scholarly journals Numerical Investigation of the Use of a New Nano-Particle in Microchannel

2021 ◽  
Author(s):  
Fuat Kaya
Keyword(s):  
Neural Network ◽  
Heat Transfer ◽  
Artificial Neural Network ◽  
Boron Nitride ◽  
Nano Particle ◽  
Flow And Heat Transfer ◽  
Comparison Research ◽  
Artificial Neural ◽  
Artificial Neural Network Ann ◽  
Good Agreement

Abstract The purpose of this paper is to study the effects of the use of Boron nitride (BN) as nano-particle on pressure drop and heat transfer in a microchannel. The governing equations for the fluid flow were solved by using Fluent CFD code and artificial neural network (ANN). Computational results acquired from Fluent CFD code and artificial neural network (ANN) for alumina (Al2O3) as nano-particle were compared with numerical values obtained in the literature for validation. On the basis of a water-cooled (only water, water+alumina and water+boron nitride) smooth microchannel were designed, and then the corresponding laminar flow and heat transfer were studied numerically. Results derived from the numerical tests (NT) and artificial neural network (ANN) show good agreement with the values mentioned in the literature and these results also show by the comparison research which was conducted considering the heat transfer and pressure loss parameters between BN and widely used alumina that BN is more convenient nano-particle.

Prediction of Surface Roughness and Coefficient of Friction Using Artificial Neural Network in Tribotesting of Bio-Lubricants

2019 ◽  
Vol 895 ◽  
pp. 52-57 ◽  
Author(s):  
Prasanna Vineeth Bharadwaj ◽  
T.P. Jeevan ◽  
P.S. Suvin ◽  
S.R. Jayaram
Keyword(s):  
Neural Network ◽  
Surface Roughness ◽  
Artificial Neural Network ◽  
Coefficient Of Friction ◽  
Ann Model ◽  
Artificial Neural ◽  
Artificial Neural Network Ann ◽  
Trained Neural Network ◽  
Lubrication Conditions ◽  
Good Agreement

Tribotesting is necessary to understand the behaviour of the material under various operating lubrication conditions. This paper deals with the training of an artificial neural network (ANN) model with Bio-lubricant properties and machining conditions for prediction of surface roughness and coefficient of friction in Tribotesting by Tool chip Tribometer. Experimental results obtained from Tool chip tribometer for tested bio-lubricants are compared with those obtained by ANN prediction. A good agreement in results recommends that a well trained neural network is competent enough to predict the parameters in Tribotesting process.

Prediction of Detonation Pressure of Aluminized Explosive by Artificial Neural Network

2013 ◽  
Vol 641-642 ◽  
pp. 460-463
Author(s):  
Yong Gang Liu ◽  
Xin Tian ◽  
Yue Qiang Jiang ◽  
Gong Bing Li ◽  
Yi Zhou Li
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Optimal Condition ◽  
Detonation Pressure ◽  
Ann Model ◽  
Training Set ◽  
The Neural Network ◽  
Artificial Neural ◽  
Artificial Neural Network Ann ◽  
Good Agreement

In this study, a three-layer artificial neural network(ANN) model was constructed to predict the detonation pressure of aluminized explosive. Elemental composition and loading density were employed as input descriptors and detonation pressure was used as output. The dataset of 41 aluminized explosives was randomly divided into a training set (30) and a prediction set (11). After optimized by adjusting various parameters, the optimal condition of the neural network was obtained. Simulated with the final optimum neural network [6–9–1], calculated detonation pressures show good agreement with experimental results. It is shown here that ANN is able to produce accurate predictions of the detonation pressure of aluminized explosive.

Prediction of Detonation Velocity of Aluminized Explosive by Artificial Neural Network

2013 ◽  
Vol 790 ◽  
pp. 673-676
Author(s):  
Yue Qiang Jiang ◽  
Yong Gang Liu ◽  
Xin Tian ◽  
Gong Bing Li
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Optimal Condition ◽  
Detonation Velocity ◽  
Ann Model ◽  
Training Set ◽  
The Neural Network ◽  
Artificial Neural ◽  
Artificial Neural Network Ann ◽  
Good Agreement

In this study, a three-layer artificial neural network (ANN) model was constructed to predict the detonation velocity of aluminized explosive. Elemental composition and loading density were employed as input descriptors and detonation velocity was used as output. The dataset of 61 aluminized explosives was randomly divided into a training set (49) and a prediction set (12). After optimized by adjusting various parameters, the optimal condition of the neural network was obtained. Simulated with the final optimum neural network [812, calculated detonation velocity show good agreement with experimental results. It is shown that ANN is able to produce accurate predictions of the detonation velocity of aluminized explosive.

Prediction of Post-Dryout Heat Transfer in Vertical Annular Channels Using Artificial Neural Network Method

2008 ◽  
Author(s):  
D. W. Zhao ◽  
G. H. Su ◽  
S. Z. Qiu ◽  
W. X. Tian
Keyword(s):  
Neural Network ◽  
Heat Transfer ◽  
Artificial Neural Network ◽  
Nusselt Number ◽  
Annular Channel ◽  
Experimental Investigations ◽  
Gap Size ◽  
Annular Channels ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

Experimental investigations on post-dryout heat transfer in 10×8.1, 10×7 and 10×6mm annular test sections have been carried out under low-pressure and low mass flow rate conditions. An Artificial Neural Network (ANN) was trained successfully based on the experimental data for predicting the average post-dryout Nusselt number. Based on the ANN, the effects of gap size, pressure, steam Reynolds number, Reg, inlet quality, xi, Prandtl number, (Prg)W, and the ratio of heat flux of inner-tube to that of outer-tube, qi/qo, on post-dryout heat transfer were analyzed, respectively. In present study, Nusselt number in annular channels with big gap size is larger than that in annular channels with small gap size. Nusselt number increases significantly in 1.5mm and 2.0mm annular channels while it is almost constant in 0.95mm annular channel with increasing pressure or qi/qo. Nusselt number increases with Reg in case of 0.95mm and 1.5mm gap sizes. However, Nusselt number in 2.0mm annular channel firstly increases and then decreases with increasing Reg. Nusselt number decreases with increasing inlet quality under all three annular channels condition. Nusselt number decreases significantly with increasing (Prg)W when (Prg)W is less than 1.5. The changes of Nusselt number in 1.5mm or 2.0mm annular channels are larger than that in 0.95mm annular channel.

Artificial-Neural-Network Prediction of Device Behaviors in Submicron MOS Transistors

2013 ◽  
Vol 634-638 ◽  
pp. 2442-2445
Author(s):  
Shen Li Chen ◽  
Ying Der Chen
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Fuzzy Theory ◽  
Mos Transistors ◽  
Neural Network Prediction ◽  
Network Prediction ◽  
Artificial Neural ◽  
A New Technique ◽  
Artificial Neural Network Ann ◽  
Good Agreement

A new technique is presented for modeling submicron MOSFET devices and predicting the MOSFET device behaviors by using fuzzy theory and artificial neural network (ANN). The power of ANNs used as a realization of I-V characterizations is demonstrated on the submicron MOS transistors. The prediction results are compared with experimental data of the actual devices and obtained a good agreement under different bias situations.

scholarly journals A Generalized ANN Model for Analyzing and Synthesizing Rectangular, Circular, and Triangular Microstrip Antennas

2013 ◽  
Vol 2013 ◽  
pp. 1-9 ◽  
Author(s):  
Taimoor Khan ◽  
Asok De
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Microstrip Antennas ◽  
Computational Technique ◽  
Performance Parameters ◽  
Ann Model ◽  
Ann Models ◽  
Artificial Neural ◽  
Artificial Neural Network Ann ◽  
Good Agreement

Since last one decade, artificial neural network (ANN) models have been used as fast computational technique for different performance parameters of microstrip antennas. Recently, the concept of creating a generalized neural approach for different performance parameters has been motivated in microstrip antennas. This paper illustrates a generalized neural approach for analyzing and synthesizing the rectangular, circular, and triangular MSAs, simultaneously. Such approach is very much required for the antenna designers for getting instant answer for the required parameters. Here, total seven performance parameters of three different MSAs are computed using generalized neural approach as such a method is rarely available in the open literature even for computing more than three performance parameters, simultaneously. The results thus obtained are in very good agreement with the measured results available in the referenced literature for all seven cases.

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