Meta study on the optimisation of thermoacoustic cooling systems for efficiency and cooling load
Bibliographic record
Abstract
The emerging field of thermoacoustic cooling systems (TACS) has been explored in recent years, combining the disciplines of acoustics and thermodynamics to provide an alternative to mainstream cooling technologies. This hybridised system allows a system of travelling or standing waves to absorb and release thermal energy at different points spatially, which is then harnessed to produce a cooling effect. This meta-analysis will focus on analysing parameters such as stack plate spacing and selection of a working fluid, in order to optimise the system. As this will directly impact the temperature gradient, as the temperature gradient is the core operator in the cooling process. The above parameters were examined with a combination of comparative and normalisation techniques, to synthesise data from varied experimental sources and produce accurate conclusions. The parameters investigated had differing effects on the system with regards to COPR and maximum cooling power, due to cooling power and input acoustic power increasing at different rates. The meta study concluded that a ratio of parallel-plate stack spacing to thermal penetration depth of (equation) was ideal for maximising cooling load, where as a ratio of approximately (equation) was ideal for achieving maximum COPR.
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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 | 0.012 | 0.033 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.016 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".