Home  About us  Contact
 

Effective Properties (effective + property)

Distribution by Scientific Domains
Polymers and Materials Science77%
Engineering22%

Selected Abstracts

Rapid Exponential Convergence of Finite Element Estimates of the Effective Properties of Heterogeneous Materials

ADVANCED ENGINEERING MATERIALS, Issue 11 2007
A. Gusev
We develop and validate a general-purpose error estimator for the finite element solutions for the effective properties of heterogeneous materials. We show that the error should decrease exponentially upon increasing order of the polynomial interpolation. We use this finding to demonstrate the practical feasibility of reliable property predictions for a majority of particulate-morphology heterogeneous materials. [source]

Asymptotic Back Strain Approach for Estimation of Effective Properties of Multiphase Materials

ADVANCED ENGINEERING MATERIALS, Issue 1-2 2007
A. Gusev
Estimation of the effective properties of composite materials from those of the constituents and the material's morphology is a classical problem of both theoretical and technological interest. In this work, the authors have introduced an asymptotic back strain finite element approach for numerical estimation of effective properties of multiphase materials. The proposed approach should open an appealing pathway to rational and effective computer aided design of random microstructure composite materials. [source]

Cover Picture: Anisotropy and Dynamic Ranges in Effective Properties of Sheared Nematic Polymer Nanocomposites (Adv. Funct.

ADVANCED FUNCTIONAL MATERIALS, Issue 12 2005
Mater.
Abstract Forest and co-workers report on p.,2029 that nematic polymer nanocomposite (NPNC) films can be processed in steady shear flows, which generate complex orientational distributions of the nanorod inclusions. Distribution functions for a benchmark NPNC (11,vol.-% of 1,nm,×,200,nm rods) are computed for a range of shear rates, yielding a bifurcation diagram with steady states at very low (logrolling) and high (flow-aligning) shear rates, and limit cycles (tumbling, wagging, kayaking) at intermediate shear rates. The orientational distributions dictate the effective conductivity tensor of the NPNC film, which is computed for all distribution functions, and extract the maximum principal conductivity enhancement (Emax, averaged in time for periodic distributions) relative to the matrix. The result is a "property bifurcation diagram" for NPNC films, which predicts an optimal shear rate that maximizes Emax. Nematic, or liquid-crystalline, polymer nanocomposites (NPNCs) are composed of large aspect ratio, rod-like or platelet, rigid macromolecules in a matrix or solvent, which itself may be aqueous or polymeric. NPNCs are engineered for high-performance material applications, ranging across mechanical, electrical, piezoelectric, thermal, and barrier properties. The rods or platelets possess enormous property contrasts relative to the solvent, yet the composite properties are strongly affected by the orientational distribution of the nanophase. Nematic polymer film processing flows are shear-dominated, for which orientational distributions are well known to be highly sensitive to shear rate and volume fraction of the nematogens, with unsteady response being the most expected outcome at typical low shear rates and volume fractions. The focus of this article is a determination of the ranges of anisotropy and dynamic fluctuations in effective properties arising from orientational probability distribution functions generated by steady shear of NPNC monodomains. We combine numerical databases for sheared monodomain distributions[1,2] of thin rod or platelet dispersions together with homogenization theory for low-volume-fraction spheroidal inclusions[3] to calculate effective conductivity tensors of steady and oscillatory sheared mesophases. We then extract maximum scalar conductivity enhancement and anisotropy for each type of sheared monodomain (flow-aligned, tumbling, kayaking, and chaotic). [source]

Rapid Exponential Convergence of Finite Element Estimates of the Effective Properties of Heterogeneous Materials

ADVANCED ENGINEERING MATERIALS, Issue 11 2007
A. Gusev
We develop and validate a general-purpose error estimator for the finite element solutions for the effective properties of heterogeneous materials. We show that the error should decrease exponentially upon increasing order of the polynomial interpolation. We use this finding to demonstrate the practical feasibility of reliable property predictions for a majority of particulate-morphology heterogeneous materials. [source]

Asymptotic Back Strain Approach for Estimation of Effective Properties of Multiphase Materials

ADVANCED ENGINEERING MATERIALS, Issue 1-2 2007
A. Gusev
Estimation of the effective properties of composite materials from those of the constituents and the material's morphology is a classical problem of both theoretical and technological interest. In this work, the authors have introduced an asymptotic back strain finite element approach for numerical estimation of effective properties of multiphase materials. The proposed approach should open an appealing pathway to rational and effective computer aided design of random microstructure composite materials. [source]

Cover Picture: Anisotropy and Dynamic Ranges in Effective Properties of Sheared Nematic Polymer Nanocomposites (Adv. Funct.

ADVANCED FUNCTIONAL MATERIALS, Issue 12 2005
Mater.
Abstract Forest and co-workers report on p.,2029 that nematic polymer nanocomposite (NPNC) films can be processed in steady shear flows, which generate complex orientational distributions of the nanorod inclusions. Distribution functions for a benchmark NPNC (11,vol.-% of 1,nm,×,200,nm rods) are computed for a range of shear rates, yielding a bifurcation diagram with steady states at very low (logrolling) and high (flow-aligning) shear rates, and limit cycles (tumbling, wagging, kayaking) at intermediate shear rates. The orientational distributions dictate the effective conductivity tensor of the NPNC film, which is computed for all distribution functions, and extract the maximum principal conductivity enhancement (Emax, averaged in time for periodic distributions) relative to the matrix. The result is a "property bifurcation diagram" for NPNC films, which predicts an optimal shear rate that maximizes Emax. Nematic, or liquid-crystalline, polymer nanocomposites (NPNCs) are composed of large aspect ratio, rod-like or platelet, rigid macromolecules in a matrix or solvent, which itself may be aqueous or polymeric. NPNCs are engineered for high-performance material applications, ranging across mechanical, electrical, piezoelectric, thermal, and barrier properties. The rods or platelets possess enormous property contrasts relative to the solvent, yet the composite properties are strongly affected by the orientational distribution of the nanophase. Nematic polymer film processing flows are shear-dominated, for which orientational distributions are well known to be highly sensitive to shear rate and volume fraction of the nematogens, with unsteady response being the most expected outcome at typical low shear rates and volume fractions. The focus of this article is a determination of the ranges of anisotropy and dynamic fluctuations in effective properties arising from orientational probability distribution functions generated by steady shear of NPNC monodomains. We combine numerical databases for sheared monodomain distributions[1,2] of thin rod or platelet dispersions together with homogenization theory for low-volume-fraction spheroidal inclusions[3] to calculate effective conductivity tensors of steady and oscillatory sheared mesophases. We then extract maximum scalar conductivity enhancement and anisotropy for each type of sheared monodomain (flow-aligned, tumbling, kayaking, and chaotic). [source]

About Darcy's law in non-Galilean frame

INTERNATIONAL JOURNAL FOR NUMERICAL AND ANALYTICAL METHODS IN GEOMECHANICS, Issue 3 2004
C. Geindreau
Abstract This paper is aimed towards investigating the filtration law of an incompressible viscous Newtonian fluid through a rigid non-inertial porous medium (e.g. a porous medium placed in a centrifuge basket). The filtration law is obtained by upscaling the flow equations at the pore scale. The upscaling technique is the homogenization method of multiple scale expansions which rigorously gives the macroscopic behaviour and the effective properties without any prerequisite on the form of the macroscopic equations. The derived filtration law is similar to Darcy's law, but the tensor of permeability presents the following remarkable properties: it depends upon the angular velocity of the porous matrix, it verifies Hall,Onsager's relationship and it is a non-symmetric tensor. We thus deduce that, under rotation, an isotropic porous medium leads to a non-isotropic effective permeability. In this paper, we present the results of numerical simulations of the flow through rotating porous media. This allows us to highlight the deviations of the flow due to Coriolis effects at both the microscopic scale (i.e. the pore scale), and the macroscopic scale (i.e. the sample scale). The above results confirm that for an isotropic medium, phenomenological laws already proposed in the literature fails at reproducing three-dimensional Coriolis effects in all types of pores geometry. We show that Coriolis effects may lead to significant variations of the permeability measured during centrifuge tests when the inverse Ekman number Ek,1 is ,,(1). These variations are estimated to be less than 5% if Ek,1<0.2, which is the case of classical geotechnical centrifuge tests. We finally conclude by showing that available experimental data from tests carried out in centrifuges are not sufficient to determining the effective tensor of permeability of rotating porous media. Copyright © 2004 John Wiley & Sons, Ltd. [source]

A random field model for generating synthetic microstructures of functionally graded materials

INTERNATIONAL JOURNAL FOR NUMERICAL METHODS IN ENGINEERING, Issue 7 2008
Sharif Rahman
Abstract This article presents a new level-cut, inhomogeneous, filtered Poisson random field model for representing two-phase microstructures of statistically inhomogeneous, functionally graded materials with fully penetrable embedded particles. The model involves an inhomogeneous, filtered Poisson random field comprising a sum of deterministic kernel functions that are scaled by random variables and a cut of the filtered Poisson field above a specified level. The resulting level-cut field depends on the Poisson intensity, level, kernel functions, random scaling variables, and random rotation matrices. A reconstruction algorithm including model calibration and Monte Carlo simulation is presented for generating samples of two-phase microstructures of statistically inhomogeneous media. Numerical examples demonstrate that the model developed is capable of producing a wide variety of two- and three-dimensional microstructures of functionally graded composites containing particles of various sizes, shapes, densities, gradations, and orientations. An example involving finite element analyses of random microstructures, leading to statistics of effective properties of functionally graded composites, illustrates the usefulness of the proposed model. Copyright © 2008 John Wiley & Sons, Ltd. [source]

Mechanical properties of polypropylene matrix composites reinforced with natural fibers: A statistical approach

POLYMER COMPOSITES, Issue 1 2004
J. Biagiotti
This work presents a systematic and statistical approach to evaluate and predict the properties of random discontinuous natural fiber reinforced composites. Different composites based on polypropylene and reinforced with natural fibers were produced and their mechanical properties are measured together with the distribution of the fiber size and the fiber diameter. The values obtained were related to the theoretical predictions, using a combination of the Griffith theory for the effective properties of the natural fibers and the Halpin-Tsai equation for the elastic modulus of the composites. The relationships between experimental results and theoretical predictions were statistically analyzed using a probability density function estimation approach based on neural networks. The results show a more accurate expected value with respect to the traditional statistical function estimation approach. In order to point out the particular features of natural fibers, the same proposed method is also applied to PP,glass fiber composites. [source]