Pooja Devi, Bhuvaneshvar Kumar. Investigating peristaltic motion of ternary nanofluids using cubic regression with couple stress and Darcy-Forchheimer influence[J]. 2026, 15(1): 87-112.
DOI:
Pooja Devi, Bhuvaneshvar Kumar. Investigating peristaltic motion of ternary nanofluids using cubic regression with couple stress and Darcy-Forchheimer influence[J]. 2026, 15(1): 87-112. DOI: 10.1016/j.jppr.2026.02.004.
Investigating peristaltic motion of ternary nanofluids using cubic regression with couple stress and Darcy-Forchheimer influence
摘要
Abstract
This study investigates the peristaltic motion of magnetohydrodynamic (MHD) couple-stress ternary nanofluids through an inclined asymmetric porous channel under the influence of Darcy-Forchheimer drag. Two blood-based ternary nanofluid formulations are considered: T1 (Ag+TiO
2
+Cu) and T2 (Au+Fe
3
O
4
+multi-walled carbon nanotube
MWCNT). Two ternary nanofluid combinations (Ag+TiO
2
+Cu and (Au+Fe
3
O
4
+multi-walled carbon nanotube
MWCNT) were selected to compare metallic-oxide blends with hybrid magnetic-carbon structures
enabling assessment of their distinct thermal and rheological advantages. The governing equations of momentum
energy
and concentration are developed under the long wavelength and low Reynolds number assumptions and transformed into a dimensionless form. The effects of couple stress
magnetic field
porosity
heat generation
and chemical reaction are examined using a shooting technique coupled with the classical fourth-order Runge-Kutta (RK-4) method. Results show that the magnetic field and Forchheimer effects increase flow resistance
while higher Darcy numbers enhance velocity and thermal performance. Ternary nanofluid-2 exhibits superior thermal and mass transfer rates due to the synergistic influence of Au
Fe
3
O
4
and MWCNT nanoparticles
which offer higher conductivity and low
er interfacial resistance. The outcomes provide physical insights relevant to biomedical pumping
targeted drug transport
and thermal regulation in microfluidic devices. A cubic regression model is used because it captures nonlinear interactions among magnetic
porous
and couple-stress parameters more accurately than linear or quadratic models
enabling reliable prediction of complex peristaltic flow behavior.