Ahmed E. Abouelregal, Marin Marin, Kareem Alanazi, Salman S. Alsaeed
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Dynamic behavior of a hollow cylinder under spatial–temporal nonlocality and fractional thermoelasticity incorporating the two-parameter Mittag–Leffler kernel
This study introduces a novel thermoelastic model to advance generalized thermoelastic theory by incorporating fractional derivatives and the two-parameter Mittag–Leffler kernel. Grounded in the phase-lag concept, the model uniquely integrates spatial and temporal nonlocal effects, enabling accurate representation of microscopic interactions in elastic structures. By employing the Goufo–Caputo two-parameter fractional operator in the heat conduction equation, it significantly enhances the depiction of memory effects, illustrating how past deformations and thermal conditions influence material behavior. Applied to an infinitely long, isotropic hollow cylinder, the model reveals how spatiotemporal nonlocality and fractional scaling impact thermomechanical properties under thermal variations and structural constraints. Numerical results show that fractional-order parameters minimally affect displacement but have a stronger influence on other physical properties, such as stress and strain. Similarly, nonlocal coefficients exhibit limited effects on temperature but significantly impact other critical physical quantities, offering valuable insights into thermal and structural responses.
期刊介绍:
Archive of Applied Mechanics serves as a platform to communicate original research of scholarly value in all branches of theoretical and applied mechanics, i.e., in solid and fluid mechanics, dynamics and vibrations. It focuses on continuum mechanics in general, structural mechanics, biomechanics, micro- and nano-mechanics as well as hydrodynamics. In particular, the following topics are emphasised: thermodynamics of materials, material modeling, multi-physics, mechanical properties of materials, homogenisation, phase transitions, fracture and damage mechanics, vibration, wave propagation experimental mechanics as well as machine learning techniques in the context of applied mechanics.