Optimization of Stress-Strain Curves of WC-Co Two-Phase Materials by Artificial Neural Networks Method

2015 ◽  
pp. 265-274
Author(s):  
Rabah Taouche
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Stress Strain ◽  
Two Phase ◽  
Artificial Neural

New void fraction equations for two-phase flow in helical heat exchangers using artificial neural networks

2018 ◽  
Vol 130 ◽  
pp. 149-160 ◽  
Author(s):  
A. Parrales ◽  
D. Colorado ◽  
J.A. Díaz-Gómez ◽  
A. Huicochea ◽  
A. Álvarez ◽  
...  
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Void Fraction ◽  
Heat Exchangers ◽  
Two Phase Flow ◽  
Phase Flow ◽  
Two Phase ◽  
Artificial Neural

scholarly journals Prediction of Equilibrium Phase, Stability and Stress-Strain Properties in Co-Cr-Fe-Ni-Al High Entropy Alloys Using Artificial Neural Networks

Metals ◽  
2020 ◽  
Vol 10 (12) ◽  
pp. 1569
Author(s):  
Nicolae Filipoiu ◽  
George Alexandru Nemnes
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Equilibrium Phase ◽  
Al Alloy ◽  
Mechanical And Thermal Properties ◽  
High Entropy Alloys ◽  
Huge Number ◽  
Stress Strain ◽  
High Entropy ◽  
Artificial Neural

High entropy alloys (HEAs) are still a largely unexplored class of materials with high potential for applications in various fields. Motivated by the huge number of compounds in a given HEA class, we develop machine learning techniques, in particular artificial neural networks, coupled to ab initio calculations, in order to accurately predict some basic HEA properties: equilibrium phase, cohesive energies, density of states at the Fermi level and the stress-strain relation, under conditions of isotropic deformations. Known for its high tensile ductility and fracture toughness, the Co-Cr-Fe-Ni-Al alloy has been considered as a test candidate material, particularly by adjusting the Al content. However, further enhancement of the microstructure, mechanical and thermal properties is possible by modifying also the fractions of the base alloy. Using deep neural networks, we map structural and chemical neighborhood information onto the quantities of interest. This approach offers the possibility for an efficient screening over a huge number of potential candidates, which is essential in the exploration of multi-dimensional compositional spaces.

Prediction of Pressure Drop of Various Refrigerants During Condensation in Horizontal Smooth and Micro-Fin Tubes by Means of Artificial Neural Networks

2012 ◽  
Author(s):  
Muhammet Balcilar ◽  
Ahmet Selim Dalkiliç ◽  
Şevket Özgür Atayılmaz ◽  
Hakan Demir ◽  
Somchai Wongwises
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Pressure Drop ◽  
Tube Length ◽  
Two Phase ◽  
Pressure Drops ◽  
Data Points ◽  
Artificial Neural ◽  
Fin Tubes ◽  
Liquid And Vapor Phases

The predictions of condensation pressure drops of R12, R22, R32, R125, R410A, R134a, R22, R502 and R507a flowing inside various horizontal smooth and micro-fin tubes are made using the numerical techniques of Artificial Neural Networks (ANNs) and non-linear least squares (NLS). The National Institute of Standards and Technology’s (NIST) experimental data and, Eckels’ and Pate’s experimental data, as presented in Choi et al.’s study provided by NIST, are used in our analyses. In their experimental setups, the horizontal test sections have 1.587 m, 3.78 m, 3.81 m and 3.97 m long countercurrent flow double tube heat exchangers with refrigerant flowing in the inner smooth (8 mm, 8.01 mm and 11.1 mm i.d.) and micro-fin (5.45 mm and 7.43 mm i.d.) copper tubes as cooling water flows in the annulus. Their test runs cover a wide range of saturation pressures from 0.9 MPa to 2.9 MPa, inlet vapor qualities range from 0.19 to 1.0 and mass fluxes are from 8 kg m−2s−1 to 791 kg m−2s−1. The condensation pressure drops are predicted using 673 measured data points, together with numerical analyses of artificial neural networks and non-linear least squares. The input of the ANNs for the best correlation are the measured and the values of the test sections are calculated, such as mass flux, tube length, inlet and outlet vapor qualities, critical pressure, latent heat of condensation, mass fraction of liquid and vapor phases, dynamic viscosities of liquid and vapor phases, hydraulic diameter, two-phase density, and the outputs of the ANNs as the experimental total pressure drops in the condensation data from independent laboratories. The total pressure drops of in-tube condensation tests are modeled using the artificial neural networks (ANNs) method of multi-layer perceptron (MLP) with a 12-40-1 architecture. The average error rate is 7.085%, considering the cross validation tests of the 867 condensation data points. A detailed model of f(MLP) is given for direct use in MATLAB. This explanation will enable users to predict the two-phase pressure drop with high accuracy. As a result of the dependency analyses, dependency of the output of the ANNs from 12 sets of input values is shown in detail, and the pressure drops of condensation in smooth and micro-fin tubes are found to be highly dependent on mass flux, all liquid Reynolds numbers, the latent heat of condensation, outlet vapor quality, critical pressure of the refrigerant, liquid dynamic viscosity, and tube length. New ANNs based empirical pressure drop correlations are developed separately for the conditions of condensation in smooth and micro-fin tubes as a result of the analyses.

ARTIFICIAL NEURAL NETWORKS IN LIQUID-LIQUID TWO-PHASE FLOW

2012 ◽  
Vol 199 (12) ◽  
pp. 1520-1542 ◽  
Author(s):  
R. Shirley ◽  
D. P. Chakrabarti ◽  
G. Das
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Two Phase Flow ◽  
Phase Flow ◽  
Two Phase ◽  
Artificial Neural

Development and Testing of Two-Phase Relative Permeability Predictors Using Artificial Neural Networks

SPE Journal ◽  
2002 ◽  
Vol 7 (03) ◽  
pp. 299-308 ◽  
Author(s):  
N. Silpngarmlers ◽  
B. Guler ◽  
T. Ertekin ◽  
A.S. Grader
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Relative Permeability ◽  
Two Phase ◽  
Artificial Neural
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