An Experimental Study on Heterogeneous Porous Stacks in a Thermoacoustic Heat Pump
Bibliographic record
Abstract
Growing evidence suggests that research must be done to develop energy efficient systems and clean energy conversion technologies to combat the limited sources of fossil fuel, its high price, and its adverse effects on environment. Thermoacoustic is a clean energy conversion technology that uses the conversion of sound to thermal energy and vice versa for the design of heat engines and refrigerators. However, the efficient conversion of sound to thermal energy demands research on altering fluid, operational, and geometric parameters. The present study is a contribution to improve the efficiency of thermoacoustic devices by introducing a novel stack design. This novel stack consists of alternative conducting and insulating materials or heterogeneous materials. The author examined the performance of eight different types of heterogeneous stacks (combination 1–8) that are only a fraction of the displacement amplitude long and consisted of alternating aluminum (AL) and Corning Celcor or reticulated vitreous carbon (RVC) foam materials. From the thermal field measurements, the author found that combination eight performs better (12% more temperature difference at the stack ends) than all the other combinations. One interesting feature obtained from these experiments is that combination 7 produces the minimum temperature at the cold end (17% less than other combinations). The thermal performance of the heterogeneous stack is compared to that of the traditional homogeneous stack. Based on the study, the newly proposed stack design provides better cooling performance than a traditionally designed stack.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".