Multi-objective optimization and long-time simulation of a multi-borehole ground heat exchanger system
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract Multi-objective optimization and CFD simulation are conducted to optimize the design of a multi-borehole ground heat exchanger (GHE) system and assess its long-time performance. The multi-objective optimization is performed to minimize the entropy generation number (EGN) and total cost rate by using various evolutionary algorithms, including NSGA-II, GDE-3, MOEA/D, PESA-II, SPEA-II, and SMPSO. NSGA-II and GDE-3 algorithms perform best in obtaining Pareto optimal solutions. Three prominent points on the NSGA-II Pareto frontier, representing the results of single-objective thermodynamic, single-objective economic, and multi-objective optimizations, are simulated in three dimensions over three months. The trends of EGN variations extracted from the transient CFD simulation agree well with those from the steady analytical model. The EGN obtained from multi-objective optimization is 58.8% lower than the EGN obtained using single-objective economic optimization and 1.9 times higher than that calculated from single-objective thermodynamic optimization. Likewise, the total cost rate obtained from multi-objective optimization is 64.4% lower than the value obtained from single-objective thermodynamic optimization and four times higher than that calculated using single-objective economic optimization. The proposed optimization approach can be reliably applied to improve the design of multi-borehole GHE systems.
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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 it