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Record W3021140832 · doi:10.1016/j.heliyon.2020.e03823

Free convection of a suspension containing nano-encapsulated phase change material in a porous cavity; local thermal non-equilibrium model

2020· article· en· W3021140832 on OpenAlexaff
Mohammad Ghalambaz, Seyed Mohsen Hashem Zadeh, S.A.M. Mehryan, Amir Haghparast, Hossein Zargartalebi

Bibliographic record

VenueHeliyon · 2020
Typearticle
Languageen
FieldEngineering
TopicPhase Change Materials Research
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsSuspension (topology)Phase changePorous mediumMaterials scienceNano-ThermalPhase-change materialPorosityNatural convectionMechanicsThermodynamicsConvectionChemical engineeringComposite materialPhysicsEngineering

Abstract

fetched live from OpenAlex

Due to the instinctive temperature-dependent heat capacity of the Nano-Encapsulated Phase Change Material (NEPCM), there is a growing interest in the potential applications of such materials in heat transfer. As such, steady-state natural convection in a porous enclosure saturated with nanofluid using NEPCMs has been investigated in this study. The cavity is assumed to have constant hot and cold temperatures at the left and right vertical boundaries, respectively, and fully insulated from the bottom and top walls. Considering the Local Thermal Non-equilibrium (LTNE) approach for the porous structure, the governing equations are first non-dimensionalized and then solved by employing the finite element Galerkin method. The impact of different parameters, such as porous thermal conductivity ( k s ), solid-fluid interface heat transfer (10 ≤ H ≤ 10 5 ), Stefan number (0.2 ≤ Ste ≤ 1), and volume fraction of nanoparticles (0.0 ≤ φ ≤ 0.05) on the patterns of the fluid and solid isotherms, streamlines and the contours of the heat capacity ratio, fusion temperature (0.05 ≤ θ f ≤ 1), local and average Nusselt numbers, and overall heat transfer ratio has been studied. It is shown that improving the porous thermal conductivity not only leads to an increase in the rate of heat transfer but also augments the fluid flow inside the cavity. For low values of the Ste , the rate of heat, transferred in the porous enclosure, is intensified. However, regardless of the amount of the Stefan number, the maximum rate of heat transfer is achievable when the non-dimensional fusion temperature is approximately 0.5. Employing NEPCMs in a highly conductive porous structure is more efficacious only when the phases are in the state of local thermal equilibrium. Nonetheless, the rate of heat transfer is higher when the Local thermal non-equilibrium is validated between the phases. Besides, for poor thermal conductivity of the porous medium like glass balls (LTE condition), adding 5% of the nano-encapsulated phase change materials to pure water can boost the rate of heat transfer up to 47% (for Ste = 0.2 and θ f = 0.5). This thermal investigation of NEPCMs shows in detail how advantageous are these nanoparticles in heat transfer and opens up an avenue for further application-based studies.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.006
Threshold uncertainty score0.011

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.058
GPT teacher head0.285
Teacher spread0.227 · 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 source (direct Gemma or distilled Codex), 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

Citations57
Published2020
Admission routes1
Has abstractyes

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