Computational Modeling of Thermal Energy Storage in Rock Beds
Bibliographic record
Abstract
Thermal energy storage (TES) systems are commonly employed for enhancing the efficiency of commercial and residential heating and cooling systems, by matching thermal energy supply and demand during summer-winter, day-night, and peak-off-peak periods. TES in these systems is usually achieved by changing the temperature of materials (sensible systems) and/or inducing solid-liquid phase change (latent heat systems). Such systems are also categorized as seasonal (long-term) and diurnal (short-term). In this work, the focus is on sensible diurnal TES systems consisting of rock beds, with air as the working fluid. They are relatively simple, easy to construct, inexpensive, and quite effective for many solar energy and building engineering applications. Numerous publications on rock-bed TES systems are available, but there is an urgent need for efficient computational methods for designing and optimizing them. The contributions of this work are the following: proposal of cost-effective mathematical models of fluid flow and heat transfer in rock beds; adaptation of a finite volume method (FVM) for the solution of this model; applications of this FVM to two test problems (with analytical solutions) and one demonstration problem; proposal of suitable thermofluid performance evaluation criteria for the rock-bed TES systems of interest; and presentation and discussions of the results.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".