Constructal tree-shaped water distribution networks by an environmental approach
Bibliographic record
Abstract
Flow systems most often present thermal, mechanical and chemical losses due to irreversibility during the fl owing fl uid transport.These losses strongly impact both their energy performances and the fl owing fl uid quality.In this paper, two effective, drinking water and irrigation, tree-shaped networks (from the fl uid quality and energy performance points of view) are constructed by using the constructal approach coupled with the exergy destruction minimization method.It is shown that in the construction of tree-shaped network for water distribution, the method of exergy destruction minimization is equivalent to minimizing mechanical irreversibility (this is equivalent to pumping power) under a water quality constraint.For both phenomena occurring in the network (energy consumption and the fl uid quality degradation), this study offers new interesting routes for optimizing the system either by the exergy destruction minimization (in that case, both irreversible processes are taken into account in the design procedure) or by minimizing one of the two irreversible processes, the other being taken into account as the design constraint.The originality of the method relies on the introduction of the environmental protection through the control of the fl owing fl uid quality.This paper shows that, for the performance improvement of a water distribution network, it is important to focus on the design of the network rather than enhancing only the transport properties.Note fi nally that the focus on the quality in fl ow systems is a crucial approach in environmental engineering such as drinking water distribution systems or chemical fl uids transfer systems.The approach presented in this paper should be seen as an introduction to reactive fl ow systems designing by constructal approach.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".