2015
DOI: 10.3844/ajeassp.2015.767.774
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Confluences among Big Data, Finite Element Analysis and High Performance Computing

Abstract: Big Data analyzes correlations from huge raw data and predicts outcomes. It has great impacts on scientific discoveries and value creation. High Performance Computing (HPC) uses parallel processing and advanced programs or software packages to complete complicated jobs quickly. Finite Element Method (FEM) is very powerful in scientific computation and engineering analysis. It has created huge values in almost every area of engineering. In a lot of applications, Finite Element Analysis (FEA) strongly relies on … Show more

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Cited by 3 publications
(2 citation statements)
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“…With the advancement of computing technology, numerical approaches are much favourable, particularly within a finite element modelling (FEM) framework (Wang et al, 2015). In earlier work, due to singularity stress occurred at the notch tip, FEM requires very fine mesh at the vicinity of the notch and further increases difficulties to model a sharp crack.…”
Section: Introductionmentioning
confidence: 99%
“…With the advancement of computing technology, numerical approaches are much favourable, particularly within a finite element modelling (FEM) framework (Wang et al, 2015). In earlier work, due to singularity stress occurred at the notch tip, FEM requires very fine mesh at the vicinity of the notch and further increases difficulties to model a sharp crack.…”
Section: Introductionmentioning
confidence: 99%
“… Aly and Abuelnasr, 2010;Farahani et al, 2010;Ahmed et al, 2010;Kunanoppadon, 2010;Helmy and El- Taweel, 2010;Qutbodin, 2010;Pattanasethanon, 2010;Fen et al, 2011;Thongwan et al, 2011; Theansuwan and Triratanasirichai, 2011; Al Smadi, 2011; Tourab et al, 2011; Raptis et al, 2011; Momani et al, 2011; Ismail et al, 2011; Anizan et al, 2011; Tsolakis and Raptis, 2011; Abdullah et al, 2011; Kechiche et al, 2011; Ho et al, 2011; Rajbhandari et al, 2011; Aleksic and Lovric, 2011; Kaewnai and Wongwises, 2011; Idarwazeh, 2011; Ebrahim et al, 2012; Abdelkrim et al, 2012; Mohan et al, 2012; Abam et al, 2012; Hassan et al, 2012; Jalil and Sampe, 2013; Jaoude and El-Tawil, 2013; Ali andShumaker, 2013;Zhao, 2013;El-Labban et al, 2013;Djalel et al, 2013; Nahas and Kozaitis, 2013;Petrescu and Petrescu, 2014a;2014b;2014c;2014d;2014e;2014f;2014g;2014h;2014i;2015a;2015b;2015c;2015d;2015e;Fu et al, 2015; Al-Nasra et al, 2015; Amer et al, 2015;Sylvester et al, 2015b;Kumar et al, 2015;Gupta et al, 2015;Stavridou et al, 2015b;Ge and Xu, 2015;Moretti, 2015;Wang et al, 2015).…”
mentioning
confidence: 99%