Heat transfer performance of different lattice structures in porous medium combustion
Bibliographic record
Abstract
Porous medium combustion technology has notable characteristics of high efficiency and low emissions, which makes it widely applicable in industrial furnaces and heating systems. This study considers the influence of lattice structure on solid conduction and convective heat transfer. This study used a pore-scale model to perform numerical simulations of the Kelvin, cubic (Cube), and body-centered cubic (BC - Cube) lattice structures. The porosity and flow velocity are taken as the variables for investigation, while the effective thermal conductivity, pressure drop, convective heat transfer coefficient, area goodness factor, and convective heat transfer rate are used as evaluation criteria for analysis. The results indicate that within the 80% to 95% porosity range, the effective thermal conductivity of the lattice structure decreases with increasing porosity. At a porosity of 80%, the effective thermal conductivity of the Kelvin lattice structure differs by 7.6% from that of the body-centered cubic lattice structure, demonstrating that the pillar area and heat transfer path of the lattice structure significantly influence its effective thermal conductivity. Furthermore, across the fluid velocity range of 0.5 to 10 m/s, the Kelvin lattice structure exhibits the highest pressure drop, convective heat transfer coefficient, and convective heat transfer rate, while the body-centered cubic lattice structure shows the best overall convective heat transfer capability.
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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 | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 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 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".