Non Linear Thermal Radiation Analysis of Electromagnetic Chemically Reacting Ternary Nanofluid Flow over a Bilinear Stretching Surface
Bibliographic record
Abstract
Background: The study investigates three-dimensional boundary layer flow in a reactive, rotating nanomaterial liquid, emphasizing non-linear thermal diffusion and radiation effects over a stretchable surface influenced by a Lorentz force. Water serves as the base fluid, with nanoparticles of silver (Ag), molybdenum disulfide (MoS₂), and copper (Cu) incorporated to enhance thermal conductivity. Rotational effects are introduced by a system rotating around a vertical axis at a constant angular velocity (⍵*). Such configurations are of significant interest in thermal management systems, heat exchangers, and other industrial applications. Motivation: The increasing demand for advanced heat transfer mechanisms has driven interest in hybrid and ternary nanofluids due to their superior thermal properties compared to conventional fluids. This study aims to contribute to this growing field by analyzing the combined effects of magnetic fields, rotation, and thermal radiation on the flow and heat transfer behavior in nano materials. These insights are critical for optimizing heat transfer systems in energy, manufacturing, and engineering sectors. Aim and Objective: The research seeks to analyze heat,velocity and concentration transfer rates in a three-dimensional flow system with two thermo physical models.The study focuses on the effects of magnetic field strength (M), stretching ratio (λ), Radiation parameter (R) and rotational parameter (γ) on heat transfer and fluid flow. Methodology: The governing nonlinear partial differential equations (PDEs) are transformed into ordinary differential equations (ODEs) using similarity transformations. The numerical solutions are obtained using the BVP4C method and the shooting technique. MATLAB is employed to compute and graphically represent the results, including profiles for velocity, temperature, and concentration, along with Nusselt (Nu) and Sherwood (Sh) numbers. Results and Conclusions: The analysis reveals that key parameters, such as magnetic field strength, stretching ratio, and rotational effects, significantly influence heat transfer and flow characteristics.In Model-1, the percentage increase in heat transfer due to an increase in nanoparticle volume fraction (ϕ₂) is approximately 0.50%, while in Model-2, it is around 0.35%.For the stretching ratio, Model -1 shows a transfer rate increase of about 29.01%, while Model -2 shows an increase of approximately 29.12%.Radiation effects expand the momentum layer and enhance the primary velocity in both cases. Model 2 demonstrates higher accuracy and efficiency for practical applications. Residual analysis confirms model reliability, with Model-1 at 98.73 % and Model-2 at 99.15%. These findings inform parameter optimization in heat transfer applications, particularly in thermal management systems and heat exchangers.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".