ItemArticle

Machine learning-enhanced heat and mass transfer study of elliptic motion in piezoelectric thermoelastic plates using Green-Naghdi III and three-phase-lag theories

Title
Machine learning-enhanced heat and mass transfer study of elliptic motion in piezoelectric thermoelastic plates using Green-Naghdi III and three-phase-lag theories
Creator
Singhal, Abhinav; Seema; Saeed, Abdulkafi Mohammed; Nirwal, Sonal; Das, Soumik; Chaudhary, Anjali
Description
Rayleigh-type surface waves in piezoelectric (PE) solids are pivotal for acoustic sensors, microelectromechanical systems (MEMS), and non-destructive evaluation. However, classical thermoelastic models fail under high heat flux due to the assumption of infinite thermal signal speeds, which limits their accuracy in coupled thermo-mechanical systems. To capture finite-speed and memory-dependent thermal effects, the Rayleigh wave propagation in a transversely isotropic (TI) PE half-space using generalized theories (such as Green-Naghdi type III (GN-III) and three-phase-lag (TPL)) is studied in this paper. The analytical formulation under varied electrical and thermal boundary conditions has been obtained. Secular equations are derived to characterize phase velocity, attenuation, and specific energy loss. A regression-based machine learning (ML) surrogate model is trained by using an analytical dataset to provide rapid predictions of wave parameters. Additionally, a confusion matrix classifier is applied to identify boundary conditions from simulated wave response features. The results demonstrated that the phase velocity increases with inclination angle and stabilizes with wave number, whereas attenuation and specific loss vary strongly by boundary condition (e.g., minimal in shorted-isothermal cases). The ML surrogate successfully reconstructed analytical predictions with minimal residual error, and the confusion matrix demonstrates accurate classification performance and validates the diagnostic potential of the framework. The novelty of this paper lies in integrating dual thermoelastic theories with machine learning, merging mechanics, heat transfer, and intelligent computing. These findings enable enhanced SAW sensor designs for precise gas/chemical detection, low-loss NDE tools for aerospace composite defect identification, and real-time diagnostics in biomedical ultrasonics for clearer imaging and efficient energy harvesting. 2026 Elsevier Ltd
Source
International Communications in Heat and Mass Transfer;Volume;172;Issue;;Article No.;110482;
Date
01-01-2026
Publisher
Elsevier Ltd
Subject
Attenuation and phase velocity; Computational mechanics; Confusion matrix; Elliptic motion energy wave; GreenNaghdi III; Heat and mass transfer; Machine learning; Multiphysics simulation; Piezoelectric thermoelasticity; Three-phase-lag theory
Coverage
Singhal A., Christ University, Bengaluru, 560029, India; Seema, Christ University, Bengaluru, 560029, India; Saeed A.M., Department of Mathematics, College of Science, Qassim University, Buraydah, 51452, Saudi Arabia; Nirwal S., Centre for Mathematical Needs, Department of Mathematics, CHRIST (Deemed to be University), Bengaluru, India; Das S., School of Physical Sciences, Amrita Vishwa Vidyapeetham, Mysuru Campus, Karnataka, 570026, India; Chaudhary A., Department of Management, College of Business Administration, Princess Nourah bint Abdulrahman University, P.O.Box 84428, Riyadh, 11671, Saudi Arabia
Rights
Restricted Access; Hardcopy may be available in the library
Relation
ISSN: 7351933; CODEN: IHMTD
Format
online
Language
English
Type
Article

Citation

Singhal, Abhinav; Seema; Saeed, Abdulkafi Mohammed; Nirwal, Sonal; Das, Soumik; Chaudhary, Anjali, “Machine learning-enhanced heat and mass transfer study of elliptic motion in piezoelectric thermoelastic plates using Green-Naghdi III and three-phase-lag theories,” CHRIST (Deemed To Be University) Institutional Repository, accessed June 20, 2026, https://archives.christuniversity.in/items/show/22280.

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