scholarly journals Application of Artificial Neural Networks for Accurate Prediction of Thermal and Rheological Properties of Nanofluids

2020 ◽  
Author(s):  
Behzad Vaferi
Keyword(s):  
Heat Transfer ◽  
Neural Networks ◽  
Artificial Neural Networks ◽  
Thermophysical Properties ◽  
Convective Heat Transfer Coefficient ◽  
Industrial Applications ◽  
Mean Square ◽  
Ann Models ◽  
Artificial Neural ◽  
Mean Square Errors

Nanofluids have recently been considered as one of the most popular working fluid in heat transfer and fluid mechanics. Accurate estimation of thermophysical properties of nanofluids is required for the investigation of their heat transfer performance. Thermal conductivity coefficient, convective heat transfer coefficient, and viscosity are some the most important thermophysical properties that directly influence on the application of nanofluids. The aim of the present chapter is to develop and validate artificial neural networks (ANNs) to estimate these thermophysical properties with acceptable accuracy. Some simple and easy measurable parameters including type of nanoparticle and base fluid, temperature and pressure, size and concentration of nanoparticles, etc. are used as independent variables of the ANN approaches. The predictive performance of the developed ANN approaches is validated with both experimental data and available empirical correlations. Various statistical indices including mean square errors (MSE), root mean square errors (RMSE), average absolute relative deviation percent (AARD%), and regression coefficient (R2) are used for numerical evaluation of accuracy of the developed ANN models. Results confirm that the developed ANN models can be regarded as a practical tool for studying the behavior of those industrial applications, which have nanofluids as operating fluid.

scholarly journals INDUSTRIAL APPLICATIONS OF ARTIFICIAL NEURAL NETWORKS

2014 ◽  
pp. 8-20
Author(s):  
Kurosh Madani
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Real World ◽  
Industrial Applications ◽  
Large Field ◽  
Nonlinear Functions ◽  
The Past ◽  
Real World Applications ◽  
Ann Models ◽  
Artificial Neural

In a large number of real world dilemmas and related applications the modeling of complex behavior is the central point. Over the past decades, new approaches based on Artificial Neural Networks (ANN) have been proposed to solve problems related to optimization, modeling, decision making, classification, data mining or nonlinear functions (behavior) approximation. Inspired from biological nervous systems and brain structure, Artificial Neural Networks could be seen as information processing systems, which allow elaboration of many original techniques covering a large field of applications. Among their most appealing properties, one can quote their learning and generalization capabilities. The main goal of this paper is to present, through some of main ANN models and based techniques, their real application capability in real world industrial dilemmas. Several examples through industrial and real world applications have been presented and discussed.

scholarly journals Application of artificial neural networks to predict the COVID-19 outbreak

2020 ◽  
Vol 5 (1) ◽  
Author(s):  
Hamid Reza Niazkar ◽  
Majid Niazkar
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Incubation Period ◽  
Prediction Models ◽  
Time Interval ◽  
Policy Makers ◽  
Ann Models ◽  
Artificial Neural ◽  
Relative Errors ◽  
Mean Square Errors

Abstract Background Millions of people have been infected worldwide in the COVID-19 pandemic. In this study, we aim to propose fourteen prediction models based on artificial neural networks (ANN) to predict the COVID-19 outbreak for policy makers. Methods The ANN-based models were utilized to estimate the confirmed cases of COVID-19 in China, Japan, Singapore, Iran, Italy, South Africa and United States of America. These models exploit historical records of confirmed cases, while their main difference is the number of days that they assume to have impact on the estimation process. The COVID-19 data were divided into a train part and a test part. The former was used to train the ANN models, while the latter was utilized to compare the purposes. The data analysis shows not only significant fluctuations in the daily confirmed cases but also different ranges of total confirmed cases observed in the time interval considered. Results Based on the obtained results, the ANN-based model that takes into account the previous 14 days outperforms the other ones. This comparison reveals the importance of considering the maximum incubation period in predicting the COVID-19 outbreak. Comparing the ranges of determination coefficients indicates that the estimated results for Italy are the best one. Moreover, the predicted results for Iran achieved the ranges of [0.09, 0.15] and [0.21, 0.36] for the mean absolute relative errors and normalized root mean square errors, respectively, which were the best ranges obtained for these criteria among different countries. Conclusion Based on the achieved results, the ANN-based model that takes into account the previous fourteen days for prediction is suggested to predict daily confirmed cases, particularly in countries that have experienced the first peak of the COVID-19 outbreak. This study has not only proved the applicability of ANN-based model for prediction of the COVID-19 outbreak, but also showed that considering incubation period of SARS-COV-2 in prediction models may generate more accurate estimations.

scholarly journals Modeling thermal properties of exotic fruits pulps: an artificial neural networks approach

2020 ◽  
Vol 9 (11) ◽  
pp. e7509119806
Author(s):  
Leandro Soares Santos ◽  
Moysés Naves de Moraes ◽  
Julia Dos Santos Lopes ◽  
Luciana Carolina Bauer ◽  
Paulo Bonomo ◽  
...  
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Correlation Coefficient ◽  
Thermophysical Properties ◽  
Data Sets ◽  
Back Propagation Algorithm ◽  
Data Set ◽  
Polynomial Models ◽  
Artificial Neural ◽  
Mean Square Errors

Thermophysical properties are important in design, simulation, optimization, and control of food processing. Its prediction is very important but theoretical basis is difficult and empirical models were commonly used. In this work, the modeling of neural networks was applied as an alternative to predict density, thermal conductivity and thermal diffusivity from the temperature and moisture content of jackfruit, genipap and umbu. Data sets from literature were used, combined and individually, to obtain four networks. Supervised multilayer perceptron networks were developed, using the back-propagation algorithm. Several configurations of artificial neural networks (ANNs) were evaluated with one or two hidden layers and a maximum of 21 and 12 neurons in each one, respectively. Data sets were divided to learning (60%) and verification (40%) steps. Best ANNs were chosen based on correlation coefficient and root mean square errors (RMSE), and compared with polynomial models using average absolute deviations (AADs). From total disposable data set, the best ANN developed presents one hidden layer with 15 neurons and shows the same predictive ability of ANNs created from individual fruits data sets, presenting close RMSE and correlation coefficient. The ANNs developed presents AADs near to polynomial models and appers as alternative to conventional modeling. Results indicate that the ANN created from total data set can replace nine polynomial models to predict the thermophysical properties of jackfruit, genipap and umbu pulps.

scholarly journals Comparison study of CFD and artificial neural networks in predicting temperature fields induced by natural convention in a square enclosure

2019 ◽  
Vol 23 (6 Part A) ◽  
pp. 3481-3492
Author(s):  
Shiyu Zhou ◽  
Xiaoping Liu ◽  
Guangyue Du ◽  
Chuanze Liu ◽  
Yucheng Zhou
Keyword(s):  
Heat Transfer ◽  
Neural Networks ◽  
Artificial Neural Networks ◽  
Natural Convection ◽  
Classical Problem ◽  
Temperature Fields ◽  
Ann Model ◽  
Artificial Neural ◽  
Relative Errors ◽  
Mean Square Errors

Natural convection in an enclosure is a classical problem in heat transfer field. In this study, natural convection induced by the heat source in the enclosure is studied with two analysis methods, i. e. CFD and artificial neural networks (ANN). The heat transfer in the enclosure is an unsteady process. During this process, the temperature fields in the enclosure are changing with time. The vertical temperature field of y = 0 at one moment is picked up for investigation. Firstly, FLUENT software which is a simulation program of CFD is adopted to simulate the temperature fields under different computation conditions. Then part of the simulation condition?s temperature data is picked for training an ANN model and the rest of data is used for validating the ANN model. It has been found from the comparison between the CFD simulation and ANN prediction that the two results have a good agreement with each other. In the comparison, the max relative errors are around 12%, mean relative errors are around 0.3%, mean square errors are around 0.6%, values of absolute fraction of variance are all not less than 0.99. The results demonstrated that the ANN prediction have enough accuracy.

scholarly journals Use of Artificial Neural Networks for Prediction of the Convective Heat Transfer Coefficient in Evaporative Mini-Tubes

2016 ◽  
Vol 17 (1) ◽  
pp. 23-34 ◽  
Author(s):  
Ricardo Romero-Méndez ◽  
Patricia Lara-Vázquez ◽  
Francisco Oviedo-Tolentino ◽  
Héctor Martín Durán-García ◽  
Francisco Gerardo Pérez-Gutiérrez ◽  
...  
Keyword(s):  
Heat Transfer ◽  
Neural Networks ◽  
Artificial Neural Networks ◽  
Heat Transfer Coefficient ◽  
Transfer Coefficient ◽  
Convective Heat Transfer ◽  
Convective Heat ◽  
Convective Heat Transfer Coefficient ◽  
Artificial Neural

Improving Pin-Fin Heat Transfer Predictions Using Artificial Neural Networks

2013 ◽  
Author(s):  
Jason K. Ostanek
Keyword(s):  
Neural Network ◽  
Heat Transfer ◽  
Neural Networks ◽  
Reynolds Number ◽  
Artificial Neural Networks ◽  
Power Law ◽  
Transfer Data ◽  
Pin Fin ◽  
The Neural Network ◽  
Artificial Neural

In much of the public literature on pin-fin heat transfer, Nusselt number is presented as a function of Reynolds number using a power-law correlation. Power-law correlations typically have an accuracy of 20% while the experimental uncertainty of such measurements is typically between 5% and 10%. Additionally, the use of power-law correlations may require many sets of empirical constants to fully characterize heat transfer for different geometrical arrangements. In the present work, artificial neural networks were used to predict heat transfer as a function of streamwise spacing, spanwise spacing, pin-fin height, Reynolds number, and row position. When predicting experimental heat transfer data, the neural network was able to predict 73% of array-averaged heat transfer data to within 10% accuracy while published power-law correlations predicted 48% of the data to within 10% accuracy. Similarly, the neural network predicted 81% of row-averaged data to within 10% accuracy while 52% of the data was predicted to within 10% accuracy using power-law correlations. The present work shows that first-order heat transfer predictions may be simplified by using a single neural network model rather than combining or interpolating between power-law correlations. Furthermore, the neural network may be expanded to include additional pin-fin features of interest such as fillets, duct rotation, pin shape, pin inclination angle, and more making neural networks expandable and adaptable models for predicting pin-fin heat transfer.

scholarly journals Applicability of Artificial Neural Networks to Predict Mechanical and Permeability Properties of Volcanic Scoria-Based Concrete

2018 ◽  
Vol 2018 ◽  
pp. 1-16 ◽  
Author(s):  
Aref M. al-Swaidani ◽  
Waed T. Khwies
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Network Architecture ◽  
Supplementary Cementitious Material ◽  
Natural Pozzolans ◽  
Long Time ◽  
Ann Models ◽  
Artificial Neural ◽  
And Performance ◽  
And Training

Numerous volcanic scoria (VS) cones are found in many places worldwide. Many of them have not yet been investigated, although few of which have been used as a supplementary cementitious material (SCM) for a long time. The use of natural pozzolans as cement replacement could be considered as a common practice in the construction industry due to the related economic, ecologic, and performance benefits. In the current paper, the effect of VS on the properties of concrete was investigated. Twenty-one concrete mixes with three w/b ratios (0.5, 0.6, and 0.7) and seven replacement levels of VS (0%, 10%, 15%, 20%, 25%, 30%, and 35%) were produced. The investigated concrete properties were the compressive strength, the water permeability, and the concrete porosity. Artificial neural networks (ANNs) were used for prediction of the investigated properties. Feed-forward backpropagation neural networks have been used. The ANN models have been established by incorporation of the laboratory experimental data and by properly choosing the network architecture and training processes. This study shows that the use of ANN models provided a more accurate tool to capture the effects of five parameters (cement content, volcanic scoria content, water content, superplasticizer content, and curing time) on the investigated properties. This prediction makes it possible to design VS-based concretes for a desired strength, water impermeability, and porosity at any given age and replacement level. Some correlations between the investigated properties were derived from the analysed data. Furthermore, the sensitivity analysis showed that all studied parameters have a strong effect on the investigated properties. The modification of the microstructure of VS-based cement paste has been observed, as well.

scholarly journals Basin-Scale Prediction of Sea Surface Temperature with Artificial Neural Networks

2018 ◽  
Vol 35 (7) ◽  
pp. 1441-1455 ◽  
Author(s):  
Kalpesh Patil ◽  
M. C. Deo
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Indian Ocean ◽  
Surface Temperature ◽  
Sea Surface Temperature ◽  
Sea Surface ◽  
Prediction Skill ◽  
Basin Scale ◽  
Ann Models ◽  
Artificial Neural

AbstractThe prediction of sea surface temperature (SST) on the basis of artificial neural networks (ANNs) can be viewed as complementary to numerical SST predictions, and it has fairly sustained in the recent past. However, one of its limitations is that such ANNs are site specific and do not provide simultaneous spatial information similar to the numerical schemes. In this work we have addressed this issue by presenting basin-scale SST predictions based on the operation of a very large number of individual ANNs simultaneously. The study area belongs to the basin of the tropical Indian Ocean (TIO) having coordinates of 30°N–30°S, 30°–120°E. The network training and testing are done on the basis of HadISST data of the past 140 yr. Monthly SST anomalies are predicted at 3813 nodes in the basin and over nine time steps into the future with more than 20 million ANN models. The network testing indicated that the prediction skill of ANNs is attractive up to certain lead times depending on the subbasin. The ANN models performed well over both the western Indian Ocean (WIO) and eastern Indian Ocean (EIO) regions up to 5 and 4 months lead time, respectively, as judged by the error statistics of the correlation coefficient and the normalized root-mean-square error. The prediction skill of the ANN models for the TIO region is found to be better than the physics-based coupled atmosphere–ocean models. It is also observed that the ANNs are capable of providing an advanced warning of the Indian Ocean dipole as well as abnormal basin warming.

scholarly journals Large-scale, high-resolution comparison of the core visual object recognition behavior of humans, monkeys, and state-of-the-art deep artificial neural networks

2018 ◽  
Author(s):  
Rishi Rajalingham ◽  
Elias B. Issa ◽  
Pouya Bashivan ◽  
Kohitij Kar ◽  
Kailyn Schmidt ◽  
...  
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
High Resolution ◽  
Large Scale ◽  
State Of The Art ◽  
Neural Mechanisms ◽  
Mechanistic Models ◽  
Object Discrimination ◽  
Ann Models ◽  
Artificial Neural

ABSTRACTPrimates—including humans—can typically recognize objects in visual images at a glance even in the face of naturally occurring identity-preserving image transformations (e.g. changes in viewpoint). A primary neuroscience goal is to uncover neuron-level mechanistic models that quantitatively explain this behavior by predicting primate performance for each and every image. Here, we applied this stringent behavioral prediction test to the leading mechanistic models of primate vision (specifically, deep, convolutional, artificial neural networks; ANNs) by directly comparing their behavioral signatures against those of humans and rhesus macaque monkeys. Using high-throughput data collection systems for human and monkey psychophysics, we collected over one million behavioral trials for 2400 images over 276 binary object discrimination tasks. Consistent with previous work, we observed that state-of-the-art deep, feed-forward convolutional ANNs trained for visual categorization (termed DCNNIC models) accurately predicted primate patterns of object-level confusion. However, when we examined behavioral performance for individual images within each object discrimination task, we found that all tested DCNNIC models were significantly non-predictive of primate performance, and that this prediction failure was not accounted for by simple image attributes, nor rescued by simple model modifications. These results show that current DCNNIC models cannot account for the image-level behavioral patterns of primates, and that new ANN models are needed to more precisely capture the neural mechanisms underlying primate object vision. To this end, large-scale, high-resolution primate behavioral benchmarks—such as those obtained here—could serve as direct guides for discovering such models.SIGNIFICANCE STATEMENTRecently, specific feed-forward deep convolutional artificial neural networks (ANNs) models have dramatically advanced our quantitative understanding of the neural mechanisms underlying primate core object recognition. In this work, we tested the limits of those ANNs by systematically comparing the behavioral responses of these models with the behavioral responses of humans and monkeys, at the resolution of individual images. Using these high-resolution metrics, we found that all tested ANN models significantly diverged from primate behavior. Going forward, these high-resolution, large-scale primate behavioral benchmarks could serve as direct guides for discovering better ANN models of the primate visual system.

Sign in / Sign up

Export Citation Format

Share Document