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Investigations of Rotational Speed and Flow Rate on Centrifugal Pump Performance Using CFD

2022· article· en· W4292073230 on OpenAlexaff
Tianyu Sun, Renkun Wang, Zihao Zeng

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicCavitation Phenomena in Pumps
Canadian institutionsOkanagan University CollegeUniversity of British Columbia, Okanagan CampusUniversity of British Columbia
Fundersnot available
KeywordsCentrifugal pumpRotational speedMechanicsComputational fluid dynamicsRotodynamic pumpMass flow rateFluentVolumetric flow rateAxial-flow pumpFlow (mathematics)Internal flowSpecific speedPower (physics)Progressive cavity pumpMechanical engineeringPhysicsVariable displacement pumpEngineeringReciprocating pumpThermodynamicsImpeller

Abstract

fetched live from OpenAlex

Fluent is a professional CFD software, which is used to simulate and analyze fluid flow and heat exchange problems in complex geometric areas. It can accurately describe the internal flow field situation of the centrifugal pump model. The velocity and pressure distribution of the fluid in the centrifugal pump are obtained through the numerical simulation of the internal flow field in the centrifugal pump under the set rated and variable working conditions. Since one of the parameters that this experiment is working on, the rotating speed of the centrifugal pump was limited to a few certain values for the purposes of determining the head, power and pump efficiency when varying other parameters such as mass flow rate. Efficiency of centrifugal pump is calculated by the power output divided by power input. Power output can be calculated based on the density, gravitational acceleration, mass flow rate and pump head. Power input is calculated by moment and rotational speed. Mass flow rate is also an important parameter when determine the efficiency of the centrifugal pump. Based on the analysis and calculations above, several conclusions have been drawn regarding the mass flow rate, rotational speed, power and efficiency.

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 score0.010
Threshold uncertainty score0.459

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 head0.020
GPT teacher head0.213
Teacher spread0.192 · 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
Published2022
Admission routes1
Has abstractyes

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