Entropy optimization in squeezed nanofluidic dissipative transport of radiative water conveying aluminum alloys
Bibliographic record
Abstract
In this paper, squeezing transport of radiative water conveying aluminum alloys (i.e., AA7072 and AA7075) mobilized by entropy generation and dissipative energy is analyzed. Problem is formulated in a rotating frame with the consideration of magnetohydrodynamics and Joule heating aspects. Maxwell model for nanofluid has been used to incorporate the thermophysical properties of nanoelements. Formulated governing expressions have been transformed into system of ODEs by introducing similarity variables. The transformed system of ODEs is then numerically solved by Runge–Kutta–Fehlberg (RKF) method based on shooting background. The physical quantities (i.e., skin-friction coefficient, Nusselt and Bejan numbers) of scientific interest are formulated and illustrated via various plots. Graphical representations of squeezing function, temperature profile and velocity profile have been made to examine the effects of involved parameters. Streamlines and isotherms patterns have been formed and discussed. To authenticate the validity of model, skin-friction values have been compared with published literature for limited version of the model. Entropy and temperature of the system are improved with the involvement of aluminum alloys in water. Symmetrical behavior of streamlines is observed for positive approach of squeezing parameter.
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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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".