The Influence of Geometry on the Performance of a Helical Steel Pile as a Geo-Exchange System
Bibliographic record
Abstract
Foundation piles have the potential to improve the economic and technical feasibility of ground source heat pump (GSHP) systems. They simultaneously provide structural support and energy for space heating and cooling. In this study, a thoroughly verified and validated numerical model of a novel in-ground heat exchanger for GSHP systems is developed and used to simulate and optimize performance. Commercially available helical steel casings with nominal sizes according to the American Petroleum Institute (API) of API 13.5, API 23, API 29, API 53, API 60, and API 68 were considered. The flow rates considered were 1 L/min, 2 L/min and 4 L/min for laminar flow and lower pressure drops. Results show the performance to increase with increasing pile size owing to improved heat transfer and longer residence times. Optimizing performance with an API 68 steel casing, (with a 2” nominal plastic pipe) gives a capacity increase of 0.01 ton/pile (or 8.3%), which would reduce the approximate pile array required for a 3 ton cooling system from 13 to 12. Doubling the size of this pile while keeping other parameters constant gives an 18.5% capacity improvement with an output capacity of 0.28 ton/pile, and an 11, 20 m pile array requirement to meet a 3 ton cooling load. With a maximum heat exchange rate of 58.6 W per meter depth, this shallow in-ground heat exchanger has the potential to minimize energy and costs for small-scale implementation. In addition, the possible low flow rates help reduce pumping power requirements. This study provides a foundation for sizing and design of helical steel piles. Moreover, the study also gives insights into pile performance in multi-layered soils where the thermal conductivity varies with depth.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".