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ICONE19-43503 DEVELOPING A HEAT-TRANSFER CORRELATION FOR SUPERCRITICALWATER FLOWING IN VERTICAL TUBES AND ITS APPLICATION IN SCWRS

2011· article· en· W1908277944 on OpenAlexaffabout
Sahil Gupta, Sarah Mokry, Igor Pioro

Bibliographic record

VenueThe Proceedings of the International Conference on Nuclear Engineering (ICONE) · 2011
Typearticle
Languageen
FieldEngineering
TopicHeat transfer and supercritical fluids
Canadian institutionsOntario Tech University
Fundersnot available
KeywordsSupercritical fluidThermodynamicsReynolds numberHeat transfer coefficientHeat transferMechanicsMass fluxHeat fluxWork (physics)Materials scienceSupercritical flowFlow (mathematics)FluentComputational fluid dynamicsPhysicsTurbulence

Abstract

fetched live from OpenAlex

This paper presents an analysis of new heat-transfer correlations developed for supercritical water flowing in vertical bare tubes. It is an extension of the previous work performed at the University of Ontario Institute of Technology. A large dataset within conditions similar to those of SuperCritical Water-cooled nuclear Reactors (SCWRs) was obtained from the Institute for Physics and Power Engineering (Obninsk, Russia). The experimental dataset was obtained in supercritical water flowing upward in a 4-m long stainless-steel vertical bare tube with 10-mm internal diameter. The data points were collected at pressure of about 24 MPa, inlet temperatures from 320 to 350℃, values of mass flux ranged from 200 to 1500 kg/m^2s and heat fluxes up to 1250 kW/m^2 for several combinations of wall and bulk-fluid temperatures that were below, at, or above the pseudocritical temperature. Previous studies have shown that existing correlations, such as the Dittus-Boelter, Bishop et al., and Jackson correlations, deviate significantly from experimental Heat Transfer Coefficient (HTC) values, especially, within the pseudocritical range. The Swenson et al. correlation provided a relatively improved fit for the experimental data, as compared to the previous three correlations within some flow conditions, but deviates from data within other conditions. Also, HTC and wall temperature values calculated with the FLUENT CFD code might deviate significantly from the experimental data, for example, the k-ε model (wall function). However, the k-ε model (low Reynolds numbers) shows better fit within some flow conditions. Therefore, a new empirical correlation based on the Swenson et al. approach was developed. In this approach, the majority of thermophysical properties are obtained at the wall temperature as opposed to those obtained at the bulk-fluid temperature in previous correlations, which is considered as the conventional approach. Statistical error calculations were performed using analytical and graphical techniques. Results showed that calculated wall temperatures according to the new correlation were within ±10% and HTC values were within ±25% for the analyzed dataset. The correlation was also compared against data from other datasets. The proposed correlation can be used: (1) for a preliminary heat-transfer calculations in SCWR fuel channels as a conservative approach; (2) for calculations of supercritical-water heat-transfer in heat exchangers in SCWR indirect-cycle concepts; (3) for calculations of heattransfer in heat exchangers for the co-generation of hydrogen at supercritical water NPPs; (4) for calculations of SCW heat-transfer in heat exchangers for other Generation IV reactor concepts with an indirect cycle; (5) for future comparisons with other independent datasets and with bundled data; (6) for the verification of computer codes for SCWR core thermalhydraulics; and (7) for the verification of scaling parameters between water and modeling fluids (CO_2, refrigerants, etc).

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 categoriesnone
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 score0.800
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 head0.032
GPT teacher head0.220
Teacher spread0.188 · 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.

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

Citations4
Published2011
Admission routes2
Has abstractyes

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