Experimental Validation of Numerical Simulations of a Closed-Loop Thermosyphon Operating With Slurries of a Microencapsulated Phase-Change Material
Bibliographic record
Abstract
Experimental validation and calibration of numerical simulations of a closed-loop thermosyphon operating under steady-state conditions with slurries of a microencapsulated phase-change material (MCPCM) suspended in distilled water are presented. The slurries exhibited a non-Newtonian, shear-thinning, power-law rheological behavior in the range of parameters considered; and the constants in the related model were calibrated using data from specially conducted experiments. The flows of these slurries in the problems of interest were laminar. Furthermore, the velocity and temperature differences between the dispersed and conveying phases of these slurries were negligibly small, so homogeneous models could be used for mathematical representations of the fluid flow and heat transfer phenomena. A hybrid numerical method was used in the simulations: detailed two-dimensional axisymmetric control-volume finite element (CVFEM) simulations of the heated and cooled sections of the thermosyphon were coupled with segmented quasi-one-dimensional finite volume (FVM) simulations of the other portions. The CVFEM and FVM used in this work are well-established. Thus, the verification of these methods is not addressed here. Rather, the details of the thermosyphon, effective properties of the MCPCM and slurries, overviews of the hybrid model and the aforementioned numerical methods, notes on the experimental calibration and validation, and some results are presented and discussed.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".