Numerical investigation of large‐scale seasonal rock‐pit energy storage system
Bibliographic record
Abstract
Abstract A twodimensional axisymmetric model, which is computationally inexpensive, has been proposed to predict the property changes that occur in a seasonal rock‐pit energy storage (RPES) system. The geometry of the rock‐pit has been simplified into a shape that can be extended for any seasonal storage system with a three‐dimensional conical geometry. The computational domain has been solved using both linear thermal equilibrium (LTE) and linear thermal non‐equilibrium (LTNE) models, and the former has been found to be computationally quick and accurate. The model has predicted a 95% energy saving at the highest flow conditions required in the mine. Further analysis has suggested that the increase in storage capacity by decreasing the porosity of rocks in the rock‐pit is insignificant compared to the corresponding increase in fan power. Furthermore, the investigation done on natural rocks has indicated an increased ability to store heat in the rock‐pit when rocks with higher thermal mass have been used. The use of RPES has shown a significant potential to reduce the carbon footprint. Finally, an economic analysis done on the system has shown a return on investment of just under 12 years.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".