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Record W2285098331 · doi:10.1115/1.4032727

Multi-Objective Optimization of a Steam Surface Condenser Using the Territorial Particle Swarm Technique

2016· article· en· W2285098331 on OpenAlexaff
Pooya Mirzabeygi, Chao Zhang

Bibliographic record

VenueJournal of Energy Resources Technology · 2016
Typearticle
Languageen
FieldEngineering
TopicHeat Transfer and Boiling Studies
Canadian institutionsWestern University
Fundersnot available
KeywordsCondenser (optics)Computational fluid dynamicsParticle swarm optimizationHeat exchangerCondensationWork (physics)MechanicsHeat transferTube (container)Flow (mathematics)Pressure dropSurface condenserFluid dynamicsMechanical engineeringMaterials scienceComputer scienceEngineeringThermodynamicsSuperheated steamPhysicsSteam turbineAlgorithm

Abstract

fetched live from OpenAlex

The multi-objective territorial particle swarm optimization (MOTPSO) technique is proposed in this work for the optimal design of steam surface condensers. The main objective of this work is to maximize the condensation rate in a condenser while the pressure loss is minimized. Various design parameters, such as the tube outside diameter, thickness, and pitch, are considered to find the optimal ones for shell and tube heat exchangers considered in this study. The two-dimensional computational fluid dynamics (CFD) analysis is performed to solve the fluid flow and heat transfer in the condenser to assess the performance of different designs.

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 score0.261
Threshold uncertainty score0.260

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 head0.013
GPT teacher head0.235
Teacher spread0.222 · 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

Citations8
Published2016
Admission routes1
Has abstractyes

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