Computational Study of Melting and Solidification in a Prototype Four-pass Thermal Storage Module
Bibliographic record
Abstract
A computational study is presented that utilizes the enthalpy-porosity model in ANSYS-Fluent to predict melting and solidification for the base case of a simple four-pass thermal storage module containing a paraffin phase change material.The prototype module is a rectangular prism of square cross-section oriented vertically with four stainless steel tubes passing through it for passage of a heat transfer fluid (HTF).The computational model was calibrated in a previous study and was utilized with the properties provided for the PCM.Comparisons between the computed results and experimental results reported previously for the same module show that the enthalpy-porosity model predicts the flow, the interface structure and the melting time reasonably well.The same model predicts the evolution of the interface structure during solidification reasonably well but over-predicts the solidification time by more than double.This implies that calibration must be done for both processes to capture the impacts of the different interface structure and the impact that temperature gradient and interface evolution have on processes and thermophysical properties.Comparisons of the outlet temperatures during melting were also reasonably well predicted in terms of trend and value.In its current form, the model is useful for the development of heat transfer elements that consider enhancements to shorten charging (melting) times of the module.The results of this study are a baseline from which enhancements in terms of HTF flowrate and heat transfer elements can be compared.
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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 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".