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Your search keyword '"Hung Nguyen-Xuan"' showing total 7 results
Start Over Author "Hung Nguyen-Xuan" Journal computers, materials & continua
7 results on '"Hung Nguyen-Xuan"'

1. Optimal Resource Allocation Method for Device-to-Device Communication in 5G Networks

Author

T. Vu-Huu, M. Abdel-Wahab, C. Le-Thanh, and Hung Nguyen-Xuan

Subjects
Biomaterials, Mechanics of Materials, business.industry, Computer science, Modeling and Simulation, Device to device, Resource allocation, Electrical and Electronic Engineering, business, 5G, Computer Science Applications, Computer network
Published
2022

3. Stabilization for Equal-order Polygonal Finite Element in Incompressible Fluid Flow Computation

Author

M. Abdel-Wahab, T. Vu-Huu, C. Le-Thanh, and Hung Nguyen-Xuan

Subjects
Physics, Mathematical analysis, Stokes flow, Instability, Projection (linear algebra), Finite element method, Computer Science Applications, Physics::Fluid Dynamics, Biomaterials, Flow velocity, Mechanics of Materials, Modeling and Simulation, Compressibility, Fluid dynamics, Electrical and Electronic Engineering, Pressure gradient
Abstract

This paper presents an adapted stabilisation method for the equal-order mixed scheme of finite elements on convex polygonal meshes to analyse the high velocity and pressure gradient of incompressible fluid flows that are governed by Stokes equations system. This technique is constructed by a local pressure projection which is extremely simple, yet effective, to eliminate the poor or even non-convergence as well as the instability of equal-order mixed polygonal technique. In this research, some numerical examples of incompressible Stokes fluid flow that is coded and programmed by MATLAB will be presented to examine the effectiveness of the proposed stabilised method.

Published
2020

5. A Size-Dependent Functionally Graded Higher Order Plate Analysis Based on Modified Couple Stress Theory and Moving Kriging Meshfree Method

Author

T. D. Tran, Hung Nguyen-Xuan, and Chien H. Thai

Subjects
Length scale, Isotropy, Mathematical analysis, 0211 other engineering and technologies, 02 engineering and technology, Bending, Functionally graded material, Computer Science Applications, Biomaterials, Discrete system, Mechanics of Materials, Kriging, Modeling and Simulation, 0202 electrical engineering, electronic engineering, information engineering, 020201 artificial intelligence & image processing, Boundary value problem, Virtual work, Electrical and Electronic Engineering, 021106 design practice & management, Mathematics
Abstract

A size-dependent computational approach for bending, free vibration and buckling analyses of isotropic and sandwich functionally graded (FG) microplates is in this study presented. We consider both shear deformation and small scale effects through the generalized higher order shear deformation theory and modified couple stress theory (MCST). The present model only retains a single material length scale parameter for capturing properly size effects. A rule of mixture is used to model material properties varying through the thickness of plates. The principle of virtual work is used to derive the discrete system equations which are approximated by moving Kriging interpolation (MKI) meshfree method. Numerical examples consider the inclusions of geometrical parameters, volume fraction, boundary conditions and material length scale parameter. Present results are compared with the analytical solution as well as other numerical solutions. High reliability and effectiveness of the present method is confirmed.

Published
2018

6. The Interaction between Microcapsules with Different Sizes and Propagating Cracks.

Author

Xiaoying Zhuang, Hung Nguyen-Xuan, and Shuai Zhou

Subjects
DISCRETE element method, ELASTIC modulus, CRACKING of concrete, SELF-healing materials, COMPRESSIVE strength
Abstract

The microcapsule-contained self-healing materials are appealing since they can heal the cracks automatically and be effective for a long time. Although many experiments have been carried out, the influence of the size of microcapsules on the self-healing effect is still not well investigated. This study uses the two-dimensional discrete element method (DEM) to investigate the interaction between one microcapsule and one microcrack. The influence of the size of microcapsules is considered. The potential healing time and the influence of the initial damage are studied. The results indicate that the coalescence crack is affected by the size of holes. The elastic modulus, the compressive strength and the coalescence stress decrease with the rising radius of holes. The initial damage in experiments should be greater than 95% of the compressive strength to enhance the self-healing effect. The large microcapsules require slight initial damage. Both a new type of displacement field near the crack and a new category of coalescence crack are observed. The influence of sizes of holes on the cracking behavior of concrete with a circular hole and a pre-existing crack is clarified. [ABSTRACT FROM AUTHOR]

Published
2021
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7. Forecasting Damage Mechanics By Deep Learning.

Author

Duyen Le Hien Nguyen, Dieu Thi Thanh Do, Jaehong Lee, Rabczuk, Timon, and Hung Nguyen-Xuan

Subjects
DEEP learning, HYDRAULIC fracturing, SHORT-term memory, POROUS materials, MACHINE learning, TIME series analysis, CONCEPT mapping
Abstract

We in this paper exploit time series algorithm based deep learning in forecasting damage mechanics problems. The methodologies that are able to work accurately for less computational and resolving attempts are a significant demand nowadays. Relied on learning an amount of information from given data, the long short-term memory (LSTM) method and multi-layer neural networks (MNN) method are applied to predict solutions. Numerical examples are implemented for predicting fracture growth rates of L-shape concrete specimen under load ratio, single-edge-notched beam forced by 4-point shear and hydraulic fracturing in permeable porous media problems such as storage-toughness fracture regime and fracture-height growth in Marcellus shale. The predicted results by deep learning algorithms are well-agreed with experimental data. [ABSTRACT FROM AUTHOR]

Published
2019
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