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Record W3132069837 · doi:10.2166/wst.2021.061

Numerical study on effects of chamber design and multi-inlet on storm geyser

2021· article· en· W3132069837 on OpenAlexaff
Jiachun Liu, Shuangqing Zhang, Biao Huang, David Z. Zhu

Bibliographic record

VenueWater Science & Technology · 2021
Typearticle
Languageen
FieldEngineering
TopicWater Systems and Optimization
Canadian institutionsUniversity of Alberta
FundersKey Research and Development Program of Zhejiang ProvinceFundamental Research Funds for the Provincial Universities of ZhejiangNatural Science Foundation of NingboNational Natural Science Foundation of China
KeywordsInletInflowPressure dropEnvironmental scienceBody orificeStormComputational fluid dynamicsMarine engineeringGeologyMechanicsEngineeringGeomorphologyMechanical engineering

Abstract

fetched live from OpenAlex

Storm geysers increasingly occur in sewer systems under climate change and rapid urbanization. Mitigation measures are in great demand to avoid safety problems. In this study, three-dimensional computational fluid dynamics models of single-inlet and multi-inlet systems were established to investigate geysering induced by rapid filling and assess the effectiveness of potential mitigation methods. The modeling results suggest that increasing the capacity of the downstream pipe before the inflow front reaches the chamber can effectively reduce the maximum geyser pressure. The peak pressure can be significantly mitigated when the chamber size is designed with care and the drop height between the upstream and downstream pipes is reduced. A diversion deflector with air vents and an orifice plate at the riser top end can alleviate the maximum pressure by about 65% with about 75% of the entrapped air being released. The peak pressure during the geyser event in the multi-inlet model is less than that of a single-inlet model under the same total inflow condition, but more water can be released.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.415
Threshold uncertainty score0.289

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 head0.012
GPT teacher head0.222
Teacher spread0.210 · 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 designBench or experimental
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

Citations3
Published2021
Admission routes1
Has abstractyes

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