Comparative CFD Study of HTF Connection Configurations for Optimized Energy Management in Latent Thermal Storage of CSP Systems
Bibliographic record
Abstract
This study presents a computational investigation of multi-stage latent heat thermal energy storage (LHTES) systems integrated with organic phase change materials (PCMs) for concentrated solar power (CSP) applications. The novelty of this work lies in evaluating the influence of heat transfer fluid (HTF) connection strategies on melting dynamics and system efficiency, with a particular emphasis on the trade-off between localized melting enhancement and overall thermal stability. The cascaded PCM configuration, comprising Octadecanoic, RT58, and Dodecanoic, was modeled within two geometrical layouts: Model 1 with extended HTF connections and Model 2 with direct PCM coupling. Using computational fluid dynamics (CFD) in ANSYS Fluent with the solidification/melting model, simulations were performed under controlled inlet temperature (100 °C).The results reveal that eliminating HTF connections accelerates localized melting rates by reducing flow resistance and promoting direct PCM–fluid interaction. However, extended HTF pathways improve staged melting and layering effects, mitigating thermal shock and ensuring uniform temperature distribution. Model 1 achieved more controlled thermal progression, while Model 2 favored faster early-stage melting but required longer to reach full PCM liquefaction. These findings underscore the critical role of HTF connection design in balancing charging time, heat transfer uniformity, and operational reliability. The study provides valuable insights into optimizing LHTES integration for CSP plants, demonstrating that careful geometric tuning of HTF pathways enhances both energy efficiency and long-term system stability.
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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 | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".