Progress on rock thermal energy storage (RTES): A state of the art review
Bibliographic record
Abstract
Abstract Thermal energy is one of the most widely encountered energy forms in our daily life. To ensure efficient utilization and conversion of this energy, the balance between supply and demand needs to be maintained. For this purpose, thermal energy storage is required. There are various thermal energy storage systems available; one of the most basic is sensible thermal energy storage which includes rock thermal energy storage (RTES). This rock‐based energy storage has recently gained significant attention due to its capability to hold large amounts of thermal energy, relatively simple storage mechanism and low cost of storage medium. Accordingly, numerous studies have been conducted to elucidate the basic flow and heat transfer mechanism and to evaluate the performance of this energy storage. The major technical challenges hindering the wide adoption of this technology are the enormous pressure drop across the storage and nonoptimal heat transfer from the heat transfer fluid to the storage medium and vice versa. These issues will directly and indirectly affect the overall cost (capital, operational, and maintenance costs) of the system. To eliminate this issue and assist further development of this technology, it is crucial to compile and extract important findings from these previous studies, identify the challenge and research gap, and draw guidelines for the upcoming research and development. At the moment, this kind of compilation does not exist. Hence, this paper is prepared with the primary objective to comprehensively review the current technology and development of RTES and to propose a potential way forward based on the pain point identified. Discussion on the nontechnical aspect such as policy and regulations as well as community awareness will also be outlined and discussed.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| 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".