Sustainable Fluid Selection Strategies for Multi-Stage Latent Thermal Energy Storage: A CFD Study of Air and Water in CSP Plants
Bibliographic record
Abstract
This study presents a computational investigation of a multi-stage latent heat thermal energy storage (LHTES) system integrated with organic phase change materials (PCMs) for concentrated solar power (CSP) applications, with a specific focus on the influence of heat transfer fluid (HTF) selection on melting dynamics and thermal stability. The cascaded PCM configuration, comprising Octadecanoic, RT58, and Dodecanoic, was encapsulated in aluminum containers and analyzed using ANSYS Fluent with the solidification/melting model under an inlet temperature of 100 °C. Two fluids, water and air, were compared under identical operating conditions to assess their impact on charging efficiency and system performance. The results reveal that water, owing to its higher thermal conductivity and specific heat capacity, enabled rapid melting, achieving full PCM liquefaction within 58 seconds, significantly reducing charging time and enhancing responsiveness for CSP integration. Conversely, air demonstrated a slower melting process, requiring 179 seconds, but exhibited smoother and more uniform temperature distributions, minimizing localized thermal stresses and promoting long-term reliability. The findings highlight a fundamental trade-off between rapid energy capture and thermal stability, emphasizing that strategic HTF selection is critical to optimizing performance, efficiency, and durability of LHTES systems for sustainable solar energy applications.
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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 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".