Elastic Behavior of Laminate and Sandwich Composites

2004 ◽  
pp. 91-160
Author(s):  
Holm Altenbach ◽  
Johannes Altenbach ◽  
Wolfgang Kissing
Keyword(s):  
Elastic Behavior ◽  
Sandwich Composites

Elastic Behavior of Laminate and Sandwich Composites

2018 ◽  
pp. 103-176
Author(s):  
Holm Altenbach ◽  
Johannes Altenbach ◽  
Wolfgang Kissing
Keyword(s):  
Elastic Behavior ◽  
Sandwich Composites

Electron Microscopy of Metal-Matrix Composites

1970 ◽  
Vol 28 ◽  
pp. 404-405
Author(s):  
A. Lawley ◽  
M. R. Pinnel ◽  
A. Pattnaik
Keyword(s):  
Electron Microscopy ◽  
Metal Matrix Composites ◽  
Metal Matrix ◽  
Critical Evaluation ◽  
Elastic Behavior ◽  
Matrix Composites ◽  
Directionally Solidified ◽  
Fracture Characteristics ◽  
Broad Program

As part of a broad program on composite materials, the role of the interface on the micromechanics of deformation of metal-matrix composites is being studied. The approach is to correlate elastic behavior, micro and macroyielding, flow, and fracture behavior with associated structural detail (dislocation substructure, fracture characteristics) and stress-state. This provides an understanding of the mode of deformation from an atomistic viewpoint; a critical evaluation can then be made of existing models of composite behavior based on continuum mechanics. This paper covers the electron microscopy (transmission, fractography, scanning microscopy) of two distinct forms of composite material: conventional fiber-reinforced (aluminum-stainless steel) and directionally solidified eutectic alloys (aluminum-copper). In the former, the interface is in the form of a compound and/or solid solution whereas in directionally solidified alloys, the interface consists of a precise crystallographic boundary between the two constituents of the eutectic.

Dynamics of Nanoparticle Chain Aggregates of Carbon Under Tension

2003 ◽  
Vol 778 ◽  
Author(s):  
Rajdip Bandyopadhyaya ◽  
Weizhi Rong ◽  
Yong J. Suh ◽  
Sheldon K. Friedlander
Keyword(s):  
Mechanical Properties ◽  
Carbon Black ◽  
Polymer Film ◽  
Elastic Behavior ◽  
Small Strains ◽  
Transmission Electron ◽  
Chain Aggregates ◽  
Nanoparticle Chains ◽  
Reinforcing Filler

AbstractCarbon black in the form of nanoparticle chains is used as a reinforcing filler in elastomers. However, the dynamics of the filler particles under tension and their role in the improvement of the mechanical properties of rubber are not well understood. We have studied experimentally the dynamics of isolated nanoparticle chain aggregates (NCAs) of carbon made by laser ablation, and also that of carbon black embedded in a polymer film. In situ studies of stretching and contraction of such chains in the transmission electron microscope (TEM) were conducted under different maximum values of strain. Stretching causes initially folded NCA to reorganize into a straight, taut configuration. Further stretching leads to either plastic deformation and breakage (at 37.4% strain) or to a partial elastic behavior of the chain at small strains (e.g. 2.3% strain). For all cases the chains were very flexible under tension. Similar reorientation and stretching was observed for carbon black chains embedded in a polymer film. Such flexible and elastic nature of NCAs point towards a possible mechanism of reinforcement of rubber by carbon black fillers.

On the Post-Elastic Behavior of LRPH Connections

2019 ◽  
Vol 12 (6) ◽  
pp. 341
Author(s):  
Salvatore Benfratello ◽  
Luigi Palizzolo ◽  
Pietro Tabbuso ◽  
Santo Vazzano
Keyword(s):  
Elastic Behavior

Stochastic Modeling of Elastic Behavior of Fibrous Ablators

2021 ◽  
Author(s):  
Mujan Seif ◽  
Sean McDaniel ◽  
Matthew Beck ◽  
Alexandre Martin
Keyword(s):  
Stochastic Modeling ◽  
Elastic Behavior

Elastic behavior, phase transition, and pressure induced structural evolution of analcime

2006 ◽  
Vol 91 (4) ◽  
pp. 568-578 ◽  
Author(s):  
G. D. Gatta ◽  
F. Nestola ◽  
T. B. Ballaran
Keyword(s):  
Phase Transition ◽  
Structural Evolution ◽  
Elastic Behavior

scholarly journals A Hybrid Artificial Intelligence Model to Predict the Elastic Behavior of Sandstone Rocks

2019 ◽  
Vol 11 (19) ◽  
pp. 5283 ◽  
Author(s):  
Gowida ◽  
Moussa ◽  
Elkatatny ◽  
Ali
Keyword(s):  
Poisson’S Ratio ◽  
Accurate Determination ◽  
Oil And Gas Industry ◽  
Poisson's Ratio ◽  
Elastic Behavior ◽  
Percentage Error ◽  
Ann Model ◽  
Field Development ◽  
Well Completion

Rock mechanical properties play a key role in the optimization process of engineering practices in the oil and gas industry so that better field development decisions can be made. Estimation of these properties is central in well placement, drilling programs, and well completion design. The elastic behavior of rocks can be studied by determining two main parameters: Young’s modulus and Poisson’s ratio. Accurate determination of the Poisson’s ratio helps to estimate the in-situ horizontal stresses and in turn, avoid many critical problems which interrupt drilling operations, such as pipe sticking and wellbore instability issues. Accurate Poisson’s ratio values can be experimentally determined using retrieved core samples under simulated in-situ downhole conditions. However, this technique is time-consuming and economically ineffective, requiring the development of a more effective technique. This study has developed a new generalized model to estimate static Poisson’s ratio values of sandstone rocks using a supervised artificial neural network (ANN). The developed ANN model uses well log data such as bulk density and sonic log as the input parameters to target static Poisson’s ratio values as outputs. Subsequently, the developed ANN model was transformed into a more practical and easier to use white-box mode using an ANN-based empirical equation. Core data (692 data points) and their corresponding petrophysical data were used to train and test the ANN model. The self-adaptive differential evolution (SADE) algorithm was used to fine-tune the parameters of the ANN model to obtain the most accurate results in terms of the highest correlation coefficient (R) and the lowest mean absolute percentage error (MAPE). The results obtained from the optimized ANN model show an excellent agreement with the laboratory measured static Poisson’s ratio, confirming the high accuracy of the developed model. A comparison of the developed ANN-based empirical correlation with the previously developed approaches demonstrates the superiority of the developed correlation in predicting static Poisson’s ratio values with the highest R and the lowest MAPE. The developed correlation performs in a manner far superior to other approaches when validated against unseen field data. The developed ANN-based mathematical model can be used as a robust tool to estimate static Poisson’s ratio without the need to run the ANN model.

A multiscale homogenization procedure to predict the elasto-viscoplastic behavior of polymer-based nanocomposites

2021 ◽  
pp. 096739112110233
Author(s):  
Mohammad Hassan Shojaeefard ◽  
Abolfazl Khalkhali ◽  
Sharif Khakshournia
Keyword(s):  
Design Procedure ◽  
Polymeric Matrix ◽  
Elastic Behavior ◽  
Algorithm Optimization ◽  
Multiphase Materials ◽  
Rate Dependent ◽  
Main Challenge ◽  
Compressive Loads ◽  
Structural Parts ◽  
The Impact

It has been demonstrated that adding a few percent of nanoscale reinforcements, leads to remarkable improvement in mechanical properties of the polymers such as stiffness, damping, and energy absorption. These lightweight materials are attractive substitutes for the heavy metallic structural parts in the automotive, military, aerospace and many other industries. However, due to complexity of these multiphase materials, accurate modeling of their behavior in real loading cases is still ambiguous. The impact simulation is a vital step in design procedure of a vehicle, where a strain rate-dependent model of its components is required. In this paper, an elasto-viscoplastic modeling procedure of the polymer-based nanocomposites, assuming the elastic behavior of the nano-phase is presented; whereas the polymeric matrix deformation is dependent to the loading rate and is characterized by the method of Genetic algorithm optimization-based fitting to the experimental observations. By introducing a modified Halpin-Tsai method, the nanocomposite is then modeled as a homogenized material where the modification algorithm is the main challenge. A combination of approaches including parametric analysis, central composite design of experiments and response surface method is proposed to modify the tangent modulus of the polymeric matrix to be passed as the input to the Halpin-Tsai equations. Finally, the procedure is implemented to a set of epoxy-GNP nanocomposites under unidirectional compressive loads with different rates and the stress-strain curves are predicted with a decent precision.

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