Uncertainty quantification of the fracture properties of polymeric nanocomposites based on phase field modeling

Hamdia, Khader M.; Msekh, Mohammed A.; Silani, Mohammad; Vu-Bac, Nam; Zhuang, Xiaoying; Nguyen-Thoi, T.; Rabczuk, Timon

doi:10.1016/j.compstruct.2015.08.051

Cited by 95 publications

(23 citation statements)

References 72 publications

(69 reference statements)

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“…Latin hypercube sampling is a modified sampling method which is reliable even for fewer data. This method was used to provide planning points in input space in such a way that 1000 data were selected in inputs range . Radial design is a method that starts from a point in input spaces and a step is taken for each factor.…”

Section: Methodsmentioning

confidence: 99%

Fabricating and robust artificial neural network modeling nanoscale polyurethane fiber using electrospinning method

Hosaini‐Alvand

Mirshekar

Khorasani

et al. 2017

J of Applied Polymer Sci

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With regard to the fact that currently there is no comprehensive method to predict diameter of polyurethane/solvent fiber from electrospinning, in this study, diameter prediction of polyurethane/solvent fiber was conducted using neural networks and an error of 166 nm was observed. This error shows that artificial neural networks (ANNs) can predict diameter of electrospinning polyurethane fibers well. Then, considering weak repeatability nature of electrospinning in fabricating fibers with desired diameter, least mean square is used to improve stability of neural network model that shows an error of 113 nm, which represented better results compared to common ANN. To investigate the effect of each one of parameters affecting fiber diameter, sensitivity analysis was conducted. Along with this predicting model, sensitivity analysis can be used to reduce parameters space before conducting future studies. © 2017 Wiley Periodicals, Inc. J. Appl. Polym. Sci. 2017, 134, 45116.

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Section: Methodsmentioning

confidence: 99%

Fabricating and robust artificial neural network modeling nanoscale polyurethane fiber using electrospinning method

Hosaini‐Alvand

Mirshekar

Khorasani

et al. 2017

J of Applied Polymer Sci

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“…Manymethods have been proposed to conduct resolve uncertainty analysis [41][42][43][44][45]. In this work, the RSM approach is applied to the optimization of the index SRA and SLA.…”

Section: D-optimal Design For Response Surface Methodologymentioning

confidence: 99%

Numerical Evaluation and Optimization of Multiple Hydraulically Fractured Parameters Using a Flow-Stress-Damage Coupled Approach

Wang

et al. 2016

Energies

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Abstract:Multiple-factor analysis and optimization play a critical role in the the ability to maximizethe stimulated reservoir volume (SRV) and the success of economic shale gas production. In this paper, taking the typical continental naturally fractured silty laminae shale in China as anexample, response surface methodology (RSM) was employed to optimize multiple hydraulic fracturing parameters to maximize the stimulated area in combination with numerical modeling based on the coupled flow-stress-damage (FSD) approach. This paper demonstrates hydraulic fracturing effectiveness by defining two indicesnamelythe stimulated reservoir area (SRA) and stimulated silty laminae area (SLA). Seven uncertain parameters, such as laminae thickness, spacing, dip angle, cohesion, internal friction angle (IFA), in situ stress difference (SD), and an operational parameter-injection rate (IR) with a reasonable range based on silty Laminae Shale, Southeastern Ordos Basin, are used to fit a response of SRA and SLA as the objective function, and finally identity the optimum design under the parameters based on simultaneously maximizingSRA and SLA. In addition, asensitivity analysis of the influential factors is conducted for SRA and SLA. The aim of the study is to improve the artificial ability to control the fracturing network by means of multi-parameteroptimization. This work promises to provide insights into the effective exploitation of unconventional shale gas reservoirs via optimization of the fracturing design for continental shale, Southeastern Ordos Basin, China.

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“…Commonly atomistic and continuum models are widely used to evaluate the properties at different scales [9,10,11,12,13,14,15,16]. Some analytical approaches, such as Mori-Tanaka method (MT) [17] and Maxwell−Garnett type effective medium approximation method (MG-EMA) [18] are widely applied in prediction of thermal properties of composite material.…”

Section: Introductionmentioning

confidence: 99%

Modeling Kapitza resistance of two-phase composite material

Mortazavi

Zhuang

et al. 2016

Composite Structures

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Uncertainty quantification of the fracture properties of polymeric nanocomposites based on phase field modeling

Cited by 95 publications

References 72 publications

Fabricating and robust artificial neural network modeling nanoscale polyurethane fiber using electrospinning method

Fabricating and robust artificial neural network modeling nanoscale polyurethane fiber using electrospinning method

Numerical Evaluation and Optimization of Multiple Hydraulically Fractured Parameters Using a Flow-Stress-Damage Coupled Approach

Modeling Kapitza resistance of two-phase composite material

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