MétaCan
Menu
Back to cohort
Record W4382559954 · doi:10.18280/mmep.100334

Experimental Investigation of Thermo-Fluid Characteristics in Air Flow Through Corrugated Tubes with Various Configurations

2023· article· en· W4382559954 on OpenAlexvenueno aff
Salwa Ahmad Sarow, Saad Najeeb Shehab

Bibliographic record

VenueMathematical Modelling and Engineering Problems · 2023
Typearticle
Languageen
FieldEngineering
TopicCyclone Separators and Fluid Dynamics
Canadian institutionsnot available
FundersMustansiriyah University
KeywordsMechanicsMaterials scienceFlow (mathematics)Fluid dynamicsPhysics

Abstract

fetched live from OpenAlex

Engineering and industrial applications, such as heat exchangers, cooling systems, and solar collectors, require designs that optimize heat transfer rates and enhance thermal performance.Corrugated tubes present a viable solution for these applications.In this study, an experimental simulation method was employed to examine the thermo-fluid characteristics of air flow through a one-start horizontally spiraled corrugated tube under turbulent forced convection.A parametric investigation was carried out for various configurations of spirally corrugated tubes subjected to uniform heat flux.Three distinct types of spirally corrugated tubes were investigated: Continuous corrugated tubes with 6mm pitch, continuous corrugated tubes with 18mm pitch, and interrupted corrugated tubes with 6mm pitch.Additionally, a smooth tube was examined for comparison.All tubes were fabricated from copper, and the corrugations were created through cold forming.The effects of air velocity and wall heat flux on flow and heat transfer rates were analyzed, with five air velocities (ranging from 2.5 to 4.5m/s) and six surface heat fluxes (ranging from 5,000 to 10,000W/m 2 ) considered.Results revealed that the average Nusselt number for the interrupted corrugated tube was 72.5% higher than the smooth tube, 63.5% higher than the corrugated tube with an 18mm pitch, and 12.5% greater than the corrugated tube with a 6mm pitch.

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: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.351
Threshold uncertainty score0.622

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 head0.016
GPT teacher head0.199
Teacher spread0.183 · 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

Citations0
Published2023
Admission routes1
Has abstractyes

Explore more

Same venueMathematical Modelling and Engineering ProblemsSame topicCyclone Separators and Fluid DynamicsFrench-language works237,207