to “Optimization, modeling and accurate prediction of thermal conductivity and dynamic viscosity of stabilized ethylene glycol and water mixture Al2O3 nanofluids by NSGA-II using ANN” [Int. Commun. Heat Mass Transfer 82 (2017) 154–160]

Esfe, Mohammad Hemmat; Razi, Peyman; Hajmohammad, Mohammad Hadi; Rostamian, Seyed Hadi; Sarsam, Wail Sami; Arani, Ali Akbar Abbasian; Dahari, Mahidzal

doi:10.1016/j.icheatmasstransfer.2017.05.005

Cited by 5 publications

(8 citation statements)

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“…In MATLAB, the identified discrete-time model is transformed into the continuous-time model and model order is reduced by balanced realization, obtaining the identified system model in the form of first-order plus time delay [35,36]. The performance parameter used for validating the identified model is the percentage of fit as the following expression (21), where y(k) is the actual output, ŷ(k) is the model output, and y(k) is the mean of the actual output:…”

Section: Experimental Setup and System Identificationmentioning

confidence: 99%

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A Reference-Model-Based Neural Network Control Method for Multi-Input Multi-Output Temperature Control System

Liu

Hashimoto

et al. 2020

Processes

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Neural networks (NNs), which have excellent ability of self-learning and parameter adjusting, has been widely applied to solve highly nonlinear control problems in industrial processes. This paper presents a reference-model-based neural network control method for multi-input multi-output (MIMO) temperature system. In order to improve the learning efficiency of the NN control, a reference model is introduced to provide the teaching signal for the NN controller. The control inputs for the MIMO system are given by the sum of the output of the conventional integral-proportional-derivative (I-PD) controller and the outputs of the neural network controller.The proposed NN control method can not only improve the transient response of the system, but can also realize temperature uniformity in MIMO temperature systems. To verify the proposed method, simulations are carried out in MATLAB/SIMULINK environment and experiments are carried out on the DSP (Digital Signal Processor)-based experimental platform, respectively. Both results are quantitatively compared to those obtained from the conventional I-PD control systems. The effectiveness of the proposed method has been successfully verified.

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Section: Experimental Setup and System Identificationmentioning

confidence: 99%

“…In thermal systems, the NN has been used for heat transfer data analysis, performance prediction, and dynamic modeling etc. [20][21][22][23]. It is shown that NN is well suitable to deal with complex nonlinear relationships in control systems.…”

Section: Introductionmentioning

confidence: 99%

A Reference-Model-Based Neural Network Control Method for Multi-Input Multi-Output Temperature Control System

Liu

Hashimoto

et al. 2020

Processes

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“…Esfe, Rostamian, Toghraie and Yan (2016), Esfe, Afrand, Gharehkhani, Rostamian, Toghraie and Dahari (2016), Esfe, Afrand, Yan, Yarmand, Toghraie and Dahari (2016), Esfe, Ahangar, Toghraie, Hajmohammad, Rostamian and Tourang (2016) and Esfe, Yan, Afrand, Sarraf, Toghraie and Dahari (2016) have designed an artificial neural network on thermal conductivity of Al 2 O 3 –water–EG (60–40 percen) nanofluid using experimental data. Esfe, Afrand, Rostamian and Toghraie (2017), Esfe, Hajmohammad, Toghraie, Rostamian, Mahian and Wongwises (2017), Esfe, Razi, Hajmohammad, Rostamian, Sarsam, Arani and Dahari (2017) examined the rheological behavior of MWCNTs/ZnO-SAE40 hybrid nano-lubricants under various temperatures and solid volume fraction fractions. Mohammad Hemmat Esfe, Rostamian, Toghraie and Yan (2016), Esfe, Afrand, Gharehkhani, Rostamian, Toghraie and Dahari (2016), Esfe, Afrand, Yan, Yarmand, Toghraie and Dahari (2016), Esfe, Ahangar, Toghraie, Hajmohammad, Rostamian and Tourang (2016) and Esfe, Yan, Afrand, Sarraf, Toghraie and Dahari (2016) used artificial neural network to predict thermal conductivity of ethylene glycol with alumina nanoparticle, Journal of Thermal Analysis and Calorimetry , 126(2) pp.…”

Section: Introductionmentioning

confidence: 99%

“…Mohammad Hemmat Esfe, Afrand, Rostamian and Toghraie (2017), Esfe, Hajmohammad, Toghraie, Rostamian, Mahian and Wongwises (2017), Esfe, Razi, Hajmohammad, Rostamian, Sarsam, Arani and Dahari (2017) studied about the optimization, modeling and accurate prediction of thermal conductivity and dynamic viscosity of stabilized ethylene glycol and water mixture Al 2 O 3 nanofluids by NSGA-II using ANN. Zadkhast et al (2017) have developed a new correlation to estimate the thermal conductivity of MWCNT-CuO/water hybrid nanofluid via an experimental investigation.…”

Section: Introductionmentioning

confidence: 99%

“…Abbas and Davood (2017) carried out a computational fluid dynamics simulation of heat transfer and fluid flow characteristics in a vortex tube by considering the various parameters. Mohammad Hemmat Esfe, Afrand, Rostamian and Toghraie (2017), Esfe, Hajmohammad, Toghraie, Rostamian, Mahian and Wongwises (2017) and Esfe, Razi, Hajmohammad, Rostamian, Sarsam, Arani and Dahari (2017) have studied the multi-objective optimization of nanofluid flow in double tube heat exchangers for applications in energy systems.…”

Section: Introductionmentioning

confidence: 99%

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MHD flow and heat transfer analysis of Newtonian and non-Newtonian nanofluids due to an inclined stretching surface

Nandeppanavar¹,

Srinivasulu

Shanker

2019

MMMS

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Purpose The purpose of this paper is to study the flow, heat and mass transfer of MHD Casson nanofluid due to an inclined stretching sheet using similarity transformation, the governing PDE’S equations of flow, heat and mass transfer are converted into ODE’S. The resulting non-linear ODE’S are solved numerically using an implicit finite difference method, which is known as Kellor-box method. The effects of various governing parameters on velocity, temperature and concentration are plotted for both Newtonian and non-Newtonian cases. The numerical values of skin friction, Nusselt number and Sherwood number are calculated and tabulated in various tables for different values of physical parameters. It is noticed that the effect of angle of inclination enhances the temperature and concentration profile whereas velocity decreases. The temperature decreases due to the increase in the parametric values of Pr and Gr due to thickening in the boundary layer. Design/methodology/approach Numerical method is applied to find the results. Findings Flow and heat transfer analysis w.r.t various flow and temperature are analyzed for different values of the physical parameters. Research limitations/implications The numerical values of skin friction, Nusselt number and Sherwood number are calculated and tabulated in various tables for different values of physical parameters. Practical implications The study of the boundary layer flow, heat and mass transfer is important due to its applications in industries and many manufacturing processes such as aerodynamic extrusion of plastic sheets and cooling of metallic sheets in a cooling bath. Originality/value Here in this paper the authors have investigated the MHD boundary layer flow of a Casson nanofluid over an inclined stretching sheet along with the Newtonian nanofluid as a limited.

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Influence of inclined Lorentz forces through a porous media on squeezing Cu-H₂o nanofluid in the presence of heat source/sink

Mousavisani

Khalesi

Golbaharan

et al. 2019

HFF

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Purpose The purpose of this paper is to find the approximate solutions of unsteady squeezing nanofluid flow and heat transfer between two parallel plates in the presence of variable heat source, viscous dissipation and inclined magnetic field using collocation method (CM). Design/methodology/approach The partial governing equations are reduced to nonlinear ordinary differential equations by using appropriate transformations and then are solved analytically by using the CM. Findings It is observed that the enhancing values of aligned angle of the magnetic causes a reduction in temperature distribution. It is also seen that the effect of nanoparticle volume fraction is significant on the temperature but negligible on the velocity profile. Originality/value To the best of the authors’ knowledge, no research has been carried out considering the combined effects of inclined Lorentz forces and variable heat source on squeezing flow and heat transfer of nanofluid between the infinite parallel plates.

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Corrigendum to “Optimization, modeling and accurate prediction of thermal conductivity and dynamic viscosity of stabilized ethylene glycol and water mixture Al2O3 nanofluids by NSGA-II using ANN” [Int. Commun. Heat Mass Transfer 82 (2017) 154–160]

Cited by 5 publications

References 0 publications

A Reference-Model-Based Neural Network Control Method for Multi-Input Multi-Output Temperature Control System

A Reference-Model-Based Neural Network Control Method for Multi-Input Multi-Output Temperature Control System

MHD flow and heat transfer analysis of Newtonian and non-Newtonian nanofluids due to an inclined stretching surface

Influence of inclined Lorentz forces through a porous media on squeezing Cu-H₂o nanofluid in the presence of heat source/sink

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Corrigendum to “Optimization, modeling and accurate prediction of thermal conductivity and dynamic viscosity of stabilized ethylene glycol and water mixture Al2O3 nanofluids by NSGA-II using ANN” [Int. Commun. Heat Mass Transfer 82 (2017) 154–160]

Cited by 5 publications

References 0 publications

A Reference-Model-Based Neural Network Control Method for Multi-Input Multi-Output Temperature Control System

A Reference-Model-Based Neural Network Control Method for Multi-Input Multi-Output Temperature Control System

MHD flow and heat transfer analysis of Newtonian and non-Newtonian nanofluids due to an inclined stretching surface

Influence of inclined Lorentz forces through a porous media on squeezing Cu-H2o nanofluid in the presence of heat source/sink

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Influence of inclined Lorentz forces through a porous media on squeezing Cu-H₂o nanofluid in the presence of heat source/sink