Numerical Analysis of Solar Energy Storage within a Lobed Triplex Tube Heat Exchanger Utilizing Porous Metal Foam and Y-Shaped Fins
Bibliographic record
Abstract
Latent heat storage units (LHSU) greatly minimize the issue of solar energy intermittent and discontinuous supply since they allow to storage of large amounts of thermal energy without any temperature gradients. In this category, the most popular solar thermal application of a latent thermal energy storage system (TES), often equipped with Phase Change Material (PCM), is the triplex tube heat exchanger (TTHX). Also, increasing thermal conductivity is a crucial subject to improving storage capacity due to the poor thermal conductivity of PCMs. To improve augmenting the conductivity of paraffin (RT54HC), optimization of the heat exchanger geometry (lobed heat exchanger), using extended surface (Y-shaped fin), and porous metal foams (aluminium foam-20 PPIs and 0.88 porosity) have been used as thermal conductivity enhancers of PCM-based energy storage devices. Heat transfer fluid (H2O) across inner and outer tubes is considered too as the working fluid. The main goal of this investigation is to develop a highly efficient TES system in domestic and industrial hot water heating applications with a TTHX equipped with the aforementioned thermal conductivity enhancement techniques. To model the PCM melting, an enthalpy-porosity approach is employed, where governing equations are numerically solved with a finite volume approach. When the porous foam is included, a two-energy equation local thermal non-equilibrium model is employed. The findings are summarized in terms of the liquid fraction, temperature evolution, and rate of energy storage charge. The outcomes illustrate that Case B and Case C-pure PCM compared to Case A decreases the charging time by 14.91% and 30.54%, respectively. Also, the addition of metal foam in the lobed pipe (case B) reduces melting time by 73.61% and increases the rate of energy storage when compared to Case A-pure PCM.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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".