A comparison of extremum seeking algorithms applied to vapor compression system optimization
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Bibliographic record
Abstract
In recent years, a number of extremum seeking algorithms have been proposed. While each approach aims to estimate the gradient of a performance metric in realtime and steer inputs to values that optimize the metric, the way in which each method accomplishes this goal can have practical implications that depend on the application. In this paper, we compare the performance of traditional perturbation-based extremum seeking to time-varying extremum seeking in the context of optimizing the energy efficiency of a vapor compression system. In order to benchmark these algorithms, we simulate their performance using a moving-boundary model of a vapor compression machine that has been tuned and calibrated to data gathered from a multi-split style room air conditioner operating in cooling mode. We show that while perturbation-based extremum seeking appears simplest to tune, some challenging minima are not obtained. Also, we find that time-varying extremum seeking converges faster and more reliably than the other method tested.
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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) | 0.001 | 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