Improved Rock-based Thermal Energy Storage (RTES) with Perforated Plate Based on Double Thermocline Design
Bibliographic record
Abstract
Advancing the transition to renewable energy necessitates significant investments, especially in energy storage solutions to mitigate the variability and intermittency of renewable electricity generation.Rockbased Thermal Energy Storage (RTES) offers the vital adaptability needed to incorporate significant amounts of renewable energy by harnessing excessive energy and storing it in rocks or geological formations.This stored energy can then be used for various applications like heating or generating electricity, providing a reliable and sustainable energy source.One of the main challenges in the widespread use of RTES is the thermal losses (mainly because of the diffusion of thermocline) and the high pressure drop in the packed beds.Hence, a novel RTES system is proposed to overcome these limitations.The design has a perforated plate that delivers air in a certain length from the inlet.The fluid flow and heat transfer inside the packed bed are studied using a 2D Computational Fluid Dynamics (CFD) model considering the local thermal non-equilibrium (LTNE) at the air-rock interface.For the optimal case (perforated length to bed length of 0.7), the proposed design is shown to increase the charging efficiency of the conventional RTES by 14 percent by decreasing the fan power requirement and increasing the stored thermal energy.This improvement is due to the emergence of a secondary thermocline that bypasses the main thermocline, and hence, utilizes the packed bed more efficiently while also reducing the fan power requirement.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".