Faster computation of g-functions used for modeling of ground heat exchangers with reduced memory consumption
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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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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