New simple analytical method for flow enhancement predictions of pulsatile flow of a structured fluid
Bibliographic record
Abstract
In this work, a new simplified method to find the fluidity enhancement of a non-Newtonian liquid under a pulsating (time-dependent) pressure gradient is analyzed. The fluidity enhancement is predicted by means of a Taylor series expansion of the flow rate in the vicinity of the applied wall stress. This expansion is shown to render the same results as several perturbation techniques used at length in the literature. Both new and the conventional perturbation methods are equivalent in their predictions of the fluidity enhancement. Even though the flow and rheology behavior are modeled using the Bautista-Manero-Puig constitutive equation, it is shown that the prediction of the fluidity enhancement does not depend on the constitutive model employed, but a condition of shear thinning behavior of the fluid is necessary for it. Flow enhancement is predicted using rheological data for blood since this fluid naturally flows under a pulsatile pressure gradient. The flow enhancement equation is found to have a similar form as the equation of the Rabinowitsch formalism in fully developed Poiseuille flow. This simplified technique will help in saving machine time for numerical predictions in computational blood flow simulations.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".