Numerical solution of bio-nano-convection transport from a horizontal plate with blowing and multiple slip effects
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Bibliographic record
Abstract
In this paper, a new bio-nano-transport model is presented. The effects of first- and second-order velocity slips, thermal slip, mass slip, and gyro-tactic (torque-responsive) microorganism slip of bioconvective nanofluid flow from a moving plate under blowing phenomenon are numerically examined. The flow model is expressed by partial differential equations which are converted to a similar boundary value problem by similarity transformations. The boundary value problem is converted to a system of nonlinear equations which are then solved by a Matlab nonlinear equation solver fsolve integrated with a Matlab ODE solver ode15s . The effects of selected control parameters (first order slip, second order slip, thermal slip, microorganism slip, blowing, nanofluid parameters) on the non-dimensional velocity, temperature, nanoparticle volume fraction, density of motile micro-organism, skin friction coefficient, heat transfer rate, mass flux of nanoparticles and mass flux of microorganisms are analyzed. Our analysis reveals that a higher blowing parameter enhances micro-organism propulsion, flow velocity and nano-particle concentration, and increases the associated boundary layer thicknesses. A higher wall slip parameter enhances mass transfer and accelerates the flow. The MATLAB computations have been rigorously validated with the second-order accurate finite difference Nakamura tri-diagonal method. The current study is relevant to microbial fuel cell technologies which combine nanofluid transport, bioconvection phenomena and furthermore can be applied in nano-biomaterials sheet processing systems.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it