Bronze Wool as a Porous Mixer for Air Temperature Uniformity in Energy Exchangers
Bibliographic record
Abstract
This study investigates the use of bronze wool as a porous air mixer to improve temperature uniformity at the outlet of a liquid-to-air membrane energy exchanger (LAMEE). Traditional air mixers often cause excessive pressure losses and are difficult to install in compact systems. Bronze wool offers a lightweight, space-efficient alternative with favorable thermal and structural properties. Experimental evaluations were conducted using mixers with varying wool mass, porosity, and structure, under both horizontal and vertical duct orientations. Results show that bronze wool mixers significantly reduce temperature gradients, achieving up to 75% statistical effectiveness in horizontal ducts and 54% range effectiveness in vertical ducts. In compact wool structures, thermal conduction within the metal fibers is the primary temperature homogenization mechanism, while physical mixing from advection and dispersion occurs in higher-porosity structures. Among all configurations, the accordion wool structure demonstrated the highest range effectiveness, attributed to enhanced multi-directional conduction. The pressure drop across the mixers remained relatively low, confirming the suitability of metal wool air mixers for use in systems where minimizing flow resistance is critical. The findings demonstrate that bronze wool mixers provide a compact, low-resistance, and effective solution for temperature homogenization in air ducts.
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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 | 0.001 | 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 | 0.001 | 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".