Prediction and modeling of MWCNT/Carbon (60/40)/SAE 10 W 40/SAE 85 W 90(50/50) nanofluid viscosity using artificial neural network (ANN) and self-organizing map (SOM)

Maddah, Heydar; Aghayari, Reza; Ahmadi, Mohammad Hossein; Rahimzadeh, Mohammad; Ghasemi, Nahid

doi:10.1007/s10973-018-7827-1

Cited by 52 publications

(16 citation statements)

References 38 publications

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“…In the last decades this tool has been gaining space in problems solving in different areas as in the investigation of the relative viscosity of multi-walled carbon nanotube (Maddah et al, 2018), prediction of chemical elements of the periodic table (Lemes & Dal Pino, 2011) and waterlogging risk assessment (Lai et al, 2017). Cremasco et al (2019) studied the application of artificial neural networks, SOM type, for the diffusion of inorganic ions in quail egg.…”

Section: Introductionmentioning

confidence: 99%

Application of artificial neural networks in the study of Mozzarella cheese salting

et al. 2021

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A highly efficient tool that has stood out in data processing and treatment is Artificial Neural Networks (ANNs). Self-organized maps (SOM), which is a type of ANNs, are easy-to-view computational models that simulate information processing and knowledge acquisition. Applying this tool, we analyzed the behavior of the film formed on the mozzarella cheese surface when subjected to salting by immersion. In order to reduce the amount of NaCl in the food, KCl was used as a partial substitute in adequate amounts, without causing sensory changes in the cheese. The diffusion of salts in the brine is caused by the transfer of sodium and potassium ions, induced by concentration gradient of these salts inside and outside the food. The application of artificial neural networks proved to be an effective tool in the evaluation of the influence of the film on the diffusion process of Na + and K + ions, in the mozzarella cheese salting.

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Section: Introductionmentioning

confidence: 99%

Application of artificial neural networks in the study of Mozzarella cheese salting

et al. 2021

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“…As an illustration, it is practicable to reduce system size or enhance the thermal performance of materials [5][6][7][8]. In this way, some investigations have been implemented on the use of nanotechnology in thermal applications [9][10][11][12][13][14][15][16][17][18][19]. Additionally, some studies 2 of 14 have focused on the prediction of the thermal conductivity ratio associated with various nanofluids with the help of using experiments and artificial neural networks [20][21][22][23][24][25][26][27][28][29][30][31].…”

Section: Introductionmentioning

confidence: 99%

Prediction of Thermo-Physical Properties of TiO2-Al2O3/Water Nanoparticles by Using Artificial Neural Network

et al. 2020

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In this paper, an artificial neural network is implemented for the sake of predicting the thermal conductivity ratio of TiO2-Al2O3/water nanofluid. TiO2-Al2O3/water in the role of an innovative type of nanofluid was synthesized by the sol–gel method. The results indicated that 1.5 vol.% of nanofluids enhanced the thermal conductivity by up to 25%. It was shown that the heat transfer coefficient was linearly augmented with increasing nanoparticle concentration, but its variation with temperature was nonlinear. It should be noted that the increase in concentration may cause the particles to agglomerate, and then the thermal conductivity is reduced. The increase in temperature also increases the thermal conductivity, due to an increase in the Brownian motion and collision of particles. In this research, for the sake of predicting the thermal conductivity of TiO2-Al2O3/water nanofluid based on volumetric concentration and temperature functions, an artificial neural network is implemented. In this way, for predicting thermal conductivity, SOM (self-organizing map) and BP-LM (Back Propagation-Levenberq-Marquardt) algorithms were used. Based on the results obtained, these algorithms can be considered as an exceptional tool for predicting thermal conductivity. Additionally, the correlation coefficient values were equal to 0.938 and 0.98 when implementing the SOM and BP-LM algorithms, respectively, which is highly acceptable.

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“…Additionally, pulsating heat pipes' thermal resistance has been estimated by Ahmadi et al [4] with the help of an ANN. In addition to these, some investigations have been done on evaluating the thermal performance of various applications with the help of neural networks [5][6][7][8]. Among these applications, artificial neural networks have been widely used in drying processes and other processes [9][10][11][12][13][14][15][16].…”

Section: Introductionmentioning

confidence: 99%

Moisture Estimation in Cabinet Dryers with Thin-Layer Relationships Using a Genetic Algorithm and Neural Network

Maleki¹,

Ghazvini

Ahmadi

et al. 2019

Mathematics

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Nowadays, industrial dryers are used instead of traditional methods for drying. When designing dryers suitable for controlling the process of drying and reaching a high-quality product, it is necessary to predict the gradual moisture loss during drying. Few studies have been conducted to compare thin-layer models and artificial neural network models on the kinetics of pistachio drying in a cabinet dryer. For this purpose, ten mathematical-experimental models with a neural network model based on the kinetic data of pistachio drying were studied. The data obtained was from a cabinet dryer evaluated at four temperatures of inlet air and different air velocities. The pistachio seeds were placed in a thin layer on an aluminum sheet on a drying tray and weighed by a scale attached to the computer at different times. In the neural network, data was divided into three parts: Educational (60%), validation (20%) and testing (20%). Finally, the best mathematical-experimental model using a genetic algorithm and the best neural network structure for predicting instantaneous moisture were selected based on the least squared error and the highest correlation coefficient.

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Prediction and modeling of MWCNT/Carbon (60/40)/SAE 10 W 40/SAE 85 W 90(50/50) nanofluid viscosity using artificial neural network (ANN) and self-organizing map (SOM)

Cited by 52 publications

References 38 publications

Application of artificial neural networks in the study of Mozzarella cheese salting

Application of artificial neural networks in the study of Mozzarella cheese salting

Prediction of Thermo-Physical Properties of TiO2-Al2O3/Water Nanoparticles by Using Artificial Neural Network

Moisture Estimation in Cabinet Dryers with Thin-Layer Relationships Using a Genetic Algorithm and Neural Network

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