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Record W3040337628 · doi:10.1002/htj.21847

Numerical evaluation and the effects of geometrical and operational parameters on thermal performance of the shell and double coil tube heat exchanger

2020· article· en· W3040337628 on OpenAlexaff
Mehdi Noorbakhsh, Mohsen Pourfallah, Seyed Soheil Mousavi Ajarostaghi, Mohammad Zaboli

Bibliographic record

VenueHeat Transfer · 2020
Typearticle
Languageen
FieldEngineering
TopicHeat Transfer and Optimization
Canadian institutionsUniversité de Sherbrooke
Fundersnot available
KeywordsMechanicsNusselt numberHeat transfer coefficientElectromagnetic coilMaterials scienceHeat transferHeat exchangerConcentric tube heat exchangerShell and tube heat exchangerPlate heat exchangerPressure dropShell (structure)Convective heat transferThermodynamicsComposite materialEngineeringPhysicsElectrical engineeringReynolds numberTurbulence

Abstract

fetched live from OpenAlex

Abstract Heat exchangers are extensively used in various industries. In this study, the impact of geometric and flow parameters on the performance of a shell and double helical coil heat exchanger is studied numerically. The investigated geometric parameters include external coil pitch, internal coil pitch, internal coil diameter, and coil diameter. The influences of considered geometrical parameters are analyzed on the output temperature of the hot and cold fluid, convective heat transfer coefficient, pressure drop, and average Nusselt number. Water is considered as working fluid in both shell and tube. As an innovation, double helical coils are used instead of one in the heat exchanger. To compare the obtained results accurately, in each section, the heat transfer area (coil outer surface) is kept constant in all models. The results show that the geometrical parameters of double helical coils significantly affect the heat transfer rate.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.002
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.016
GPT teacher head0.214
Teacher spread0.198 · 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 source (direct Gemma or distilled Codex), 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

Citations31
Published2020
Admission routes1
Has abstractyes

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