Optimizing the layout of geothermal energy piles to minimize ground temperature changes
Bibliographic record
Abstract
• A new framework optimizes GEP layouts to reduce ground temperature changes (ΔT g ). • The surface temperature effect on GEP layout design is considered for the first time. • Computation time reduced by over 95% compared with classical methods. Geothermal energy piles (GEPs) are increasingly used in building foundations to provide sustainable heating and cooling. However, their operation can lead to undesirable changes in ground temperature (ΔT g ), which may reduce system efficiency and structural safety. Minimizing ΔT g is therefore critical in cold climates, where temperature drops may induce ground freezing and potentially affect the bearing capacity of foundation piles. To address this challenge and enhance the performance of GEPs, this study proposes a novel framework that optimizes their layout to minimize ΔT g during operation. In the framework, the finite element method (FEM) simulates the thermal response of energy piles, the genetic algorithm (GA) optimizes their layout, and the superposition principle reduces computational cost by decomposing complex simulations into simpler sub-problems. The framework also included the effect of ground surface temperatures and significantly reduced the computational time by over 95 % compared with classical methods. The study found that the framework allocated more GEPs to the areas with higher rather than lower ground surface temperatures to minimize ΔT g in a geothermal field. Moreover, using the optimal layout, the framework achieved a more significant reduction in ΔT g when using fewer GEPs. Notably, this reduction in ΔT g could reach 1.9 °C. Finally, the proposed framework significantly reduced computational time compared with other simulation methods. In conclusion, the proposed framework optimizes GEP layouts efficiently, offering valuable insights for the design of geothermal systems under various ground surface temperature conditions.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".