Prediction of heat transfer coefficient during condensation of R134a in inclined tubes using artificial neural network

Azizi, Sadra; Ahmadloo, Ebrahim

doi:10.1016/j.applthermaleng.2016.05.189

Cited by 75 publications

(20 citation statements)

References 66 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Neurons in hidden and output layers have specific activation function for generation of outputs from summation function. In this paper, hyperbolic tangent sigmoid (tansig) and linear function (purelin) are used as hidden and output layers' activation function as follows, respectively (Azizi and Ahmadloo, 2016)…”

Section: Network Structurementioning

confidence: 99%

“…There are many learning algorithms for training like gradient descent with adaptive learning rule, gradient descent with momentum adaptive learning rule, scaled conjugate gradient and Levenberg-Marquardt (LM) (Cay et al, 2012). In this study, LM was utilized as the training function for learning process due to its fast convergence and stability in training (Azizi and Ahmadloo, 2016).…”

Section: Learning Algorithmmentioning

confidence: 99%

“…Network outputs are compared with actual data obtained from numerical simulation for investigating the performance of the ANN in predicting the solution of the problem. For the ANN, statistical criteria of mean square error (MSE), root mean square error (RMSE), coefficient related to the actual and predicted values (R) and mean relative error (MRE) defined as follows (Azizi and Ahmadloo, 2016):…”

Section: Statistical Criteria For Outputs Comparisonmentioning

confidence: 99%

See 2 more Smart Citations

An Innovative Approach for Study of Thermal Behavior of an Unsteady Nanofluid Squeezing Flow between Two Parallel Plates Utilizing Artificial Neural Network

Gorgani

Maghsoudi²,

Sadeghi

2019

European Journal of Sustainable Development Research

View full text Add to dashboard Cite

This study reveals the thermal behavior of an unsteady nanofluid streaming between two parallel plates by using artificial neural network (ANN). Initially, a similarity solution is employed to simplify the partial differential equations (PDSs) and convert them into a system of coupled nonlinear ordinary differential equations (ODEs). Subsequently, a numerical analysis is undertaken to verify the predicted results applying forth order Runge Kutta method. ANN is utilized to provide a nonlinear map between the considered input parameters such as solid volume fraction (Φ), Eckert number (Ec) and a moving parameter which represents the movement of the parallel plates (S), and output parameters like Nusselt number (Nu). Considering the accuracy of the current results, it is concluded that ANN method can be a potential reliable approach for function approximation. Results indicate that an optimal network with 16 neurons exists in hidden layer for which the value of RMSE for testing data is found to be 0.001364.

show abstract

Section: Network Structurementioning

confidence: 99%

Section: Learning Algorithmmentioning

confidence: 99%

Section: Statistical Criteria For Outputs Comparisonmentioning

confidence: 99%

See 1 more Smart Citation

An Innovative Approach for Study of Thermal Behavior of an Unsteady Nanofluid Squeezing Flow between Two Parallel Plates Utilizing Artificial Neural Network

Gorgani

Maghsoudi²,

Sadeghi

2019

European Journal of Sustainable Development Research

View full text Add to dashboard Cite

show abstract

“…As mentioned earlier, it is possible to define several numbers of hidden layers for a MLP-NN model; however, according to literature works [50,51] using one hidden layer is sufficient to accurately model a nonlinear data modeling process. In present work, in addition to networks with one hidden layer, the performance of different networks with two and three hidden layers was also examined.…”

Section: Mlp-nn Modelmentioning

confidence: 99%

“…10 methods because it provides stable training process and fast convergence of network to optimum solution [51]. In present work the LM technique was utilized to train the developed network.…”

Section: Accepted Manuscriptmentioning

confidence: 99%

Prediction of heat capacities of ionic liquids using chemical structure based networks

Barati-Harooni

Najafi‐Marghmaleki

Mohammadi

2017

Journal of Molecular Liquids

View full text Add to dashboard Cite

Accurate Prediction of Kinematic Viscosity of Biodiesels and their Blends with Diesel Fuels

Mehrizadeh

Nikbin‐Fashkacheh

Zand

et al. 2020

J Americ Oil Chem Soc

View full text Add to dashboard Cite

Viscosity of mixtures of biodiesels (admixtures) and mixtures of biodiesel/diesel (blends) is a important parameter for determining their combustion behavior. There is no universal and general model for prediction of viscosity of these systems at different conditions. Hence, developing simple, accurate, and general models for prediction of viscosity of these systems is of great importance. In this work, three computer-based models named multilayer perceptron neural network (MLP-NN), radial basis function optimized by particle swarm optimization (PSO-RBF), and adaptive neuro fuzzy inference system optimized by hybrid approach (Hybrid-ANFIS) were developed for prediction of viscosity of blends and admixtures. A number of 966 experimental data covering wide ranges of influencing parameters were utilized to develop the models. The accuracy of predictions of the developed models was examined by using different statistical quality measure approaches as well as comparing their results with the predictions of literature models. Results showed that the developed models present accurate predictions and are superior to the literature models. The predictions of PSO-RBF model were also better than Hybrid-ANFIS and MLP-NN models.

show abstract

Prediction of heat transfer coefficient during condensation of R134a in inclined tubes using artificial neural network

Cited by 75 publications

References 66 publications

An Innovative Approach for Study of Thermal Behavior of an Unsteady Nanofluid Squeezing Flow between Two Parallel Plates Utilizing Artificial Neural Network

An Innovative Approach for Study of Thermal Behavior of an Unsteady Nanofluid Squeezing Flow between Two Parallel Plates Utilizing Artificial Neural Network

Prediction of heat capacities of ionic liquids using chemical structure based networks

Accurate Prediction of Kinematic Viscosity of Biodiesels and their Blends with Diesel Fuels

Contact Info

Product

Resources

About