Impact of Heat Transfer Fluid Velocity on Sustainable Multi-Stage Latent Thermal Energy Storage Performance in CSP Systems
Bibliographic record
Abstract
This study presents a computational investigation of a multi-stage latent heat thermal energy storage (LHTES) system integrated with cascaded organic phase change materials (PCMs) for application in concentrated solar power (CSP) plants. The research focuses on quantifying the influence of heat transfer fluid (HTF) inlet velocity on melting dynamics, charging efficiency, and overall thermal response of the storage unit. A three-stage PCM configuration—employing Octadecanoic Acid, RT58, and Dodecanoic Acid—was modeled to exploit sequential melting across distinct temperature ranges, enhancing system stability and thermal utilization. Using computational fluid dynamics (CFD) in ANSYS Fluent with the SIMPLE algorithm, simulations were carried out under fixed HTF temperature (100°C) while systematically varying inlet velocity from 35 m/s to 70 m/s. The results demonstrate that increased HTF velocity accelerates convective heat transfer and reduces melting duration, though improvements remain incremental compared to temperature-driven effects. Additionally, higher velocities introduce greater heat loss to the aluminum shell, limiting net efficiency gains. This work provides key insights into velocity optimization as a secondary performance parameter in LHTES design, complementing temperature control strategies to maximize charging efficiency, minimize thermal losses, and enhance CSP plant reliability and sustainability.
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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) | 0.000 | 0.000 |
| 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) | 0.001 | 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".