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Record W4212928198 · doi:10.1016/j.csite.2022.101873

Numerical investigation of the flow dynamics and heat transfer in a rectangular shell-and-tube heat exchanger

2022· article· en· W4212928198 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueCase Studies in Thermal Engineering · 2022
Typearticle
Languageen
FieldEngineering
TopicHeat Transfer and Optimization
Canadian institutionsUniversité de Sherbrooke
Fundersnot available
KeywordsBaffleShell and tube heat exchangerMechanicsTurbulenceHeat exchangerReynolds numberConcentric tube heat exchangerMaterials sciencePrandtl numberThermodynamicsHeat transferShell (structure)PhysicsComposite material

Abstract

fetched live from OpenAlex

Numerical simulations have been carried out to investigate the turbulent flows and coupled conductive/convective heat transfers in a glycol to water shell-and-tube heat exchanger (one shell, one tube doing six passes). The refrigerant (glycol) flows in the tube, and a secondary fluid (water) flows in the shell. The k-ω SST model is used to close the system of equations in the turbulent regime. The present model is first favorably compared and validated against experimental and numerical researches for a concentric annular heated pipe for two radius ratios (R∗=0.1and0.5) at a bulk Reynolds number ReDh=8900 and a Prandtl number Pr=0.71. It is then extended to consider the shell-and-tube configuration with a rectangular shell. Its thermo-hydraulic performances in the tube side are quantified for different Reynolds numbers at the cold inlet ranging from 1.03 × 103 to 1.47 × 105. The performance of the heat exchanger is then enhanced by introducing baffles in the rectangular shell. The results are finally discussed in terms of three global performance metrics.

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.

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 score0.009
Threshold uncertainty score0.467

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 head0.012
GPT teacher head0.210
Teacher spread0.197 · 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