Determination and Verification of Elastic Parameters for Adhesives

2009 ◽  
pp. 54-54-15 ◽  
Author(s):  
D Jangblad ◽  
P Gradin ◽  
T Stenström
Keyword(s):  
Elastic Parameters

REFRACTION ARRIVALS THROUGH THIN HIGH‐VELOCITY LAYERS

Geophysics ◽  
1965 ◽  
Vol 30 (2) ◽  
pp. 204-212 ◽  
Author(s):  
J. H. Rosenbaum
Keyword(s):  
Exponential Decay ◽  
High Velocity ◽  
Elastic Layer ◽  
Approximate Formula ◽  
Asymptotic Theory ◽  
Surrounding Medium ◽  
Low Frequency ◽  
Horizontal Distance ◽  
Symmetric Mode ◽  
Elastic Parameters

The first significant refraction arrival through a thin high‐velocity elastic layer in an elastic medium has been investigated theoretically by means of an asymptotic theory. This first low‐frequency arrival is closely connected with the longitudinal plate wave in the thin layer. When the medium surrounding the layer is a fluid, the signal does not decay exponentially with horizontal distance; when the surrounding medium is a solid, the signal does decay exponentially. A very simple approximate formula for this exponential decay is presented and compared with numerical results of the more rigorous theory. The decay as well as the shape of the signal is dependent upon the contrast in elastic parameters between the plate and the surrounding medium. Higher‐frequency early arrivals, associated with the second symmetric mode, have also been investigated. They exhibit greater exponential decay with horizontal distance than the low‐frequency first arrivals.

Effective elastic parameters of random composites

1980 ◽  
Vol 37 (4) ◽  
pp. 377-379 ◽  
Author(s):  
A. J. Devaney ◽  
H. Levine
Keyword(s):  
Elastic Parameters ◽  
Random Composites

scholarly journals Accurate Determination of Elastic Parameters for Multicomponent Membranes

2008 ◽  
Vol 100 (8) ◽  
Author(s):  
Stefan Semrau ◽  
Timon Idema ◽  
Laurent Holtzer ◽  
Thomas Schmidt ◽  
Cornelis Storm
Keyword(s):  
Accurate Determination ◽  
Elastic Parameters

DIPLOBACK: Neural-Network-Based Backcalculation Program for Composite Pavements

1997 ◽  
Vol 1570 (1) ◽  
pp. 143-150 ◽  
Author(s):  
Lev Khazanovich ◽  
Jeffery Roesler
Keyword(s):  
Neural Network ◽  
Winkler Foundation ◽  
Multilayer Composite ◽  
Falling Weight Deflectometer ◽  
Subgrade Reaction ◽  
Elastic Parameters ◽  
The Neural Networks ◽  
Elastic Layers ◽  
Falling Weight ◽  
Moduli Of Elasticity

A neural-network-based backcalculation procedure is developed for multilayer composite pavement systems. The constructed layers are modeled as compressible elastic layers, whereas the subgrade is modeled as a Winkler foundation. The neural networks are trained to find moduli of elasticity of the constructed layers and a coefficient of subgrade reaction to accurately match a measured deflection profile. The method was verified by theoretically generated deflection profiles and falling weight deflectometer data measurements conducted at Edmonton Municipal Airport, Canada. For the theoretical deflection basins, the results of backcalculation were compared with actual elastic parameters, and excellent agreement was observed. The results of backcalculation using field test data were compared with the results obtained using WESDEF. Similar trends were observed for elastic parameters of all the pavement layers. The backcalculation procedure is implemented in a computer program called DIPLOBACK.

Elastic parameters of paramagnetic iron-based alloys from first-principles calculations

2012 ◽  
Vol 85 (5) ◽  
Author(s):  
Hualei Zhang ◽  
Marko P. J. Punkkinen ◽  
Börje Johansson ◽  
Levente Vitos
Keyword(s):  
First Principles ◽  
First Principles Calculations ◽  
Elastic Parameters ◽  
Iron Based

Porosity estimation based on rock skeleton general formula and comprehensive pore structure parameter – An application to a tight-sand reservoir

2021 ◽  
pp. 1-59
Author(s):  
Kai Lin ◽  
Xilei He ◽  
Bo Zhang ◽  
Xiaotao Wen ◽  
Zhenhua He ◽  
...  
Keyword(s):  
Aspect Ratio ◽  
Pore Structure ◽  
Seismic Data ◽  
Rock Physics ◽  
Structure Parameters ◽  
Pore Structure Parameter ◽  
Elastic Parameters ◽  
Physics Model ◽  
Rock Physics Model ◽  
Porosity Estimation

Most of current 3D reservoir’s porosity estimation methods are based on analyzing the elastic parameters inverted from seismic data. It is well-known that elastic parameters vary with pore structure parameters such as pore aspect ratio, consolidate coefficient, critical porosity, etc. Thus, we may obtain inaccurate 3D porosity estimation if the chosen rock physics model fails properly address the effects of pore structure parameters on the elastic parameters. However, most of current rock physics models only consider one pore structure parameter such as pore aspect ratio or consolidation coefficient. To consider the effect of multiple pore structure parameters on the elastic parameters, we propose a comprehensive pore structure (CPS) parameter set that is generalized from the current popular rock physics models. The new CPS set is based on the first order approximation of current rock physics models that consider the effect of pore aspect ratio on elastic parameters. The new CPS set can accurately simulate the behavior of current rock physics models that consider the effect of pore structure parameters on elastic parameters. To demonstrate the effectiveness of proposed parameters in porosity estimation, we use a theoretical model to demonstrate that the proposed CPS parameter set properly addresses the effect of pore aspect ratio on elastic parameters such as velocity and porosity. Then, we obtain a 3D porosity estimation for a tight sand reservoir by applying it seismic data. We also predict the porosity of the tight sand reservoir by using neural network algorithm and a rock physics model that is commonly used in porosity estimation. The comparison demonstrates that predicted porosity has higher correlation with the porosity logs at the blind well locations.

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