Therporaoustic Convection: Modeling and Analysis of Flow, Thermal, and Energy Fields
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The problem of therporacoustic (thermal-porous-acoustic) convection near a porous medium, representative of a stack in a thermoacoustic engine/refrigerator, is modeled and analyzed in this paper. Assumptions (e.g., long wave, short stack, and small amplitude oscillation) are made to enable simplification of the governing unsteady-compressible-viscous forms of the continuity, momentum, and energy equations to achieve analytical solutions for the fluctuating velocity and temperature and the complex Nusselt number. Boundary walls are assumed to be very thin in thickness and the conduction heat transfer inside the boundary walls are neglected in this paper. The derived analytical results are expressed mainly in terms of the Darcy number (Da), critical temperature gradient ratio (Γ0), Swift number (Sw), Prandtl number (Pr), and modified Rott’s and Swift’s parameters (fν and fk). The real part of the fluctuating flow complex Nusselt number approaches to the steady result, as reported in the literature, at the zero frequency limit. While in the high frequency limit, the real part of the complex Nusselt number matches well with the limit obtained by other oscillating flow researchers with slight differences explained by additional terms included in this work. A wave equation for the pressure fluctuation is modeled by combining the continuity, momentum, and energy equations and subsequent integrations which, in the inviscid no-stack limit, approaches the Helmholtz wave equation. Based on the derived energy flux density equation performance plots are proposed, which give the Swift number at the maximum energy transfer (Sw0) for a given Γ0 and Da.
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.
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) | 0.000 | 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