A Redesign Methodology to Improve the Performance of a Thermal Energy Storage with Phase Change Materials: A Numerical Approach
Bibliographic record
Abstract
In recent years, phase change materials (PCMs) have been presented as a suitable alternative for thermal energy storage (TES) systems for solar water heater (SWH) applications. However, PCMs’ low thermal conductivity and the high dependence on external conditions are the main challenges during the design of TES systems with PCMs. Design actions to improve the performance of the TES systems are crucial to achieve the necessary stored/released thermal energy and guarantee the all-day operation of SWHs under specific system requirements. In this study, a TES with PCM in the configuration of a heat exchanger was redesigned, focused on achieving two main targets: an outlet water temperature over 43 °C during discharging time (15 h) and efficiency over 60% to supply the hot water demand of two families (400 L). A four-step redesign methodology was proposed and implemented through numerical simulations to address this aim. It was concluded that the type, encapsulation shape, and amount of PCM slightly impacted the system’s performance; however, selecting a suitable sensible heat storage material had the highest impact on meeting the system’s targets. The redesigned TES reached 15 operating hours with a minimum outlet water temperature of 45.30 °C and efficiency of 76.08%.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".