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Record W4415500731 · doi:10.1061/jhend8.hyeng-14415

Deoxygenation of Water for Small-Scale Laboratory Studies in Hydraulic Engineering

2025· article· en· W4415500731 on OpenAlexaff
Pouria Rahmati, Susan Gaskin, Suk Yi Lo

Bibliographic record

VenueJournal of Hydraulic Engineering · 2025
Typearticle
Languageen
FieldEngineering
TopicHydraulic flow and structures
Canadian institutionsMcGill University
Fundersnot available
KeywordsDeoxygenationAerationResidence time (fluid dynamics)OxygenVolumetric flow rateCavitationDissolutionTap water

Abstract

fetched live from OpenAlex

Achieving low dissolved oxygen levels in water is required for rigorous investigation of aeration processes in laboratory experiments in hydraulic engineering. The effectiveness of ultrasonic irradiation, vacuum degassing, water spraying techniques, and combinations thereof for deoxygenating tap water is evaluated in a closed-loop laboratory setting. A parametric study assessed the impact of ultrasonic irradiation (20 kHz, 2 kW), vacuum surface pressure (16–101 kPa), initial water temperature (11°C–39°C), and reservoir residence time (68–250 s) for flow rates in the range of 2.0–7.3 L/s. Results demonstrate that ultrasonic irradiation at 20 kHz did not significantly enhance the deoxygenation rate compared to vacuum degassing alone, suggesting that higher frequencies may be required to induce effective acoustic cavitation. Vacuum degassing effectively reduced dissolved oxygen levels, achieving a reduction from 10 down to 2.4 mg/L within 30 min at 16 kPa surface pressure and 33°C. Water spraying in a vacuum marginally improved deoxygenation at higher surface pressures by increasing the air–water interfacial area but became less effective at lower pressures due to increased surface mixing. Higher initial water temperatures and lower residence times had a minor influence on the deoxygenation rate. The results enable the design of deoxygenation systems suited for small-scale closed-loop hydraulic laboratories to establish well-controlled initial dissolved oxygen conditions.

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)0.001
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 score0.442
Threshold uncertainty score0.978

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
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 head0.010
GPT teacher head0.238
Teacher spread0.228 · 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

Citations0
Published2025
Admission routes1
Has abstractyes

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