Numerical Study of Unsteady Boundary Layer Flow of a Biomagnetic Fluid over a Horizontal Stretching Sheet with Magnetic Dipole
Bibliographic record
Abstract
This paper focuses on the theoretical and numerical investigation of the unsteady, viscous, incompressible, two-dimensional laminar boundary layer flow of a Newtonian biomagnetic fluid over a stretching sheet under the influence of an applied magnetic field in the presence of heat transfer. The magnetic field is induced by a magnetic dipole placed below the stretching sheet. The magnetic field intensity represents the magneto-thermo-mechanical coupling. This allows exclusion of the biofluid that is distant from the sheet at Curie temperature to avoid further magnetization. The unsteadiness of the flow is discernible in the fluid flow properties. The mathematical model of the problem conforms to the principles of Magnetohydrodynamics (MHD) and Ferrohydrodynamics (FHD). In this work, the study is performed on a specific biofluid, human blood. The modified Stokes principle is used to implement the model under the assumption that along with the three thermodynamic variables P, ρ, and T, the Biomagnetic Fluid Dynamics (BFD) fluid behavior can be characterized as a function of magnetization M. To describe the physical problem, a coupled non-linear system of ordinary differential equations subject to appropriate boundary conditions is derived from Navier-Stokes and thermal energy equations by performing non-dimensionalization of the considered variables. To solve these equations, the dsolve routine in the MAPLE software is used. Numerical results for flow profiles and the local skin friction coefficient (Cfx) and the local Nusselt number (Nux) are discussed for different values of unsteadiness parameter (A), biomagnetic interaction parameter (B) and a rational quantity (ϵ). The achieved results are compared with previously published work for steady state flow, and they seem to be in good agreement. It is found that MHD and FHD interaction parameters affect significantly on the velocity, temperature and pressure field. A successful completion will bring interesting results for better understanding of the biomagnetic fluid flow characteristics and can be beneficial to medical and bioengineering applications; particularly for estimating the characteristics of blood flow in stenosed arteries.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".