MétaCan
Menu
Back to cohort
Record W2974248372 · doi:10.1080/01457632.2019.1661666

Heat Transfer and Entropy Generation in Concentric/Eccentric Double-Pipe Helical Heat Exchangers

2019· article· en· W2974248372 on OpenAlexaff
Jundika C. Kurnia, Seyed Ali Ghoreishi‐Madiseh, Agus P. Sasmito

Bibliographic record

VenueHeat Transfer Engineering · 2019
Typearticle
Languageen
FieldEngineering
TopicHeat Transfer and Optimization
Canadian institutionsMcGill UniversityUniversity of British Columbia
FundersMinistère de l'Education Nationale, de l'Enseignement Superieur et de la RechercheMinistry of Higher Education, Malaysia
KeywordsMechanicsLaminar flowHeat transferMaterials scienceHeat exchangerThermodynamicsAnnulus (botany)Pressure dropMass flow ratePipe flowPhysicsTurbulenceComposite material

Abstract

fetched live from OpenAlex

Double-pipe helical heat exchangers are integral to contemporary mechanical refrigeration equipment. Modification of flow geometry has been widely adopted to enhance heat transfer performance of a heat exchanger. The objective of this study is to numerically investigate heat transfer and entropy generation in a double pipe helical heat exchanger with various cross-sections. A computational model for laminar convective heat transfer was developed and validated against the results from previously published literature. To capture entropy generation, the entropy balance equation for open system is adopted. Effect of inner pipe Dean number, inner pipe and annulus inlet mass flow rate ratio, eccentricity, and flow configuration (co-flow and counter-flow) were examined and discussed in light of computational results. To ensure fair comparison, the considered geometries have same inner pipe cross-section area, same annulus cross-section area, and same outer surface area of inner pipe. The results suggest that square cross-section offers best performance in term of heat transfer, pressure drop and entropy generation. In addition, concentric configuration is more appropriate for low flow rate application while eccentric outer configuration is more suitable for high flow rate application.

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)0.000
metaresearch head score (Gemma)0.000
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 score0.001
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.012
GPT teacher head0.200
Teacher spread0.187 · 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

Citations14
Published2019
Admission routes1
Has abstractyes

Explore more

Same venueHeat Transfer EngineeringSame topicHeat Transfer and OptimizationFrench-language works237,207