MétaCan
Menu
Back to cohort
Record W4321457662 · doi:10.3390/su15053875

Contribution of Air Management to the Energy Efficiency of Water Pipelines

2023· article· en· W4321457662 on OpenAlexaff
Elias Tasca, Mohsen Besharat, Helena M. Ramos, Edevar Luvizotto, Bryan Karney

Bibliographic record

VenueSustainability · 2023
Typearticle
Languageen
FieldEngineering
TopicWater Systems and Optimization
Canadian institutionsUniversity of Toronto
FundersCoordenação de Aperfeiçoamento de Pessoal de Nível Superior
KeywordsPipeline transportEnvironmental scienceWork (physics)ElectricityAirflowEnvironmental engineeringEnergy consumptionEngineeringMechanical engineering

Abstract

fetched live from OpenAlex

Water conveyance systems are notorious for incurring considerable energy expenditures, either as losses of gravitational potential energy or as increased electricity consumption. Entrapped air pockets, originating from ineffective or nonexistent air management schemes, are common and often significant contributors to these energy costs. This work summarizes the detrimental influence of entrapped air on the energetics and conveyance capacity of pressurized pipelines and identifies those conditions that typically result in temporary or persistent air accumulations. Gravity and pumped lines are considered and gravity lines are shown to be more prone to the negative effects of entrapped air. In addition, initially robust air management strategies can gradually degrade if poorly adjusted to evolving circumstances. The paper critically assesses two common air management strategies: through employing air valves or by air removal by hydraulic means—that is, by considering a line’s configuration along with an attempt to predict the necessary flow conditions for the hydraulic removal of entrapped air.

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: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.581
Threshold uncertainty score0.130

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 head0.004
GPT teacher head0.201
Teacher spread0.198 · 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

Citations7
Published2023
Admission routes1
Has abstractyes

Explore more

Same venueSustainabilitySame topicWater Systems and OptimizationFrench-language works237,207