<b>Constructal microchannel networks of rarefied gas with minimal flow resistance</b>
Bibliographic record
Abstract
In this article, we answer the question of how to optimally design a rarefied gas distribution network from a source point to a given number of equidistant users such that the diameters of the pipes used to carry the fluid fall in the microscale. A slip boundary condition is used to take into account the effects introduced by the smallness of the pipes. By specifying the overall pressure drop across the network, we maximize the total mass flow rate through the dendritic structure under global volume constraint using an evolutionary algorithm. Four complexity levels are considered, nbif=0, 1, 2, and 3, where nbif is the number of levels of bifurcation present in the structure. The results show that the version of Murray’s law originally proposed in order to determine the optimal diameters of the pipes is not valid when rarefaction is present, since the power-law exponent varies significantly with the number of outlet users N. Additionally, the results show that the bifurcation angles decrease in the presence of rarefaction as N increases. The article ends by exploring the robustness of nonoptimized complex gas distribution networks.
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How this classification was reachedexpand
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) | 0.000 | 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) | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".