scholarly journals Depicting Aluminium DC Casting by Means of Dimensionless Numbers

2018 ◽  
Author(s):  
José C. Méndez ◽  
Ricardo R. Ambriz ◽  
David Jaramillo ◽  
Gabriel Plascencia
Keyword(s):  
Dimensionless Numbers ◽  
Dc Casting

GAS-LIQUID FLOW PATTERN RECOGNITION USING DIMENSIONLESS NUMBERS

2019 ◽  
Author(s):  
Caio Araujo ◽  
Tiago Ferreira Souza ◽  
Maurício Figueiredo ◽  
valdir estevam ◽  
Ana Maria Frattini Fileti
Keyword(s):  
Pattern Recognition ◽  
Flow Pattern ◽  
Liquid Flow ◽  
Dimensionless Numbers ◽  
Gas Liquid Flow

scholarly journals Machine Learning Model of Dimensionless Numbers to Predict Flow Patterns and Droplet Characteristics for Two-Phase Digital Flows

2021 ◽  
Vol 11 (9) ◽  
pp. 4251
Author(s):  
Jinsong Zhang ◽  
Shuai Zhang ◽  
Jianhua Zhang ◽  
Zhiliang Wang
Keyword(s):  
Machine Learning ◽  
Learning Algorithms ◽  
Digital Microfluidics ◽  
Flow Patterns ◽  
Machine Learning Algorithms ◽  
Dimensionless Numbers ◽  
Two Phase ◽  
The Difference ◽  
Input Variables ◽  
Digital Microfluidic

In the digital microfluidic experiments, the droplet characteristics and flow patterns are generally identified and predicted by the empirical methods, which are difficult to process a large amount of data mining. In addition, due to the existence of inevitable human invention, the inconsistent judgment standards make the comparison between different experiments cumbersome and almost impossible. In this paper, we tried to use machine learning to build algorithms that could automatically identify, judge, and predict flow patterns and droplet characteristics, so that the empirical judgment was transferred to be an intelligent process. The difference on the usual machine learning algorithms, a generalized variable system was introduced to describe the different geometry configurations of the digital microfluidics. Specifically, Buckingham’s theorem had been adopted to obtain multiple groups of dimensionless numbers as the input variables of machine learning algorithms. Through the verification of the algorithms, the SVM and BPNN algorithms had classified and predicted the different flow patterns and droplet characteristics (the length and frequency) successfully. By comparing with the primitive parameters system, the dimensionless numbers system was superior in the predictive capability. The traditional dimensionless numbers selected for the machine learning algorithms should have physical meanings strongly rather than mathematical meanings. The machine learning algorithms applying the dimensionless numbers had declined the dimensionality of the system and the amount of computation and not lose the information of primitive parameters.

Determination of the propagation state of the combustion zone during in-situ combustion by dimensionless numbers

Fuel ◽  
2021 ◽  
Vol 284 ◽  
pp. 118972
Author(s):  
Dong Liu ◽  
Junshi Tang ◽  
Ruonan Zheng ◽  
Qiang Song
Keyword(s):  
Combustion Zone ◽  
Dimensionless Numbers ◽  
In Situ Combustion

Thermomechanical Effects in DC Casting of Aluminium Alloy: A Numerical Benchmark Study

2000 ◽  
Vol 329-330 ◽  
pp. 493-500 ◽  
Author(s):  
Jean Marie Drezet ◽  
A. Burghardt ◽  
H.G. Fjaer ◽  
B. Magnin
Keyword(s):  
Aluminium Alloy ◽  
Benchmark Study ◽  
Dc Casting ◽  
Thermomechanical Effects

Effect on P-Element Modification on Microstructure of Al-Based Alloy

2014 ◽  
Vol 556-562 ◽  
pp. 620-623
Author(s):  
Xiao Ming Wang ◽  
Sheng Zhu ◽  
Jian Zhong Cui ◽  
Qing Chang ◽  
Qing Feng Zhu
Keyword(s):  
Magnesium Alloy ◽  
Optical Microscopy ◽  
P Element ◽  
Primary Crystal ◽  
Casting Method ◽  
Silicon Phase ◽  
Crystal Silicon ◽  
High Silica ◽  
Dc Casting ◽  
Processing Control

To develop high wear-resistant protective materials for magnesium alloy, high-silica Al-based alloy modified via P-element was fabricated by DC casting method. Microstructure of ingot samples was observed by using optical microscopy (OM), etc. The results demonstrated that Al-Si-0.01%P alloy with unique microstructure and without defects such as voids and rarefaction might be generated by processing control. Owing to modification by Al-P interalloy, primary crystal silicon phase in hypereutectic Al-Si alloy were refined effectively. Its size in Al-18Si-0.01%P alloy decreased from 50μm to 20μm, and distributed uniquely than that in Al-18Si alloy. Al-P acted as heterogeneous core of primary crystal silicon phase, which was the main mechanism for Al-P interalloy to modify primary crystal silicon in Al-Si alloy.

scholarly journals Prediction method of crack sensitivity during DC casting of Al–Mn and Al–Mg series aluminum alloys

2009 ◽  
Vol 59 (8) ◽  
pp. 417-423 ◽  
Author(s):  
Makoto Morishita ◽  
Mitsuhiro Abe ◽  
Kenji Tokuda ◽  
Makoto Yoshida
Keyword(s):  
Aluminum Alloys ◽  
Prediction Method ◽  
Dc Casting
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