• No results found

Two Dimensional Stress and Displacement Wave Propagation Under Shock Loading in Saturated Porous Materials with Two Dimensional Functionally Graded Materails Using MLPG Method

N/A
N/A
Protected

Academic year: 2020

Share "Two Dimensional Stress and Displacement Wave Propagation Under Shock Loading in Saturated Porous Materials with Two Dimensional Functionally Graded Materails Using MLPG Method"

Copied!
10
0
0

Loading.... (view fulltext now)

Full text

Loading

Figure

Fig. 1. The porous medium seen as the superposition of two continuous media [19]
Fig. 2. The boundary of local subdomain in the MLPG method [6]
Fig. 3. The vertical displacement time history on the top surface for Berea sandstone compared to analytical results.
Table 2. The maximum of radial stress at the center of domain (z = 0.5 m and r = 1.5 m in Figure 4) in various factor of fgm
+3

References

Related documents

Depending on the spe- cies, length of the evolution experiments, and conditions (mutagenic versus non-mutagenic), it is possible that different estimates of the Markov parameters

The prediction envelopes we created from the multiple regression equations available for NWOR species indicate that model-selection uncer- tainty expressed as a percentage of

The systematic review methodology describes the ap- proach which will be used to find and analyse original articles containing data from field experiments assessing the effects of

Computer simulations are performed to find the Nusselt number and the heat transfer coefficient for natural convection of nanofluids in horizontal, tilted square, annulus

The present study revealed that lipopolysaccharide (LPS)-induced NF- κ B signaling activation suppressed α -catenin expression and motility in SW620 colorectal cancer (CRC)

In addition, guarantee information privacy prepares for in advanced through online shoppers, creating trust and added value, excellent reputation and condition of

productivity (woody net primary productivity, NPP) and res- idence time of woody biomass from four dynamic global vegetation models (DGVMs) with measured inventory data from

To identify the key genes whose expression may discrim- inate between early- and late-stage OSCC samples, we adopted the following major steps: (1) merging of multiple