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Record W2480304331

Specifics of forced-convective heat transfer to supercritical CO2 flowing upward in vertical bare tubes

2015· dissertation· en· W2480304331 on OpenAlexfundaboutno aff
Eugene Saltanov

Bibliographic record

Venuee-scholar@UOIT (University of Ontario Institute of Technology) · 2015
Typedissertation
Languageen
FieldEngineering
TopicHeat transfer and supercritical fluids
Canadian institutionsnot available
FundersNatural Resources CanadaNatural Sciences and Engineering Research Council of CanadaUniversity of Ontario Institute of Technology
KeywordsSupercritical fluidConvective heat transferConvectionMechanicsHeat transferMaterials scienceForced convectionThermodynamicsEnvironmental sciencePhysics
DOInot available

Abstract

fetched live from OpenAlex

Heat transfer in the forced convection regime of fluids at supercritical conditions has been studied extensively for the past 60 years. The dominant approach to summarize the experimental results was by proposing empirical correlations for the data within the investigated range of parameters. It was soon realized by researchers worldwide that heat transfer coefficients become non-linear functions of wall and bulk-fluid temperatures at certain combinations of experimental parameters within the region of the peak of specific heat at supercritical pressures. Thus, it has become a standard approach to remove nonlinear experimental heat transfer coefficient values treating them as a sign of a deteriorated (as opposed to normal) heat transfer regime. There were recent attempts to address this shortcoming and extend the applicability of conventional empirical correlations to the deteriorated heat transfer regime. However, these attempts were not satisfactory.\nIn this thesis, a new methodology has been developed that allows the use conventional empirical correlations without distinguishing entrance effects or deteriorated heat transfer regime. The methodology is based on binning experimental data according to the parameter X = (h_b - h_pc) / (q/G) and then combining correlations based on wall and bulk-fluid temperature on each bin to minimize RMS and maximal overprediction of heat transfer coefficients within each of the bins.\nUsing this methodology, 95% of normal heat transfer data were predicted with a spread of ??19%, which is 1.74 times narrower compared to the prediction by the empirical correlations developed based on the conventional methodology and on the same data; while all the data (2786 points, including entrance effects and deteriorated heat transfer) were predicted with a spread of ??20% (based on 2??-level). The data correlated based on the new methodology where obtained within the following range of experimental parameters: P = 7.58 ??? 8.91 MPa, Tb = 20 ??? 142 ??C, Tw = 32 ??? 231 ??C, G = 885 ??? 3048 kg/m2s, q = 26 ??? 616 kW/m2K, D = 8.1 mm.\nThe experimental data were obtained based on a series of tests on supercritical CO2 flowing upwards in a bare tube at the MR-1 loop (located in Chalk River) of the former Atomic Energy of Canada Limited (AECL). Normal, deteriorated, and improved heat transfer regimes were covered in the experiments.

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), Research integrity
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 score0.104
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0010.002
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 head0.216
Teacher spread0.203 · 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 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

Citations5
Published2015
Admission routes2
Has abstractyes

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