Review of Selected Heat Transfer Topics for Solar Thermal Energy Utilization and Storage
Bibliographic record
Abstract
Abstract This review article, developed by the K6 Committee—Heat Transfer in Energy Systems, a part of the Heat Transfer Division (HTD) of the American Society of Mechanical Engineers (ASME), summarizes advancements in heat transfer technologies for solar thermal energy utilization and storage, focusing on concentrated solar power (CSP), solar-driven cooling, sensible and latent thermal energy storage (TES), and novel heat exchanger designs. Key topics include heat transfer enhancement strategies such as additive manufacturing, phase change materials (PCMs), and triply periodic minimal surface (TPMS) structures for improving efficiency. The advances in solar-driven cooling and multigeneration systems are analyzed, emphasizing thermodynamic optimization through exergy and entropy generation minimization. Additionally, the study examines emerging methodologies, including constructal theory and second-law analysis, to enhance the performance of solar thermal applications. The article highlights overlaps in TES strategies, heat exchanger innovations, and system optimization approaches, offering a comprehensive perspective on sustainable energy solutions. Future research directions include scaling advanced TES materials, optimizing hybrid cooling technologies, and improving structural integrity in high-temperature heat exchangers.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".