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Record W4409172556 · doi:10.1063/5.0260912

Air demand in plunging dropshafts of medium height

2025· article· en· W4409172556 on OpenAlex
Jiachun Liu, Biao Huang, David Z. Zhu

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenuePhysics of Fluids · 2025
Typearticle
Languageen
FieldEngineering
TopicCyclone Separators and Fluid Dynamics
Canadian institutionsUniversity of Alberta
FundersNatural Science Foundation of NingboNational Natural Science Foundation of China
KeywordsPhysicsMechanicsMeteorologyAtmospheric sciences

Abstract

fetched live from OpenAlex

Falling water in dropshafts can induce a significant amount of airflow into sewer systems. In this study, the air demand in plunging-type dropshafts with medium drops of 3.38 and 1.88 m were investigated. The downward movement of air in the dropshaft is influenced by two components of falling water: the annular flow attached to the shaft wall and the bounced flow (in the forms of mainstream or water droplets). A model for predicting air pressure gradients was developed and experimentally validated. The air demand increased with the air inlet area, the drop height, the water flow rate, and the ratio of the bounced jet to the total water flow rate. A prediction model for the air demand in dropshafts was developed, thus it is possible to assess the air demand of dropshafts across a range of drop heights, diameters, and inflow conditions. When the air inlet to the shaft is restricted, a large negative pressure is generated inside the shaft, increasing the height of the water-air mixture generated by the bottom jet impact, which reduces the impact pressure at the bottom.

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.

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 score0.487
Threshold uncertainty score0.466

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 head0.216
Teacher spread0.213 · 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