Estimation of surface layer structure from Rayleigh wave dispersion: Dense data case

1977 ◽  
Vol 48 (2) ◽  
pp. 498-512 ◽  
Author(s):  
J. M. Richardson
Keyword(s):  
Surface Layer ◽  
Rayleigh Wave ◽  
Layer Structure ◽  
Wave Dispersion ◽  
Rayleigh Wave Dispersion ◽  
Dense Data

Inversion of Rayleigh wave dispersion curves via adaptive GA and nested DLS

2019 ◽  
Vol 218 (1) ◽  
pp. 547-559 ◽  
Author(s):  
Yuhang Lei ◽  
Hongyan Shen ◽  
Xinxin Li ◽  
Xin Wang ◽  
Qingchun Li
Keyword(s):  
Rayleigh Wave ◽  
Wave Dispersion ◽  
Dispersion Curves ◽  
Rayleigh Wave Dispersion

Third Order Elastic Constants and Rayleigh Wave Dispersion of Shot Peened Aero-Engine Materials

2013 ◽  
Vol 768-769 ◽  
pp. 201-208 ◽  
Author(s):  
Marek Rjelka ◽  
Martin Barth ◽  
Sven Reinert ◽  
Bernd Koehler ◽  
Joachim Bamberg ◽  
...  
Keyword(s):  
Residual Stress ◽  
Rayleigh Wave ◽  
Elastic Constants ◽  
Cold Work ◽  
High Strength ◽  
Wave Dispersion ◽  
Third Order ◽  
Third Order Elastic Constants ◽  
Aero Engine ◽  
Rayleigh Wave Dispersion

Aero-engine components exposed to high mechanical stresses are made of high-strength alloys and additionally, they are surface treated by shot peening. This process introduces compressive residual stress into the material making it less sensitive to stress corrosion cracking and fatigue and therefore benefits the components performance and lifetime. Moreover cold work is induced in an amount depending on the peening parameters. To approximate the remaining lifetime, a quantitative, non-destructive method for stress assessment is required. It was shown that surface treatment of such alloys can be characterized by broadband Rayleigh wave dispersion measurements. However, the relative contributions of residual stress and cold work, respectively, remained an open point. This paper presents the determination of third order elastic constants (TOEC) for IN718 and Ti6246, providing, together with a model for the inversion of dispersion data, a quantitative access to the acoustoelastic effect. Finally, some measurements of differently treated samples are given.

Rayleigh Wave Dispersion Curve Inversion with the Artificial Bee Colony Algorithm

2021 ◽  
Vol 26 (2) ◽  
pp. 99-110
Author(s):  
Xin Wang ◽  
Hongyan Shen ◽  
Xinxin Li ◽  
Qin Li ◽  
Daoyuan Wang
Keyword(s):  
Rayleigh Wave ◽  
Dispersion Curve ◽  
Artificial Bee Colony Algorithm ◽  
Artificial Bee Colony ◽  
Wave Dispersion ◽  
Velocity Model ◽  
S Wave ◽  
Bee Colony ◽  
Search Optimization ◽  
Rayleigh Wave Dispersion

Rayleigh wave dispersion curve inversion is a non-linear iterative optimization process with multi-parameter and multi-extrema. It is difficult to carry out inversion and reconstruction of stratigraphic parameters quickly and accurately with a single linear or non-linear inversion for the data processing of Rayleigh waves with complex seismic geological conditions. We proposed a new method that combines artificial bee colony algorithm (ABC) and damped least squares algorithm (DLS) to invert Rayleigh wave dispersion curve. First, food sources are initialized in a large scale of the model based on the prior geological information. Then, after three kinds of bee operators (employed bees, onlooker bees and scout bees) transform each other and perform search optimization with several iterations, the targets are converged near the optimal solution to obtain an initial S-wave velocity model. Finally, the final S-wave velocity model is obtained by local optimization of DLS inversion with fast convergence and strong stability. The correctness of the method has been verified by one high-velocity interlayer model, and it was further applied to a real Rayleigh wave dataset. The results show that our method not only absorbs the advantages of ABC global search optimization and strong adaptability, but also makes full use of the advantages of DLS inversion, such as high accuracy and fast convergence speed. The inversion strategy can effectively suppress the inversion falling into local extrema, get rid of the dependence on an initial model, enhance the inversion stability, further improve the convergence speed and inversion accuracy, while has good anti-noise ability.

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