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Record W2940849447 · doi:10.1177/0957650919844112

Coupling of inverse method and cuckoo search algorithm for multiobjective optimization design of an axial flow pump

2019· article· en· W2940849447 on OpenAlexaff
Mohamed Abdessamed Ait Chikh, Idir Belaidi, Sofiane Khelladi, Abderrachid Hamrani, Farid Bakir

Bibliographic record

VenueProceedings of the Institution of Mechanical Engineers Part A Journal of Power and Energy · 2019
Typearticle
Languageen
FieldEngineering
TopicCavitation Phenomena in Pumps
Canadian institutionsMcGill University
Fundersnot available
KeywordsCuckoo searchMulti-objective optimizationTurbomachineryCentrifugal pumpRotor (electric)Mathematical optimizationGenetic algorithmMathematicsSortingMaximizationControl theory (sociology)Computer scienceAlgorithmEngineeringParticle swarm optimizationMechanical engineering

Abstract

fetched live from OpenAlex

This work describes the application of a multiobjective cuckoo search method for turbomachinery design optimization of an axial pump. Maximization of the total efficiency and minimization of the required net positive suction head of the pump are the two objective functions considered for the optimization problem. The optimization process is carried out on a range of imposed volumetric flow rates, with taking into account at each discretized radius between the hub and tip of the rotor: the profile camber, rotor wall thickness, angular deviation, and the solidity, regarded as geometrical constraints and nominal flow rate as mechanical constraint. Two strategies are proposed in order to solve the problem. In the first one, three forms of mono-objective model with two variables, total efficiency and net positive suction head, are considered. In the second one, a multiobjective model with nondominated sorting scheme is adopted. A comparative evaluation of results obtained from the proposed approach with those of a reference machine and genetic algorithm allowed us to validate the present work.

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: Simulation or modeling
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.687
Threshold uncertainty score0.381

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.235
Teacher spread0.223 · 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
GenreMethods

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

Citations12
Published2019
Admission routes1
Has abstractyes

Explore more

Same venueProceedings of the Institution of Mechanical Engineers Part A Journal of Power and EnergySame topicCavitation Phenomena in PumpsFrench-language works237,207