A CONSTRUCTAL HEMODYNAMIC STUDY OF BYPASS GRAFTS WITH SIZE CONSTRAINT
Bibliographic record
Abstract
A blood vessel bypass is a common way to restore blood flow due to blocked or narrowed arteries allowing oxygen-rich blood to be routed to the tissues. Herein, using a three-dimensional numerical simulation, the response of various vessel bypass designs to blood flow under size-limiting constraints is explored and compared to the flow in healthy arteries. Finding the best design requires a size constraint in the analysis; otherwise, the result is a configuration with excessive size in a limited allocated space, which represents a waste of material and an unnecessary space occupied by it. This study unveils the geometrical features of bypass grafts that have structural integrity while also minimizing the rate of entropy generation under volume constraint (constructal design). In a stenosed vessel with a bypass, the effect of bypass geometry, graft-vessel(host) diameter ratio, and stenose degree is analyzed and compared to a healthy vessel. This study concludes, among other things, that leaving the stenosed region of the vessel permeable to blood flow is only safe if the degree of stenosis is less than 0.5, both in terms of not being significantly different from flow conditions in a healthy vessel and also in terms of the structural integrity of the graft. The results presented here can be applied to any bypass graft and provide designers and practitioners with basic information.
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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) | 0.000 | 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".