S. W. Teklu

57283939200

Publications - 2

Intelligent predictive neural network analysis of stefan blowing impacts on chemical reactive flow of Boger nanofluid with thermophoresis and brownian motion

Publication Name: Discover Nano

Publication Date: 2026-12-01

Volume: 21

Issue: 1

Page Range: Unknown

Description:

This study scrutinizes the effect of thermal radiation and Stefan blowing on the chemical reactive flow of Boger nanofluid across a stretched sheet with Darcy Forchheimer medium and heat generation using an intelligent computational framework based on Artifice neural network—Bayesian regularization. Furthermore, Brownian motion and thermophoresis properties have been examined. The suggested model of how Stefan blowing affects the chemical reactive flow of a Boger nanofluid with thermophoresis effects and Brownian motion has useful applications in a number of industrial and engineering operations. In chemical reactors, nano-coating technologies, and polymer processing, this model is essential for improving heat and mass transport processes. While the Boger nanofluid model accurately depicts non-Newtonian behaviour pertinent to biofluids and complex lubricants, Stefan blowing consideration offers insights on evaporation or suction effects. For the purpose of maximizing nanoparticle dispersion in cooling systems, fuel cells, and medicinal devices like targeted drug delivery systems where exact control over particle motion and chemical reactivity is crucial, Brownian motion and thermophoresis are also critical. The velocity profile improves as the Stefan blowing parameter values rise, but the thermal and concentration profiles decrease.

Open Access: Yes

DOI: 10.1186/s11671-026-04486-w

Regression and statistical analysis of heat transfer enhancement in water/ethylene glycol (40/60) base molybdenum carbide (Mo2C) MXene nanofluid using a transient fractional model

Publication Name: Discover Nano

Publication Date: 2026-12-01

Volume: 21

Issue: 1

Page Range: Unknown

Description:

To investigate the effects of fractional order (), nanoparticle volume fraction (), magnetic field strength (), and Brinkman permeability () on both flow and heat transfer characteristics, a detailed parametric and statistical analysis is conducted. The statistical regression analysis shows that the volume fraction of nanoparticles and temperature have a strong positive correlation (coefficient = 0.94, p = 0.021) indicating that Mo2C MXene is an excellent heat absorption. On the other hand, the fractional parameter α has a strong negative effect on temperature field (coefficient = − 0.086, p < 0.001), which emphasizes its importance in describing the effects of thermal memory. The findings also indicate that, although MXene nanoparticles significantly increase thermal transport, an augmentation in magnetic field strength and Brinkman resistance cause a resistive Lorentz force and frictional drag, respectively, to prevent fluid flow. These results are physically informative about non-Fourier heat transfer in MXene-based nanofluids as well as offer invaluable information to developing high-performance thermal management systems and solar-energy applications.

Open Access: Yes

DOI: 10.1186/s11671-026-04645-z