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Record W4408663586 · doi:10.1007/s11269-025-04160-3

Laboratory-Validated Model of Air Discharge at User Connections under Intermittent Water Supply

2025· article· en· W4408663586 on OpenAlexaff
Marco Ferrante, Francesco Casinini, David Meyer

Bibliographic record

VenueWater Resources Management · 2025
Typearticle
Languageen
FieldEngineering
TopicWater Systems and Optimization
Canadian institutionsUniversity of Toronto
FundersUniversità degli Studi di Perugia
KeywordsHydrogeologyEnvironmental scienceWater dischargeWater supplyHydrology (agriculture)Environmental engineeringWater resource managementEngineeringGeotechnical engineering

Abstract

fetched live from OpenAlex

Abstract The cyclic nature of intermittent supply subjects the network to frequent (partial) draining and filling. During filling, water displaces air in the pipes, and much of that air is discharged through user connections and water meters, causing over-reading and reliability issues. Important laboratory experiments have measured air discharge through meters during filling, but have not yet quantified or modeled the role of air compression. This paper revisits and augments these pipe-filing experiments, proposing, calibrating and interpreting a numerical model for the compressible flow of air during pipe filling. The rapid filling of a distribution-scale (1500 L) pipe compressed the air by 1.3 -2.5 fold, broke two-thirds of the tested water meters, and caused water meters to register 33-63% of the volumetric air flow as if it were water (500-1000 L). This partial registration was primarily caused by compression. Our results quantitatively relate air discharge to water meter over-registration, emphasizing the importance of gradual filling in intermittent systems. For meter protection, we recommend limiting filling speeds to less than the nominal flow rate of all downstream water meters.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.708
Threshold uncertainty score0.500

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.007
GPT teacher head0.191
Teacher spread0.184 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreEmpirical

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".

Quick stats

Citations1
Published2025
Admission routes1
Has abstractyes

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