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Record W4200390715 · doi:10.5267/j.esm.2021.12.003

Prediction and investigation between impeller blade shape parameters and performance parameters in CFD and ANN

2021· article· en· W4200390715 on OpenAlexvenueno aff
Faye Jin, Ran Tao, Ruofu Xiao

Bibliographic record

VenueEngineering Solid Mechanics · 2021
Typearticle
Languageen
FieldEngineering
TopicCavitation Phenomena in Pumps
Canadian institutionsnot available
FundersNational Natural Science Foundation of China
KeywordsImpellerCentrifugal pumpBlade (archaeology)Computational fluid dynamicsSpecific speedPerformance predictionHead (geology)Structural engineeringMechanicsComputer scienceMechanical engineeringEngineeringSimulationPhysics

Abstract

fetched live from OpenAlex

The blade shape parameters have a remarkable effect on the centrifugal pump performance. In order to reveal the relationship between these parameters and pump performance, a single channel was regarded as the research object to calculate its performance by numerical simulation, and the performance was measured on an experimental rig. The optimized ANN is proposed, and it is proved to be highly accurate. The ANN correlation coefficient of the total response could be above 0.997 after thousands of retaining. The sorts and degrees affecting performance parameters were found out by gray relation analysis. It was found that the blade angles at the leading edge were more influential for reaction force, head and minimum pressure, while the wrap angles had greater impact for efficiency. Furthermore, a multiple linear regression model was established to quantify the weight and trend of the influence of blade shape parameters on performance. The results provide a reference guide for the optimized design of centrifugal impeller to improve pump performance.

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 score0.067
Threshold uncertainty score0.881

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 head0.015
GPT teacher head0.192
Teacher spread0.176 · 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
Published2021
Admission routes1
Has abstractyes

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