A comprehensive performance evaluation of phase change materials for cold energy storage systems
Bibliographic record
Abstract
The increasing need for cooling, particularly air conditioning, is driving a significant rise in building energy consumption. This surge in demand often leads to peak loads, straining power grids and increasing costs. Cold thermal energy storage systems, especially those utilizing phase change materials, offer a promising solution to mitigate these challenges. This study presents a comprehensive investigation and performance assessment of various phase change materials for efficient cold energy storage applications. Phase change materials are considered encapsulated, one of the most common techniques in cold thermal energy storage applications. The primary objective is to develop a comprehensive methodology for the system's design and then identify the most suitable phase change material for better system performance. The research also studies the impact of storage tank porosity, diameter-to-height ratio, and pressure drop on system performance for a 1 MWh cooling capacity. The performance assessment considers all essential power drivers of the practical systems. The findings reveal that water/ice is the most efficient phase change material, requiring the smallest mass and exhibiting the highest overall system COP values by lowering the heat transfer fluid pump and chiller powers. Specifically, it has been determined that water/ice is the most efficient phase change material, requiring the smallest mass quantity of 10.4 tons and demonstrating overall coefficient of performance values ranging from approximately 3.98 to 4.37 for different heat transfer fluid options. This research contributes to the development of sustainable and cost-effective cooling solutions, addressing the growing energy demands of the building sector and promoting a more resilient and efficient energy infrastructure.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".