Set Of Elastic Properties Research Articles

Objective Function Distortion Reduction in Identification Technique of Composite Material Elastic Properties

In studies of structural mechanics, modal analysis, presented in this paper, is an important tool for analyzing the vibration of an object and its frequencies. In modal analysis, different modes of vibration and the frequencies that generate them are considered. The study covers the nondestructive identification of the elastic characteristics of materials, which involves stochastic algorithms and the application of reverse engineering (i.e., the comparison of reference eigenfrequencies with the results of mathematical models). Identification is achieved by minimizing the objective function—the smaller the value of the objective function, the higher the identification accuracy obtained. By changing the parameters of a material’s mathematical model during identification, certain (usually higher order) modes can change places in a natural frequency spectrum. This leads to the comparison of different order eigenfrequencies, slow convergence and poor accuracy of the identification process. The technique involved in this work is the mode-shape recognition of a specimen of material with an “incorrect” set of elastic properties. The results prove that the identification accuracy of a material’s elastic properties can be increased if an “incorrect” set of elastic properties is removed from the identification process. The research covers only numerical research, with a physical experiment simulation.

Deep Learning for Size-Agnostic Inverse Design of Random-Network 3D Printed Mechanical Metamaterials.

Practical applications of mechanical metamaterials often involve solving inverse problems aimed at finding microarchitectures that give rise to certain properties. The limited resolution of additive manufacturing techniques often requires solving such inverse problems for specific specimen sizes. Moreover, the candidate microarchitectures should be resistant to fatigue and fracture. Such a multi-objective inverse design problem is formidably difficult to solve but its solution is the key to real-world applications of mechanical metamaterials. Here, a modular approach titled "Deep-DRAM" that combines four decoupled models is proposed, including two deep learning (DL) models, a deep generative model based on conditional variational autoencoders, and direct finite element (FE) simulations. Deep-DRAM integrates these models into a framework capable of finding many solutions to the posed multi-objective inverse design problem based on random-network unit cells. Using an extensive set of simulations as well as experiments performed on 3D printed specimens, it is demonstrate that: 1) the predictions of the DL models are in agreement with FE simulations and experimental observations, 2) an enlarged envelope of achievable elastic properties (e.g., rare combinations of double auxeticity and high stiffness) is realized using the proposed approach, and 3) Deep-DRAM can provide many solutions to the considered multi-objective inverse design problem.

Identification of orthotropic elastic properties of wood by digital image correlation and finite element model updating techniques

Computer-aided engineering systems rely on constitutive models and their parameters to describe the material behaviour. The identification process, however, grows in complexity for more elaborated material models, increasing the number of experimental tests needed, therefore time and cost consuming. Recently, with the development of digital image technology, there has been a growing interest in the use of full-field deformation measurements. This advancement has highlighted a new methodology on inverse identification techniques such as the finite element model updating method. This work aims to identify orthotropic linear elastic constitutive parameters of Pinus pinaster Ait. wood based on a single uniaxial compressive experimental test. Clear wood specimens oriented in the radial-tangential plane were used and tested under quasi-static loading condition. Full-field displacement and strain maps were obtained using digital image correlation and used to simultaneously identify the whole set of elastic properties through finite element model updating methodology.

Rock-physics and seismic-inversion based reservoir characterization of the Haynesville Shale

Seismic reservoir characterization of unconventional gas shales is challenging due to their heterogeneity and anisotropy. Rock properties of unconventional gas shales such as porosity, pore-shape distribution, and composition are important for interpreting seismic data amplitude variations in order to locate optimal drilling locations. The presented seismic reservoir characterization procedure applied a grid-search algorithm to estimate the composition, pore-shape distribution, and porosity at the seismic scale from the seismically inverted impedances and a rock-physics model, using the Haynesville Shale as a case study. All the proposed rock properties affected the seismic velocities, and the combined effects of these rock properties on the seismic amplitude were investigated simultaneously. The P- and S-impedances correlated negatively with porosity, and the V P/V S correlated positively with clay fraction and negatively with the pore-shape distribution and quartz fraction. The reliability of these estimated rock properties at the seismic scale was verified through comparisons between two sets of elastic properties: one coming from inverted impedances, which were obtained from simultaneous inversion of prestack seismic data, and one derived from these estimated rock properties. The differences between the two sets of elastic properties were less than a few percent, verifying the feasibility of the presented seismic reservoir characterization.

A Note on Short-time Response of Two-dimensional Lattices during Dynamic Loading

The disordered 2D lattices are used extensively to study damage evolution and fracture of inhomogeneous or multi-phase systems. The present note addresses their initial elastic response during dynamic loading. Namely, a transition from short-time values of modulus of elasticity and Poisson's ratio to respective long-time values, which is not accompanied by the corresponding change of stiffness tensor components. The study is performed on three 2D truss-type lattices. It is demonstrated that the difference between the two sets of elastic properties is a result of combining effects of the initial lateral inertia and the disorder of the system.

Insight into Elastic Properties of Binary Alkali Silicate Glasses; Prediction and Interpretation through Atomistic Simulation Techniques

Molecular dynamics simulations and energy-minimization techniques have been applied for the first time to determine the whole set of elastic properties (Young's modulus, shear modulus, bulk modulus, and Poisson's ratio) of alkali silicate glasses with different ion modifiers (Li, Na, and K) in the range 0−30 mol % alkaline oxide. Excellent agreement has been found between the simulation results and the experimental data. The peculiar behavior of the Li-containing glasses with respect to the Na and K ones is extensively discussed in terms of the glass structural features. It is found that the elastic property variation as a function of alkali addition can be explained by three concurrent factors: (1) depolymerization of the silica network; (2) increasing the cohesion of the glass by the establishment of alkali−NBO bonds; and (3) decreasing the free volume with consequent increasing of the glass packing density.

Modeling elastic properties in finite‐element analysis: How much precision is needed to produce an accurate model?

The influence of elastic properties on finite-element analysis was investigated using a finite-element model of a Macaca fascicularis skull. Four finite-element analyses were performed in which the model was assigned different sets of elastic properties. In analysis 1, elastic properties were modeled isotropically using published data obtained from human limb bones. Analyses 2-4 used data obtained from skulls of a closely allied species, M. mulatta, but varied as to how those data were incorporated into the model. In analysis 2, the model was assigned a single set of isotropic elastic properties. In analysis 3, each region within the model was assigned its own set of isotropic elastic properties. Finally, in analysis 4, each region received its own set of orthotropic elastic properties. Although a qualitative assessment indicates that the locations of strain concentrations across the model are broadly similar in all analyses, a quantitative assessment of strain indicates some differences between the analyses. When strain data from the finite-element analyses were compared to strain data derived from in vivo experiments, it was found that the model deformed most realistically using the orthotropic elastic properties employed in analysis 4. Results suggest that finite-element analyses can be adversely affected when elastic properties are modeled imprecisely, and that modelers should attempt to obtain elastic properties data about the species and skeletal elements that are the subjects of their analyses.