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Record W4360998820 · doi:10.1016/j.rineng.2023.101053

Natural convection and entropy generation inside a square chamber divided by a corrugated porous partition

2023· article· en· W4360998820 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

fundA Canadian funder is recorded on the work.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueResults in Engineering · 2023
Typearticle
Languageen
FieldEngineering
TopicNanofluid Flow and Heat Transfer
Canadian institutionsnot available
FundersDepartment of Mechanical Engineering, University of AlbertaBangladesh University of Engineering and Technology
KeywordsNatural convectionPorosityPartition (number theory)Square (algebra)MechanicsEntropy (arrow of time)Porous mediumMaterials scienceMathematicsPhysicsThermodynamicsConvectionGeometryComposite materialCombinatorics

Abstract

fetched live from OpenAlex

A thorough investigation of free convection and entropy generation occurring inside a differentially heated square chamber that is filled with water and divided by a water-saturated, corrugated porous partition is performed in this numerical study. The governing mathematical equations, that describe the flow and heat transfer phenomena for fluid and porous domains, are the Navier-Stokes (modified for the porous domain using the Darcy-Brinkman-Forchheimer model) and thermal energy equations. Those systems of equations are solved using the Galerkin finite element analysis. Parametric changes are carried out for different positions, thicknesses, amplitudes, and frequencies of corrugation of the porous partition, and the corresponding results are quantitatively presented in terms of average Nusselt number along the heated wall and total entropy generation of the entire chamber with increasing Rayleigh number (10 3 ≤ Ra ≤ 10 7 ). Corresponding variations are qualitatively visualized in terms of streamlines and isotherms for comparing the related parametric changes. Furthermore, a comparative analysis is included between the use of porous and solid partitions along with a chamber without any partition. Conclusive results show that using a porous rather than a solid partition can increase the average Nusselt number by 26.28% at Ra = 10 5 up to a maximum of 565% at Ra = 10 7 . Similarly, lower thickness, higher frequency, and higher amplitude can increase the average Nusselt number by around 37.5%, 2.89%, and 1.17%, respectively.

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.

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 score0.693
Threshold uncertainty score0.683

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 head0.010
GPT teacher head0.201
Teacher spread0.191 · 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