Computational Investigation of Laminar Mixed Convection in a Vertical Pipe With Slurries of a Microencapsulated Phase-Change Material in Distilled Water
Bibliographic record
Abstract
A computational investigation of steady, laminar, mixed convection in a vertical pipe, with essentially uniform heat flux imposed on its outer surface and slurries of a microencapsulated phase-change material (MCPCM) particles suspended in distilled water as the working fluid flowing upwards, is presented. The MCPCM particles considered here have a core of solid-liquid PCM contained in a thin solid shell. The mean effective diameter of these particles is 2.5μm; the melting of the core PCM takes place primarily in the temperature range 26°C to 30°C; and the latent heat of fusion of this PCM is 129.5kJ/kg. The total length of the pipe is 2.2479m; and its inside and outside diameters are 0.01257m and 0.01588m, respectively. The main contributions of this paper are the following: i) a homogenous mathematical model is shown to be applicable to the aforementioned mixed convection phenomena; ii) correlations for the effective properties of the MCPCM slurries and procedures for their implementation are presented; iii) difficulties with the standard definition of bulk temperature when the specific heat of the fluid changes significantly with temperature are elaborated; iv) a modified bulk temperature that overcomes these difficulties is proposed; v) a finite volume method (FVM) for the solution of the proposed mathematical model is described briefly; and vi) the numerical results are presented, compared to complementary experimental data, and discussed. These comparisons show that proposed model and FVM allow cost-effective computer simulations of the problems of interest.
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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 | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".