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Record W3204823241 · doi:10.1115/fedsm2021-65814

Numerical Simulation of a Canadian Well With Several Circumferential Rows of Internal Vortex Generators

2021· article· en· W3204823241 on OpenAlexaboutno aff
Nabil Kharoua, Hamza Semmari, Houssem Korichi, Mehdi Haroun

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEnergy
TopicGeothermal Energy Systems and Applications
Canadian institutionsnot available
Fundersnot available
KeywordsVortex generatorHeat transferReynolds numberHeat exchangerHeat transfer coefficientMechanicsFluentInletEnvironmental scienceVortexTurbulenceWork (physics)ThermalMeteorologyComputer simulationMarine engineeringMechanical engineeringEngineeringPhysics

Abstract

fetched live from OpenAlex

Abstract Canadian Wells exploit the quasi-stable underground temperature throughout the year for cooling and heating applications. This type of heat exchangers is used in residential buildings, agriculture and industry. Implementing Vortex Generators (VGs) is intended to disturb the thermal and dynamic boundary layers developing in the near-wall regions leading to the increase of the heat transfer coefficient. The present work investigates the positive effects of a sequence of several rows of VGs. The commercial code ANSYS FLUENT was used to perform numerical simulations mimicking the variation of the seasonal operational conditions occurring within one year. The ambient conditions were considered for the city of Constantine located in the east of Algeria at an altitude of 600m over the sea level. Sinusoidal functions of time and depth, were used for the yearly variations of the ground and air temperatures. Parallelepiped VGs were considered in this study. The Reynolds number was in the range Re = 14975–42789. The results illustrated a contrasting effect of the Reynolds number on the heat transfer coefficient and the temperature difference between the inlet and outlet of the Canadian Well. In terms of number of VGs rows, the beneficial heat transfer effects were observed till the fifth row only.

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 categoriesInsufficient payload (model declined to judge)
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 score0.251
Threshold uncertainty score0.999

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)0.0020.000

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 head0.010
GPT teacher head0.212
Teacher spread0.202 · 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
Published2021
Admission routes1
Has abstractyes

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