CH<sub>4</sub>/air combustion in a microscale recirculating heat exchanger: Sizing design using the heat transfer approach
Bibliographic record
Abstract
Abstract Microcombustors are microscale combustion devices that can be used to power microelectromechanical systems. Many combustor configurations are reported in the literature and, among them, combustion in a microscale recirculating heat exchanger is a feasible option. In this work, a simple, double‐channel, recirculating heat exchanger is considered. The novelty of the present work lies in the heat transfer analysis approach to design a microcombustor. A combustor is designed using thermal resistance networks for a premixed fuel containing a methane–air mixture in stoichiometric ratio. The length of the combustor is designed based on the position of the combustion flame. Computational fluid dynamics is utilized to validate the theoretical results. The analysis is carried out for adiabatic and nonadiabatic conditions. The combustor lengths for adiabatic and nonadiabatic (ceramic) combustors vary from 39 to 242 mm and 49 to 276 mm, respectively, for variations in the mass flow rate of the premixed gases from 6 to 10 mg/s. A minimum limiting flow rate of 6 mg/s was identified. The average error in the maximum combustion gas temperatures between the theoretical and CFD results obtained in this work is 4.2%. The theoretical approach presented can be suitably applied to more complex geometries involving multichannels and variations in geometrical properties.
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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 | 0.000 |
| 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) | 0.001 | 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".