scholarly journals Using Perceptron Feed-Forward Artificial Neural Network (ANN) for Predicting the Thermal Conductivity of Graphene oxide-Al2O3/Water-Ethylene Glycol Hybrid Nanofluid

2021 ◽  
pp. 101055
Author(s):  
Shaopeng Tian ◽  
Noreen Izza Arshad ◽  
Davood Toghraie ◽  
S. Ali Eftekhari ◽  
Maboud Hekmatifar
Keyword(s):  
Neural Network ◽  
Thermal Conductivity ◽  
Artificial Neural Network ◽  
Graphene Oxide ◽  
Ethylene Glycol ◽  
Hybrid Nanofluid ◽  
Feed Forward ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

Prediction of the Density & Thermal Conductivity of Light Bricks on the Effect of Aluminium Elements Using Artificial Neural Network (ANN)

2019 ◽  
Vol 964 ◽  
pp. 270-279
Author(s):  
Zulkifli ◽  
Gede Panji
Keyword(s):  
Neural Network ◽  
Thermal Conductivity ◽  
Artificial Neural Network ◽  
Raw Materials ◽  
Simulation Method ◽  
Acoustic Properties ◽  
Main Element ◽  
Artificial Neural ◽  
Artificial Neural Network Ann ◽  
Element Variation

Indonesia with abundant limestone raw materials, lightweight brick is the most important component in building construction, so it needs a light brick product that qualifies in thermal, mechanical and acoustic properties. In this paper raised the lightweight brick domains that qualify on the properties of thermal conductivity as building wall components.The advantage of low light density brick (500-650 kg/m3), more economical, suitable for high rise building can reduce the weight of 30-40% in compared to conventional brick (clay brick). To obtain AAC type lightweight brick product that qualifies for low thermal and density properties to the effect of Aluminum (Al) additive element variation using artificial neural network (ANN). The composition of the main elements of lightweight brick O (29-45 % wt), Si (25-35% wt) and Ca (20-40 % wt). Mixing ratio of the main element of light brick (Ca, O and Si) with Aluminum additive element (Al), is done by simulation method of artificial neural network (ANN), Al additive element as a porosity regulator is formed. The simulation of thermal conductivity to the influence of main element variation: Ca (22-32 % wt), Si (12-33 % wt). Simulation of thermal conductivity to effect of additive Al variation (1-7 % wt). Simulation of thermal conductivity to density variation (500-1200 kg/m3). The simulated results of four AAC brick samples showed the thermal conductivity (0.145-0.192 W/m.K) to the influence of qualified Aluminum additives (2.10-6.75 % wt). Additive Al the higher the lower density value (higher porosity) additive Al smaller than 2.10 % wt does not meet the requirements in the simulation.Thermal conductivity of AAC light brick sample (0.184 W/m.K) the influence of the main elements that qualify Ca (20.32-30.35 % wt) and Si (26.57 % wt). Simulation of artificial neural network (ANN) of light brick shows that maximum allowable Si content of 26.57 % wt, Ca content is in the range 20.32-30.35 % wt, and the minimum content of aluminum in brick is light at 2.10 % wt. ANN tests performed to predict the thermal conductivity of light brick samples obtained results of the average AAC light brick thermal conductivity of 0.151 W/m.K. The best performance with Artificial Neural Network (ANN) characteristics has a validation MSE of 0.002252.

scholarly journals Application GMDH artificial neural network for modeling of Al2O3/water and Al2O3/Ethylene glycol thermal conductivity

2018 ◽  
Vol 36 (3) ◽  
pp. 773-782 ◽  
Author(s):  
Mohammad Ahmadi ◽  
Fatemeh Hajizadeh ◽  
Mohammad Rahimzadeh ◽  
Mohammad Shafii ◽  
Ali Chamkha ◽  
...  
Keyword(s):  
Neural Network ◽  
Thermal Conductivity ◽  
Artificial Neural Network ◽  
Ethylene Glycol ◽  
Artificial Neural

scholarly journals A well-trained artificial neural network for predicting the rheological behavior of MWCNT–Al2O3 (30–70%)/oil SAE40 hybrid nanofluid

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Mohammad Hemmat Esfe ◽  
S. Ali Eftekhari ◽  
Maboud Hekmatifar ◽  
Davood Toghraie
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Experimental Tests ◽  
Hybrid Nanofluid ◽  
Trial And Error ◽  
Shear Rates ◽  
Artificial Neural ◽  
Artificial Neural Network Ann ◽  
Hidden Layer ◽  
Increasing Temperature

AbstractIn this study, the influence of different volume fractions ($$\phi$$ ϕ ) of nanoparticles and temperatures on the dynamic viscosity ($$\mu_{nf}$$ μ nf ) of MWCNT–Al2O3 (30–70%)/oil SAE40 hybrid nanofluid was examined by ANN. For this reason, the $$\mu_{nf}$$ μ nf was derived for 203 various experiments through a series of experimental tests, including a combination of 7 different $$\phi$$ ϕ , 6 various temperatures, and 5 shear rates. These data were then used to train an artificial neural network (ANN) to generalize results in the predefined ranges for two input parameters. For this reason, a feed-forward perceptron ANN with two inputs (T and $$\phi$$ ϕ ) and one output ($$\mu_{nf}$$ μ nf ) was used. The best topology of the ANN was determined by trial and error, and a two-layer with 10 neurons in the hidden layer with the tansig function had the best performance. A well-trained ANN is created using the trainbr algorithm and showed an MSE value of 4.3e−3 along 0.999 as a correlation coefficient for predicting $$\mu_{nf}$$ μ nf . The results show that an increase $$\phi$$ ϕ has a significant effect on $$\mu_{nf}$$ μ nf value. As $$\phi$$ ϕ increases, the viscosity of this nanofluid increases at all temperatures. On the other hand, with increasing temperature, the viscosity of this nanofluid decreases. Based on all of the diagrams presented for the trained ANNs, we can conclude that a well-trained ANN can be used as an approximating function for predicting the $$\mu_{nf}$$ μ nf .

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