CFD-Based Assessment of Heat Transfer Fluid Temperature Effects on Multi-Stage Latent Thermal Energy Storage for CSP Energy Management
Bibliographic record
Abstract
This study presents a computational investigation of latent thermal energy storage (LHTES) systems integrated with multi-stage phase change materials (PCMs) for concentrated solar power (CSP) plants. The novelty of this research lies in analyzing the effect of elevating heat transfer fluid (HTF) inlet temperature on the melting dynamics of PCMs, with a focus on charging time and melting rate. A cascaded PCM configuration, employing Octadecanoic Acid, RT58, and Dodecanoic Acid, was modeled to capture sequential melting across distinct temperature ranges, thereby extending system efficiency. Using computational fluid dynamics (CFD) with the SIMPLE algorithm in ANSYS Fluent, simulations were conducted under controlled flow velocity while varying HTF temperatures from 100°C to 180°C. The findings reveal that increasing HTF temperature significantly accelerates PCM melting, reducing total charging time by over 65% at elevated conditions, while maintaining stable temperature layering that minimizes thermal shock. This work contributes critical insights into optimizing TES integration within CSP plants, demonstrating how strategic thermal management can enhance efficiency, reliability, and sustainability of renewable energy generation.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 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 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".