A dynamic two-level artificial neural network for estimation of parameters in combined mode conduction-radiation heat transfer in porous medium: An application to handle huge dataset with noise

A dynamic two-level artificial neural network (DTLANN) approach is used for the estimation of parameters in combined mode conduction–radiation heat transfer in a porous medium. Four commonly used neural networks: feed forward, cascade forward, fitnet, and radial basis are used in mapping artificial neural network (ANN), and their performance is compared under noisy big data (10,302 × 1300 matrix size). Governing equations for heat transfer in the porous medium through conduction and radiation modes are solved by finite volume method and discrete transfer method. This numerical model is called a direct model. A large amount of data is generated by using the direct model for different values of extinction coefficient β and convective coupling P₂. These data were divided into different groups (class) based on the temperature difference between the gas and solid phase. In the inverse analysis, a new pair of temperature profiles for the solid and gas phase is taken as input and classified with the help of a pattern net artificial neural network model. On the basis of classification, data from that particular class and its neighbor class are used for training the mapping ANN model. After the training of the mapping ANN model, corresponding values of β and P₂ are obtained as output for any new input. This DTLANN model has a high regression coefficient (R) of .99131 and can predict highly accurate values of parameters under a huge dataset with noise, within much less CPU time.     

A novel application of artificial neural network for wind speed estimation

Providing accurate multi-steps wind speed estimation models has increasing significance, because of the important technical and economic impacts of wind speed on power grid security and environment benefits. In this study, the combined strategies for wind speed forecasting are proposed based on an intelligent data processing system using artificial neural network (ANN). Generalized regression neural network and Elman neural network are employed to form two hybrid models. The approach employs one of ANN to model the samples achieving data denoising and assimilation and apply the other to predict wind speed using the pre-processed samples. The proposed method is demonstrated in terms of the predicting improvements of the hybrid models compared with single ANN and the typical forecasting method. To give sufficient cases for the study, four observation sites with monthly average wind speed of four given years in Western China were used to test the models. Multiple evaluation methods demonstrated that the proposed method provides a promising alternative technique in monthly average wind speed estimation.     

Retrieval of parameters in micromixer with obstacle by cascade-forward-type artificial neural network: Comparison of four training algorithms under noisy data

Two parameters are retrieved in a passive Y-type micromixer with circular obstacle by cascade-forward-type artificial neural network (CFANN). The governing equations are solved by the finite volume method, under specific boundary conditions. The numerical model is then used to compute velocity profile and mixing efficiency, for different values of the Reynolds number. Thus, the velocity profiles along with Reynolds number (Re) and mixing efficiency (η) constitute the input–output pair of data. These data are used to train CFANN, and the network is monitored through different means, like, histograms, performance curves, and so forth. For inverse analysis, the trained CFANN model is fed with a new velocity profile as input, and corresponding values of Reynolds number and mixing efficiency are obtained as output. In an attempt to construct the optimum CFANN model, various combinations were explored, like, (1) different numbers of neurons in the hidden layer, (2) different noise levels in input data, and (3) different algorithms in the training stage. Finally, the CFANN with 10 hidden layer neurons with Levenberg–Marquardt (LM) algorithm was found to give retrieved values with up to 0.96% absolute error for all levels of noise in the input data. Also, the CFANN model with the LM algorithm has a very high value of regression coefficient of greater than 0.998, under all the noise values. Scaled conjugate gradient algorithm gives good results for the no-noise case, but fails poorly with the rise of noise. Other algorithms, like, Bayesian regularization and resilient backpropagation, perform poorly even in the no-noise case. The present approach is highly simple, accurate, and time efficient for applying inverse analysis in micromixers.     

Modeling of convection heat transfer of supercritical carbon dioxide in a vertical tube at low Reynolds numbers using artificial neural network

Today, many researches have been directed on heat transfer of supercritical fluids; however, since the analysis of heat transfer in these fluids founded by a mathematical model based on the effective parameters is complicated, so in this paper, a group method of data handling (GMDH) type artificial neural network are used for calculating local heat transfer coefficient h_x of supercritical carbon dioxide in a vertical tube with 2 mm diameter at low Reynolds numbers (Re < 2500) by empirical results obtained by Jiang et al. [1].At first, we considered h_x as target parameter and G, Re, Bo^?, x⁺ and q_w as input parameters. Then, we divided empirical data into train and test sections in order to accomplish modeling. We instructed GMDH type neural network by 80% of the empirical data. 20% of primary data which had been considered for testing the appropriateness of the modeling were entered into the GMDH network. Results were compared by two statistical criterions (R² and RMSE) with empirical ones. The results obtained by using GMDH type neural network are in excellent agreement with the experimental results.     

Application of neural network on heat transfer enhancement of magnetohydrodynamic nanofluid

The applications of neural networks (NNs) on engineering problems have been increased for obtaining high precision results. In this study, a new type of NN known as the group method of data handling (GMDH) is applied to obtain a formulation of a heat transfer rate. The numerical method of control volume‐based finite element method (CVFEM) is applied as a robust and reliable numerical approach for simulation of magnetohydrodynamic (MHD) flow of a nanofluid inside an inclined enclosure with a sinusoidal wall. A water‐based nanofluid with Cu nanoparticles is used as main fluid in our model. Maxwell–Garnetts (MG) and Brinkman models are applied to calculate effective thermal conductivity and viscosity of nanofluid, respectively. This study tries to find that GMDH‐type NN is a reliable technique for calculation of MHD nanofluid convective based on specified variables. Our findings clearly demonstrate that GMDH‐type NN is more reliable than the CVFEM approach and this technique could efficiently identify the patterns in data and precisely estimate a performance. Comprehensive parametric studies are done to disclose the impact of significant factors such as electromagnetic force, buoyancy, nanoparticle volume fraction, and direction of enclosure on heat transfer rates. According to obtained results, heat transfer rate rises with the growth of buoyancy effects, the concentration of nanoparticles, and slope of domain while it reduces when Hartmann number is increased.     

Genetic algorithm-assisted artificial neural network for retrieval of a parameter in a third grade fluid flow through two parallel and heated plates

Genetic algorithm (GA) has been used to determine important attributes of artificial neural network (ANN), such as number of neurons in different hidden layers and division of data for training, validation, and testing. The GA-assisted ANN (GAAANN) model was used to retrieve third grade fluid (TGF) parameter (A) in a TGF flow problem. The TGF was allowed to flow through two parallel plates, which were subjected to uniform heat flux. The least square method (LSM) was used to solve the governing equations, for specified boundary conditions. In this way, temperature profiles for different values of A were computed by LSM, constituting the direct part of the problem. In the inverse part, the GAAANN model was fed with a temperature profile as input and the corresponding value of A was obtained as output. Four different GAAANN model were developed, and a detailed analysis was done in retrieving the value of A by different GAAANN models. Two very important and commonly used algorithms: Levenberg-Marquardt (LM) and scaled conjugate gradient are explored for training of the neurons. The entire four GAAANN model were able to retrieve the value of A with different levels of accuracy.     

An inverse problem of parameter estimation for heat and mass transfer in capillary porous media

This work deals with the solution of an inverse problem of parameter estimation involving heat and mass transfer in capillary porous media, as described by the dimensionless linear Luikov’s equations. The physical problem under picture involves the drying of a moist porous one-dimensional medium. The main objective of this paper is to simultaneously estimate the dimensionless parameters appearing in the formulation of the physical problem by using transient temperature and moisture content measurements taken inside the medium. The inverse problem is solved by using the Levenberg-Marquardt method of minimization of the least-squares norm with simulated measurements.     

A simplified model for generating sequences of global solar radiation data for isolated sites: Using artificial neural network and a library of Markov transition matrices approach

The purpose of this work is to develop a hybrid model which will be used to predict the daily global solar radiation data by combining between an artificial neural network (ANN) and a library of Markov transition matrices (MTM) approach. Developed model can generate a sequence of global solar radiation data using a minimum of input data (latitude, longitude and altitude), especially in isolated sites. A data base of daily global solar radiation data has been collected from 60 meteorological stations in Algeria during 1991–2000. Also a typical meteorological year (TMY) has been built from this database. Firstly, a neural network block has been trained based on 60 known monthly solar radiation data from the TMY. In this way, the network was trained to accept and even handle a number of unusual cases. The neural network can generate the monthly solar radiation data. Secondly, these data have been divided by corresponding extraterrestrial value in order to obtain the monthly clearness index values. Based on these monthly clearness indexes and using a library of MTM block we can generate the sequences of daily clearness indexes. Known data were subsequently used to investigate the accuracy of the prediction. Furthermore, the unknown validation data set produced very accurate prediction; with an RMSE error not exceeding 8% between the measured and predicted data. A correlation coefficient ranging from 90% and 92% have been obtained; also this model has been compared to the traditional models AR, ARMA, Markov chain, MTM and measured data. Results obtained indicate that the proposed model can successfully be used for the estimation of the daily solar radiation data for any locations in Algeria by using as input the altitude, the longitude, and the latitude. Also, the model can be generalized for any location in the world. An application of sizing PV systems in isolated sites has been applied in order to confirm the validity of this model.     

An artificial neural network‐based condition monitoring method for wind turbines,with application to the monitoring of the gearbox

Major failures in wind turbines are expensive to repair and cause loss of revenue due to long downtime. Condition‐based maintenance, which provides a possibility to reduce maintenance cost, has been made possible because of the successful application of various condition monitoring systems in wind turbines. New methods to improve the condition monitoring system are continuously being developed. Monitoring based on data stored in the supervisory control and data acquisition (SCADA) system in wind turbines has received attention recently. Artificial neural networks (ANNs) have proved to be a powerful tool for SCADA‐based condition monitoring applications. This paper first gives an overview of the most important publications that discuss the application of ANN for condition monitoring in wind turbines. The knowledge from these publications is utilized and developed further with a focus on two areas: the data preprocessing and the data post‐processing. Methods for filtering of data are presented, which ensure that the ANN models are trained on the data representing the true normal operating conditions of the wind turbine. A method to overcome the errors from the ANN models due to discontinuity in SCADA data is presented. Furthermore, a method utilizing the Mahalanobis distance is presented, which improves the anomaly detection by considering the correlation between ANN model errors and the operating condition. Finally, the proposed method is applied to case studies with failures in wind turbine gearboxes. The results of the application illustrate the advantages and limitations of the proposed method. Copyright © 2017 John Wiley & Sons, Ltd.     

An experimental data set for benchmarking 1-D, transient heat and moisture transfer models of hygroscopic building materials. Part II: Experimental, numerical and analytical data

This paper presents the experimental results on spruce plywood and cellulose insulation using the transient moisture transfer (TMT) facility presented in Part I [P. Talukdar, S.O. Olutmayin, O.F. Osanyintola, C.J. Simonson, An experimental data set for benchmarking 1-D, transient heat and moisture transfer models of hygroscopic building materials-Part-I: experimental facility and property data, Int. J. Heat Mass Transfer, in press, doi:10.1016/j.ijheatmasstransfer.2007.03.026] of this paper. The temperature, relative humidity and moisture accumulation distributions within both materials are presented following different and repeated step changes in air humidity and different airflow Reynolds numbers above the materials. The experimental data are compared with numerical data, numerical sensitivity studies and analytical solutions to increase the confidence in the experimental data set.