Pore network modeling of one-dimensional transient heat transfer behaviorof core-shell paraffin-based composites
Bibliographic record
Abstract
Core-shell composite phase change materials (PCMs) with a PCM core and non-PCM shell are promising for the latent heat thermal energy storage process. While the non-PCM shell layer could enhance the heat conductivity and cyclic stability of the material, it inevitably reduces the heat storage density of the composite materials. It is important to understand how various structural parameters impact the thermal conductivity of the core-shell composites and estimate the effective heat transfer coefficient and energy storage density of the core-shell composite PCMs. Using Open Pore Network Modeling (Openpnm), a python source code library for pore network modeling of porous media, we developed a one-dimensional transient heat transfer model to simulate Paraffin@SiO2, a paraffin-based core-shell PCM. The structural parameters were obtained from the Paraffin@SiO2 synthesized in our laboratory using the in-situ emulsion interfacial hydrolysis and polycondensation methods. The effects of various structural parameters of coreshell composite PCMs, including paraffin core diameter, SiO2 shell layer thickness, and SiO2 encapsulation rate, on the PCMs' thermal energy storage properties, i.e., thermal energy storage density and effective heat transfer coefficient, were investigated. Moreover, the effects of material porosity on thermal storage properties were also studied. The modeling results would provide guidance for further optimization of the synthesis process and design of core-shell composite PCMs to meet different requirements in advanced energy systems.
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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) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".