Bibliographic record
Abstract
Thermoacoustic is the science that studies the conversion of the heat into acoustical sound and vice versa. The conversion of heat to acoustic power is done through Thermoacoustic engine (TAE). This generated acoustic power can be converted to another and useful form such as mechanical or electrical energy. On the other hand, Thermoacoustic refrigerator (TAR) or heat pump is a device that uses acoustic sound to pump heat from a lower temperature reservoir. The most distinct feature of thermoacoustic systems is that they do not have moving parts, which makes them reliable. Thermoacoustic engine can recycle any source of waste heat and use sustainable heat like concentrated solar. Also, in contrast to conventional refrigeration methods, thermoacoustic refrigerator spares the usage of environmentally harmful gases that is daunting the centralized cooling industry. There is no doubt the thermoacoustic technology has been considered in various applications with some unspoken advancement. In this manuscript we intend to review the fundamentals of thermoacoustics and highlight their recent developments. Additionally, analytical simulation of thermoacoustic refrigerator will be discussed and validated against experimental published work. The goal is to reveal the effect of different parameters on the performance in an attempt to establish design guidelines for an improved technology metrics. The future prospects of thermoacoustic refrigeration are also presented at the end of this study.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 0.004 |
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".