| Metadata | Details |
|---|
| Publication Date | 2025-07-01 |
| Journal | Discover Applied Sciences |
| Authors | Vijendra Kumar Jarwal, Sushila Choudhary, Kalpna Sharma, Prasun Choudhary |
| Citations | 1 |
| Analysis | Full AI Review Included |
- Core Focus: Numerical modeling of Magnetohydrodynamic (MHD) unsteady flow and entropy generation in water-based nanofluids containing three carbon nanoparticles: Single-Walled Carbon Nanotubes (SWCNT), Graphene, and Nano-Diamond (ND).
- Performance Hierarchy: SWCNT-water nanofluid exhibited the superior thermal performance and highest temperature distribution due to its exceptional thermal conductivity (6600 W/mK).
- Magnetic Field Effects (M): Increasing the magnetic parameter significantly reduces fluid velocity (up to 71.8% reduction in fâ(eta=1)) due to the resistive Lorentz force, but elevates temperature profiles via Joule heating.
- Boundary Control: Suction (S > 0) thins both the velocity and thermal boundary layers, reducing temperature and entropy generation. Injection (S < 0) thickens the boundary layers, increasing velocity and temperature.
- Entropy Generation: Total entropy generation (Ns) is boosted by increasing the magnetic parameter (M), unsteadiness (A), and Brinkman number (Br), but is suppressed by increasing suction (S > 0) and nanoparticle volume fraction (phi).
- Skin Friction: Graphene-water nanofluid showed the highest skin friction coefficient, followed by SWCNT-water, and Nano-Diamond-water exhibited the lowest.
- Irreversibility Ratio: The Bejan number (Be) decreases sharply as the Brinkman number (Br) increases, indicating a strong shift from heat transfer-dominated to viscous dissipation-dominated entropy production.
| Parameter | Value | Unit | Context |
|---|
| Base Fluid | Pure Water | N/A | Fixed Prandtl Number (Pr) = 6.2 |
| Magnetic Parameter (M) Range | 0.0 to 3.0 | N/A | Range investigated in the simulation |
| Unsteadiness Parameter (A) Range | 0.0 to 3.0 | N/A | Range investigated in the simulation |
| Suction/Injection Parameter (S) Range | -2.0 to 2.0 | N/A | Negative S is injection, positive S is suction |
| Nanoparticle Volume Fraction (phi) Range | 0.05 to 0.25 | N/A | Range investigated |
| Brinkman Number (Br) Range | 0.5 to 3.5 | N/A | Ratio of viscous dissipation to thermal conduction |
| SWCNT Thermal Conductivity (kappa) | 6600 | W/mK | Highest conductivity nanoparticle tested |
| Graphene Thermal Conductivity (kappa) | 2500 | W/mK | Intermediate conductivity nanoparticle tested |
| Nano-Diamond (ND) Thermal Conductivity (kappa) | 1000 | W/mK | Lowest conductivity nanoparticle tested |
| Graphene Electrical Conductivity (sigma) | 1 x 107 | S/m | Highest electrical conductivity tested |
| ND Electrical Conductivity (sigma) | 34.84 x 106 | S/m | Intermediate electrical conductivity tested |
| SWCNT Electrical Conductivity (sigma) | 1400 | S/m | Lowest electrical conductivity tested |
| Max Deviation in Validation | < 0.00251 | N/A | Comparison against published results for -thetaâ(0) |
- Flow Model Formulation: A 2D unsteady laminar boundary layer flow model was established for a nanofluid (water + carbon nanoparticles) over a continuously stretching surface, incorporating MHD effects, suction/injection, viscous dissipation, and entropy generation.
- Nanofluid Property Models: The Tiwari-Das nanofluid model was used to account for solid volume fractions. Thermal conductivity (kappanf) was calculated using the Hamilton-Crosser model (for ND and Graphene) and the Maxwell theory (for SWCNT).
- Governing Equation Reduction: The flow-governing Partial Differential Equations (PDEs) for mass, momentum, and energy were reduced to a system of non-linear Ordinary Differential Equations (ODEs) using similarity transformations.
- Numerical Solution Technique: The resulting coupled ODEs (third order in dimensionless stream function f, second order in dimensionless temperature theta) were solved numerically using MATLABâs built-in
bvp4c solver.
- Boundary Conditions: The solution required guessing unknown initial conditions (a10, a20) at the surface (eta = 0) until the far-field boundary conditions (fâ(eta) â 0 and theta(eta) â 0 as eta â infinity) were satisfied.
- Computational Domain: The unbounded interval [0, infinity) was restricted to a finite interval [0, 10] with a step size of h = 0.02 to ensure computational precision.
- Advanced Cooling Systems: Utilizing SWCNT-enhanced fluids for high-efficiency thermal management in electronics (e.g., computer chips, LEDs) and industrial heat exchangers.
- Energy Storage and Conversion: Optimizing heat transfer mechanisms in solar energy systems and thermal energy storage technologies.
- Biomedical and Microfluidics: Identifying optimal flow conditions for targeted drug delivery within arterial networks, leveraging magnetic field and injection control to manage heat and momentum transfer.
- Aerospace and Automotive: Designing energy-efficient thermal systems by minimizing entropy generation, particularly in high-temperature and oxidative environments.
- Environmental Technologies: Developing high-performance sensors and detectors using Graphene nanofluids for applications like gas sensing and environmental monitoring.
View Original Abstract
Abstract This study explores the entropy generation characteristics in the flow of nanofluids over an unsteady stretching surface under the combined effects of magnetic field and suction/injection. The primary objective is to investigate that three different carbon-based nanoparticles Graphene, nano-diamond, and single-walled carbon nanotubes, dispersed in a water-based fluid, influence thermal performance and entropy production in magnetohydrodynamic flow scenarios. This work presents a novel comparison of high-conductivity nanofluids for precise thermal regulation applications. The flow governing partial differential equations, describing the unsteady nanofluid flow and heat transfer, are reduced to a system of ordinary differential equations through similarity transformations. These ODEs are numerically solved using MATLABâs built-in bvp4c solver. An increase in the magnetic parameter from $$M = 0$$ M = 0 to $$M = 3$$ M = 3 leads to a significant reduction in the momentum profiles from $$f^{\prime}\left( {\eta = 1} \right) = 0.38101$$ f ⲠΡ = 1 = 0.38101 for graphene nanofluid to $$f^{\prime}\left( {\eta = 1} \right) = 0.10741$$ f ⲠΡ = 1 = 0.10741 for nanodiamond fluid. Similarly, enhancing the injection parameter than a rise in velocity is noticed from $$f^{\prime}\left( {\eta = 1} \right) = 0.26662$$ f ⲠΡ = 1 = 0.26662 (nanodiamond fluid) to $$f^{\prime}\left( {\eta = 1} \right) = 0.42372$$ f ⲠΡ = 1 = 0.42372 (SWCNT-based nanofluid). Furthermore, an improvement in nanoparticle volume fraction $$\left( {0.05 \le \phi \le 0.10} \right)$$ 0.05 â¤ Ď â¤ 0.10 augments the thermal boundary layer, with the wall temperature rising from $$\theta \left( {\eta = 1} \right) = 0.13774$$ θ Ρ = 1 = 0.13774 for graphene nanofluid to $$\theta \left( {\eta = 1} \right) = 0.21466$$ θ Ρ = 1 = 0.21466 for SWCNT-based nanofluid. The results highlight the potential of nanoparticle-enhanced fluids and magnetic/injection control for optimizing heat and momentum transfer in advanced cooling, biomedical, and microfluidic systems. These findings support applications in electronics cooling, targeted drug delivery, and energy storage technologies.