Faster computation of g-functions used for modeling of ground heat exchangers with reduced memory consumption
Bibliographic record
Abstract
Temperature response functions, known as g-functions, are a computationally efficient method for simulating ground heat exchangers (GHEs), used with ground-source heat pump (GSHP) systems or direct ground cooling systems as part of a whole-building energy simulation. In fact, at present, there are no other methods that have sufficient accuracy and are fast enough to simulate a ground-source heat pump system in a whole-building energy simulation. The concept, mathematical derivation and an implementation of a g-function calculation program were originally developed by Claesson and Eskilson (1985). More recently (Cimmino 2018a, Cimmino 2018b, Cimmino 2019) developed an open-source g-function calculation tool known as pygfunction. This tool offers great flexibility for the user to compute g-functions for specific configurations of boreholes. However, for large borehole configurations (with ~1000 boreholes), the required time to compute a single g-function can take several hours, and the required RAM can be on the order of 100 GB, greatly exceeding most desktop PCs. In order to develop libraries of g-functions and training sets for machine learning approaches, we are computing hundreds of thousands of g-functions. This paper describes further development of Cimmino's methodology to speed the computation and reduce the memory requirements.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".