Effective Thermal Conductivity Model for Tetragonal Pin Array Stack
Bibliographic record
Abstract
Thermoacoustics is the science which describes the energy conversion between mechanical energy, in the form of acoustic waves, and heat. This energy conversion takes place in a particular porous material, named "stack" or "regenerator", which represents the core of these devices. In literature, convex uniform cross-section geometries, such as longitudinal pin array, are found to be the most efficient stack for thermoacoustic applications, thanks to the low viscous losses. On the other hand, another fundamental characteristic required for thermoacoustic porous cores is the ability to sustain a static thermal gradient along their axial length. For this reason, stack configurations with low thermal conductivity, such as transversal pin, have been also considered. Therefore, a tetragonal pin array geometry comes out as thermoacoustic stack from a trade-off between the classical longitudinal pin array (low viscous losses and high thermal conductivity) and the transversal pin array (high viscous losses and low thermal conductivity). In this work, an analytical model to assess the effective thermal conductivity of the tetragonal pin array stack is provided in order to correctly estimate the heat flux along this typology of stack. Such an analytical model has been then verified both by FEM-based numerical simulations and other correlations found in the literature.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".