Performance Optimization of Double U‐Tube Borehole Heat Exchanger for Thermal Energy Storage
Bibliographic record
Abstract
ABSTRACT This paper presents an optimization study of the thermal performance of a double U‐tube borehole heat exchanger (BHE) with two independent circuits that can be used in borehole thermal energy storage. The study applies the Taguchi method and utility concept to obtain the optimum parameters for two objective functions: maximum heat transfer rate and thermal effectiveness of the BHE. A validated numerical heat transfer model with a fully implicit method is applied to compute the transient heat transfer in the BHE. The Taguchi optimization results revealed that the optimal factors (denoted with letters and numbers showing their levels) for achieving the maximum heat transfer rate and thermal effectiveness are A1B3C2D1E3F3G3H3 and A3B3C2D3E3F3G1H1, respectively. This resulted in an optimal heat transfer rate of 120 W/m and a thermal effectiveness of 69.3%. Using the utility concept method, a single set of optimal parameters (denoted by their levels as A3B3C3D2E3F3G2H3) is obtained to maximize the performance of the BHE. These parameters yielded an optimum heat transfer rate of 87.3 W/m and thermal effectiveness of 54.6%. Finally, analysis of variance (ANOVA) showed that ground thermal conductivity, the inlet temperature of the working fluid, and borehole depth are the most influential parameters affecting the performance of the BHE. The study provides crucial information for performance improvement, enhanced energy savings, reduced environmental impact, and optimization of a hybrid ground source heat pump system that can be integrated with borehole thermal energy storage.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".