Free convection of a suspension containing nano-encapsulated phase change material in a porous cavity; local thermal non-equilibrium model
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".