The performance evolution of Xue and Yamada-Ota models for local thermal non equilibrium effects on 3D radiative casson trihybrid nanofluid
At a Glance
Section titled âAt a Glanceâ| Metadata | Details |
|---|---|
| Publication Date | 2025-03-01 |
| Journal | Scientific Reports |
| Authors | Ahmed M. Galal, Ali AkgĂŒl, Sahar Ahmed Idris, Shoira Formanova, Talib K. Ibrahim |
| Institutions | Near East University, Gulf University for Science & Technology |
| Citations | 6 |
| Analysis | Full AI Review Included |
Executive Summary
Section titled âExecutive SummaryâThis study provides a critical numerical analysis of 3D radiative flow characteristics for a Casson trihybrid nanofluid (THNF) under Local Thermal Non-Equilibrium Conditions (LTNECs). The research focuses on optimizing thermal design for advanced engineering applications.
- Core Value Proposition: The model integrates LTNE (separate liquid and solid phase temperatures), Marangoni convection, Stefan blowing, and activation energy, making it highly applicable for systems requiring precise thermal control and chemical reaction modeling (e.g., catalytic reactors).
- Nanofluid System: The THNF consists of Diamond, Cobalt Oxide (Co3O4), and Silicon Dioxide (SiO2) nanoparticles dispersed in a Sodium Alginate (NaC6H7O6) base fluid, exhibiting non-Newtonian (Casson) behavior.
- Model Comparison: The thermal conductivity of the nanofluid is assessed using both the Xue and Yamada-Ota (YO) models, adding a comparative dimension for optimized design.
- Key Performance Finding: The Yamada-Ota model consistently demonstrates superior heat transmission efficiency compared to the Xue model across various parameters.
- Stefan Blowing (Sb) Impact: Increasing the Stefan blowing parameter enhances the velocity profiles (fâ and gâ) while simultaneously causing a decline in the liquid and solid phase temperature profiles.
- LTNE Effects: A rise in the inter-phase heat transmission factor (H) significantly boosts the liquid phase temperature profile but causes the solid phase temperature profile to decline, highlighting the importance of LTNE modeling.
- Marangoni Convection (Ma) Impact: Higher Marangoni numbers increase the nanofluid velocity but decrease the temperature and concentration distributions for both phases.
Technical Specifications
Section titled âTechnical SpecificationsâThe following table summarizes the thermo-physical properties of the constituents and key dimensionless parameters used in the numerical simulation.
| Parameter | Value | Unit | Context |
|---|---|---|---|
| Base Fluid (NaAlg) Prandtl Number (Pr) | 6.5 | Dimensionless | Reference fluid property |
| Diamond (ND) Thermal Conductivity (k) | 1000 | W/mK | Nanoparticle (NP) |
| Cobalt Oxide (Co3O4) Density (rho) | 8862 | kg/m3 | Nanoparticle (NP) |
| Silicon Dioxide (SiO2) Specific Heat (Cp) | 730 | J/kgK | Nanoparticle (NP) |
| Sodium Alginate (NaAlg) Density (rho) | 989 | kg/m3 | Base fluid |
| Co3O4 Electrical Conductivity (sigma) | 1.85 x 10-6 | (Ωm)-1 | Nanoparticle |
| SiO2 Electrical Conductivity (sigma) | 1.0 x 10-18 | (Ωm)-1 | Nanoparticle |
| Casson Fluid Parameter (beta) | 0.0 to 1.3 | Dimensionless | Non-Newtonian rheology |
| Stefan Blowing Parameter (Sb) | 0.1 to 1.4 | Dimensionless | Mass transfer velocity at the surface |
| Marangoni Convection Parameter (Ma) | 0.3 to 1.8 | Dimensionless | Surface tension gradient effects |
| Activation Energy Parameter (E) | 0.3 to 1.2 | Dimensionless | Chemical reaction rate |
| Heat Source Parameter (QT) | 0.1 to 1.0 | Dimensionless | Internal heat generation |
Key Methodologies
Section titled âKey MethodologiesâThe study utilized a numerical approach to solve the complex fluid dynamics and heat transfer problem, focusing on the LTNE regime.
- Model Formulation: Established the governing 3D partial differential equations (PDEs) for momentum, energy (liquid and solid phases), and concentration, incorporating the Casson fluid model, Marangoni convection, and magnetic field effects (MHD).
- Thermal Conductivity Modeling: Incorporated two distinct modelsâthe Xue model and the Yamada-Ota (YO) modelâto calculate the effective thermal conductivity (kthnf) of the trihybrid nanofluid for comparative analysis.
- Dimensional Reduction: Applied an appropriate similarity transformation to convert the non-linear coupled PDEs into a set of non-linear ordinary differential equations (ODEs).
- Numerical Solution: The resulting ODEs, along with the boundary conditions (including Stefan blowing and Marangoni effects), were solved numerically using the Bvp4c technique (a finite difference code) implemented in MATLAB.
- Boundary Condition Implementation: Applied specific boundary constraints at the sheet surface (z = 0) for velocity, temperature (T and Tp), and concentration (C), and asymptotic conditions (z â infinity).
- Performance Metrics: Calculated and analyzed key engineering quantities, including the skin friction coefficient (Cf), Nusselt number for the liquid phase (Nu), Nusselt number for the solid phase (Nn), and Sherwood number (Sh).
Commercial Applications
Section titled âCommercial ApplicationsâThe precise control and enhanced thermal properties demonstrated by this THNF system under LTNE conditions make it valuable for several high-demand engineering sectors.
- High-Density Electronic Cooling: Applicable in microelectronic devices and data centers where localized hot spots necessitate LTNE modeling for accurate heat dissipation and where the high conductivity of the THNF is beneficial.
- Catalytic Reactors and Combustion Systems: The modelâs inclusion of activation energy and internal heat generation (QT) makes it suitable for designing and optimizing chemical processes where precise temperature regulation prevents runaway reactions.
- Solar Thermal Energy Systems: Utilization of the THNFâs enhanced thermal conductivity for improved heat transfer efficiency in solar collectors and concentrated solar power (CSP) systems.
- Microfluidics and Thin Film Coating: Marangoni convection effects are critical in these low-gravity or surface-tension-dominated environments, allowing for better control over fluid movement and material deposition.
- Medicinal and Energy Devices: Applicable in devices requiring non-Newtonian fluid transport (Casson model) and highly efficient heat exchange, such as certain drug delivery systems or specialized heat exchangers.
View Original Abstract
The proposed study investigates the characteristics of Stefan blowing and activation energy on MHD Casson Diamond-[Formula: see text][Formula: see text]based trihybrid nanofluid over a sheet with LTNECs (local thermal non-equilibrium conditions) and permeable medium. The significance of Marangoni convection as well as heat generation are considered. In order to examine the properties of heat transmission in the absence of local thermal equilibrium conditions, this paper makes use of a simple mathematical model. Local thermal non-equilibrium situations typically result in two discrete and crucial temperature gradients in both the liquid and solid phases. In systems where material qualities and heat transfer efficiency are crucial, the utilization of Xue model and Yamada-Ota model and to assess the thermal conductivity of the nanofluid adds a comparison dimension and enables optimized design. The controlling partial differential equations are reduced to non-linear ordinary differential equations using an appropriate similarity transformation. The Bvp4c technique is used to resolve the resulting equations numerically. Applications in modern thermal management systems, especially those requiring precise heat transfer control (e.g., electronic cooling, medicinal devices, energy systems), will benefit greatly from this work. The model is especially applicable to processes where chemical reactions and internal heat sources are important, like in catalytic reactors and combustion systems, because it takes into account activation energy and heat generating effects. The findings indicate that when the value of the interphase heat transmission factor increases, the solid phaseâs temperature profile and liquid phase heat transfer rate drop.