Optimal operating strategies for borehole thermal energy storage
Bibliographic record
Abstract
Borehole thermal energy storage (BTES) provides an approach to decarbonizing space and water heating through seasonal storage of waste heat. Different operating strategies, resulting in different thermal load distributions among boreholes, can be employed for the BTES. This work determines the optimal operating strategy, defined by the flow arrangement and distribution scheme, to employ for maximal storage performance, depending on the size of the available waste heat. The assessment is carried out using a newly developed, efficient 2-D finite volume heat transfer model that enables mathematical optimization of flow distribution in zoned BTES. It is shown that when the available waste heat is less than 70 % of the BTES design load, a serial flow arrangement with radial distribution yields the best storage performance, with up to 6.8 % higher utilization efficiency than the best-performing parallel arrangement strategy. For higher load scenarios, equal flow distribution in a parallel arrangement is shown to provide optimal storage performance, up to 2.9 % higher than operation under a serial arrangement. The results also highlight that maximizing heat transfer within boreholes is more critical to effective BTES performance than minimizing conductive heat loss from the system. • Charging strategies defined by flow pattern and distribution in BTES. • Efficient numerical transient heat transfer model for BTES optimization. • Optimal strategy depends on thermal load size. • Enhancing injection rates more critical than minimizing conductive heat losses.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".