A comprehensive performance evaluation of phase change materials for cold energy storage systems
Bibliographic record
Abstract
• A thorough study evaluates 12 PCMs for cold energy storage. • A modelling methodology is developed for tank design, porosity, and pressure drop analysis. • Different heat transfer fluids are assessed based on thermodynamics. • Water/ice is identified as the most efficient, versatile option. 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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".