Numerical Simulation of Dual-Phase-Lag Model and Inverse Fractional Single-Phase-Lag Problem for the Non-Fourier Heat Conduction in a Straight Fin

Mozafarifard, Milad; Azimi, Aziz; Mehrzad, Salem

doi:10.1007/s11630-019-1137-1

Cited by 11 publications

(1 citation statement)

References 35 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…They solved the problem with the hyperbolic heat conduction and DPL heat transfer theory. Mozafarifard et al [18] employed FSPL and DPL techniques to investigate transient non-Fourier heat transfer in an upstanding expanded surface with an energy source at the presence of a periodic temperature imposed on the expanded surface root. This study, for the first time, used LMNPE to obtain the heat flux relaxation time and fractional derivation orders for an upstanding fin with the mentioned conditions.…”

Section: Introductionmentioning

confidence: 99%

Estimating Relaxation Time and Fractionality Order Parameters in Fractional Non-Fourier Heat Conduction Using Conjugate Gradient Inverse Approach in Single and Three-Layer Skin Tissues

et al. 2021

View full text Add to dashboard Cite

In this work, the relaxation parameter (τ) and fractionality order (α) in the fractional single phase lag (FSPL) non-Fourier heat conduction model are estimated by employing the conjugate gradient inverse method (CGIM). Two different physics of skin tissue are chosen as the studied cases; single and three-layer skin tissues. Single-layer skin is exposed to laser radiation having the constant heat flux of Qin. However, a heat pulse with constant temperature is imposed on the three-layer skin. The required inputs for the inverse problem in the fractional diffusion equation are chosen from the outcomes of the dual phase lag (DPL) theory. The governing equations are solved numerically by utilizing implicit approaches. The results of this study showed the efficiency of the CGIM to estimate the unknown parameters in the FSPL model. In fact, obtained numerical results of the CGIM are in excellent compatibility with the FSPL model.

show abstract

Section: Introductionmentioning

confidence: 99%