 | | Mechanics of Solids
A Journal of Russian Academy of Sciences | | Founded
in January 1966
Issued 6 times a year
Print ISSN 0025-6544
Online ISSN 1934-7936 |
Archive of Issues
| Total articles in the database: | | 13653 |
| In Russian (Èçâ. ÐÀÍ. ÌÒÒ): | | 8223
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| In English (Mech. Solids): | | 5430 |
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| << Previous article | Volume 60, Issue 8 / 2025 | Next article >> |
| Seema and Abhinav Singhal, "Rayleigh-Type Surface Waves in Piezo-Thermoelastic Materials: A Comparative Study Using Green–Naghdi III and Three-Phase-Lag Models with Machine-Learning Surrogates," Mech. Solids. 60 (8), 6860-6887 (2025) |
| Year |
2025 |
Volume |
60 |
Number |
8 |
Pages |
6860-6887 |
| DOI |
10.1134/S0025654425604811 |
| Title |
Rayleigh-Type Surface Waves in Piezo-Thermoelastic Materials: A Comparative Study Using Green–Naghdi III and Three-Phase-Lag Models with Machine-Learning Surrogates |
| Author(s) |
Seema (Christ University, Bengaluru, 560029 India, mathsresearch.seema@gmail.com)
Abhinav Singhal (Christ University, Bengaluru, 560029 India) |
| Abstract |
In this work, the Green–Naghdi type III (GN-III) and Three-Phase-Lag (TPL) thermo-elastic theories are used to investigate Rayleigh-type surface wave propagation in a transversely isotropic piezo-thermoelastic half-space. Phase velocity, attenuation, and specific loss may be thoroughly
evaluated thanks to the derivation of secular equations for electrically open/shorted and thermally
insulated/isothermal boundary conditions. The findings indicate that attenuation and loss show a substantial dependency on boundary restrictions and the chosen thermoelastic model, but phase velocity
increases with inclination angle and stabilises for high wave numbers. The TPL framework predicts
somewhat greater velocities and damping because of thermal relaxation effects, while electrically
shorted isothermal surfaces produce the lowest dissipation. By explicitly incorporating governing
equations into its loss function, a Physics-Informed Neural Network (PINN) is utilised to overcome
the computational burden of solving difficult transcendental equations. The PINN provides an effective stand-in for optimisation and real-time diagnostics in SAW sensors, ultrasonic devices, and smart
piezoelectric materials by precisely reconstructing dispersion trends from sparse analytical data. |
| Keywords |
Piezoelectric thermoelasticity, Rayleigh-type waves, Green–Naghdi III, Three-phase-lag model, Machine learning |
| Received |
05 September 2025 | Revised |
22 November 2025 | Accepted |
24 November 2025 |
| Link to Fulltext |
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