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Record W3117891474 · doi:10.1115/1.4049402

Special Section on Flow Physics of Supercritical Fluids in Engineering

2020· article· en· W3117891474 on OpenAlexaff
Gongnan Xie, Kamel Hooman, Stavros Tavoularis, Bengt Sundén, Sandra K. S. Boetcher

Bibliographic record

VenueJournal of Fluids Engineering · 2020
Typearticle
Languageen
FieldEngineering
TopicHeat transfer and supercritical fluids
Canadian institutionsUniversity of Ottawa
Fundersnot available
KeywordsSupercritical fluidHeat transferHeat exchangerMechanical engineeringMicro heat exchangerThermal scienceCritical heat fluxMechanicsBuoyancyThermal fluidsMaterials scienceThermodynamicsNuclear engineeringEngineeringHeat fluxConvective heat transferPhysicsPlate heat exchanger

Abstract

fetched live from OpenAlex

Supercritical-fluid applications have recently received extensive attention. Applications of supercritical flows include thermal power generation, cooling of scramjet engines, highly efficient compact power systems, concentrated solar power, nuclear power, geothermal energy, refrigeration systems, and waste-heat utilization systems.Supercritical flow characteristics, particularly for the region in the vicinity of the pseudo-critical point, need to be investigated and analyzed to better understand the distinguished behavior of these flows, such as heat transfer deterioration and enhancement. Therefore, the main motivation of this special section of the ASME Journal of Fluids Engineering is to bring together original articles on recent developments in the field of supercritical fluids in engineering applications. After careful reviews of all the submissions, three papers were finally accepted for this special section.The papers selected include an experimental study of bulk-mode thermoacoustic instabilities of supercritical methanol flowing in a heated tube, a numerical study of the turbulent heat transfer of natural gas in a heat exchanger during trans-critical liquefaction, and a study using the buoyancy parameter to understand the deterioration of heat transfer to or from upward supercritical fluid flows. We hope this special section raises the impact and application of supercritical fluids in the industry while helping the visibility of the ASME Journal of Fluids Engineering.Although research on supercritical flow and heat transfer is very attractive and has received much attention, it is hardly possible for this section to cover most related topics. Some other important topics, such as asymmetrical heat flux condition, conjugate heat transfer in heat exchangers, new turbulence models for supercritical flows, microgravity environment, and accelerating/decelerating state are not presented here.Finally, we would like to thank the Editor-in-Chief of the ASME Journal of Fluids Engineering, Professor Francine Battaglia, for giving us the opportunity to edit the special section on a high-impact topic. We also thank the Assistant to the Editor-in-Chief, Ms. Colette Montague, for her technical support. Finally, we thank all the authors and reviewers who contributed to this special section.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.382
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.208
Teacher spread0.195 · 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.

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

Citations1
Published2020
Admission routes1
Has abstractyes

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