Three-dimensional numerical modeling of vertical ground heat exchangers: Domain decomposition and state model reduction
Bibliographic record
Abstract
Modeling of vertical ground heat exchangers is relatively complex because of the three-dimensional transient nature of the problem inside the borehole and in the surrounding ground. Furthermore, the system is characterized by various time scales with rapid changes inside the borehole and slow variations of ground temperature far away from the borehole. Most existing numerical models require important computational resources to adequately represent the short time-scale heat transfer occurring in the immediate vicinity of the borehole, which warrant their use for annual energy simulations. In this article, a three-dimensional reduced model (3D-RM), based on domain decomposition and state model reduction techniques, is proposed to reduce computation time and computer memory. Domain decomposition is used to sub-structure the domain and to vary the time-step values in each sub-domain, and state model reduction is applied to each resulting sub-zone. A comparison with a complete three-dimensional dynamic model indicates that the proposed 3D-RM model reduces computational time by a factor of about 30 without loss of accuracy. A comparison with experimental results shows that the relatively fast transients occurring in the borehole are well predicted by the 3D-RM model not only for the outlet fluid temperature but also for the tube wall temperatures at different depths.
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) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".