Pattern recognition based strategy to evaluate the stress field from dynamic photoelasticity experiments

2020 ◽  
Author(s):  
Juan Carlos Briñez de León ◽  
Mateo Rico ◽  
John W. Branch ◽  
Alejandro Restrepo Martínez
Keyword(s):  
Pattern Recognition ◽  
Stress Field ◽  
Dynamic Photoelasticity

Physical modeling of fluid-filled fractures using the dynamic photoelasticity technique

Geophysics ◽  
2021 ◽  
Vol 86 (1) ◽  
pp. T33-T43
Author(s):  
Haitao Cao ◽  
Ezequiel Medici ◽  
Roohollah Askari
Keyword(s):  
Wave Propagation ◽  
Stress Field ◽  
Seismic Wave ◽  
Seismic Data ◽  
Physical Modeling ◽  
Complex Geometry ◽  
Seismic Wave Propagation ◽  
Fluid Viscosity ◽  
Dynamic Photoelasticity ◽  
Seismic Stress

We have developed an optical apparatus based on the dynamic photoelasticity technique to visualize and analyze the propagation of the Krauklis wave within an analog fluid-filled fracture. Although dynamic photoelasticity has been used by others to study seismic wave propagation, this study adds a quantitative analysis addressing dispersion properties. We physically modeled a fluid-filled fracture using transparent photoelastic-sensitive polycarbonate and nonsensitive acrylic plates. Then we used a pixel-based framework to analyze the dispersion of a Krauklis wave excited in the fracture. Through this pixel-based framework, we thus demonstrate that the dynamic photoelasticity technique can quantitatively describe seismic wave propagation with a quality similar to experiments using conventional transducers (receivers) while additionally visualizing the seismic stress field. We observe that an increase in the fluid viscosity results in a decrease in the velocity of the Krauklis wave. We also determine the capability of the method to analyze seismic data in the case of complex geometry by modeling a sawtooth fracture. The fracture’s geometry can strongly affect the characteristics of the Krauklis wave as we note a higher Krauklis wave velocity for the sawtooth case, as well as greater perturbation of the stress field.

Observation of secondary slip systems in tungsten bicrystals

1984 ◽  
Vol 42 ◽  
pp. 478-479
Author(s):  
J. R. Fekete ◽  
R. Gibala
Keyword(s):  
Stress Field ◽  
Grain Boundaries ◽  
Deformation Behavior ◽  
Stress Axis ◽  
Polycrystalline Materials ◽  
Slip Systems ◽  
Metallic Materials ◽  
Twist Boundary ◽  
Secondary Slip ◽  
Plane Normal

The deformation behavior of metallic materials is modified by the presence of grain boundaries. When polycrystalline materials are deformed, additional stresses over and above those externally imposed on the material are induced. These stresses result from the constraint of the grain boundaries on the deformation of incompatible grains. This incompatibility can be elastic or plastic in nature. One of the mechanisms by which these stresses can be relieved is the activation of secondary slip systems. Secondary slip systems have been shown to relieve elastic and plastic compatibility stresses. The deformation of tungsten bicrystals is interesting, due to the elastic isotropy of the material, which implies that the entire compatibility stress field will exist due to plastic incompatibility. The work described here shows TEM observations of the activation of secondary slip in tungsten bicrystals with a [110] twist boundary oriented with the plane normal parallel to the stress axis.

Software pattern recognition applied to nanodiffraction patterns from a STEM instrument

1986 ◽  
Vol 44 ◽  
pp. 694-695
Author(s):  
G.Y. Fan ◽  
J.M. Cowley
Keyword(s):  
Image Processing ◽  
Pattern Recognition ◽  
Computer Software ◽  
Processing System ◽  
Light Level ◽  
Host Computer ◽  
Low Light ◽  
Image Processing System ◽  
Recent Developments ◽  
On Line

In recent developments, the ASU HB5 has been modified so that the timing, positioning, and scanning of the finely focused electron probe can be entirely controlled by a host computer. This made the asynchronized handshake possible between the HB5 STEM and the image processing system which consists of host computer (PDP 11/34), DeAnza image processor (IP 5000) which is interfaced with a low-light level TV camera, array processor (AP 400) and various peripheral devices. This greatly facilitates the pattern recognition technique initiated by Monosmith and Cowley. Software called NANHB5 is under development which, instead of employing a set of photo-diodes to detect strong spots on a TV screen, uses various software techniques including on-line fast Fourier transform (FFT) to recognize patterns of greater complexity, taking advantage of the sophistication of our image processing system and the flexibility of computer software.

Bayesian removal of noise for increased sensitivity in vector pattern recognition lattice imaging of interfaces

1993 ◽  
Vol 51 ◽  
pp. 994-995
Author(s):  
L. Fei ◽  
P. Fraundorf
Keyword(s):  
Pattern Recognition ◽  
Perfect Crystal ◽  
Unit Cell ◽  
Ion Milling ◽  
Beam Radiation ◽  
Major Interest ◽  
Unit Cells ◽  
Mapping Process ◽  
Increased Sensitivity ◽  
Electron Microscopes

Interface structure is of major interest in microscopy. With high resolution transmission electron microscopes (TEMs) and scanning probe microscopes, it is possible to reveal structure of interfaces in unit cells, in some cases with atomic resolution. A. Ourmazd et al. proposed quantifying such observations by using vector pattern recognition to map chemical composition changes across the interface in TEM images with unit cell resolution. The sensitivity of the mapping process, however, is limited by the repeatability of unit cell images of perfect crystal, and hence by the amount of delocalized noise, e.g. due to ion milling or beam radiation damage. Bayesian removal of noise, based on statistical inference, can be used to reduce the amount of non-periodic noise in images after acquisition. The basic principle of Bayesian phase-model background subtraction, according to our previous study, is that the optimum (rms error minimizing strategy) Fourier phases of the noise can be obtained provided the amplitudes of the noise is given, while the noise amplitude can often be estimated from the image itself.

Pattern Recognition: The Basis of All Cognition?

1989 ◽  
Vol 34 (11) ◽  
pp. 988-989
Author(s):  
Erwin M. Segal
Keyword(s):  
Pattern Recognition
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