A Novel Micro‐Thermophotovoltaic Combustor of Hydrogen–Air to Enable Ultra‐Lean Combustion, High Thermal Output and NO Low Emissions
Bibliographic record
Abstract
This study presents a novel micro‐combustor (MC) design called micro‐trapped vortex combustor (MTVC) for micro‐thermophotovoltaic (MTPV) devices used in small‐scale electricity generation. Traditional MC designs struggle to operate efficiently under ultra‐lean regimes due to flame quenching, limiting their performance. The proposed MTVC incorporates the trapped vortex concept, inspired by aeronautical applications, to improve thermal performance and stability under ultra‐lean conditions. Numerical simulations, using the Navier–Stokes and energy equations for laminar and reactive flow, are conducted to compare the MTVC with conventional micro‐backward‐step combustors (MBSCs) under hydrogen (H 2 )–air mixture combustion. The study focuses on key performance parameters such as temperature distribution, heat recirculation, flame shape, flow topology, radiative power and emissions. The results show that the MTVC can operate at an ultra‐lean equivalence ratio of Φ = 0.5, while the MBSC experiences flame quenching below Φ = 0.7. The MTVC design achieves up to 26.51% higher radiative power and a 36% improvement in energy conversion efficiency compared to traditional combustor designs. Additionally, the MTVC produces 43% less nitrogen oxides (NOx) emissions, demonstrating its potential for both higher efficiency and reduced environmental impact in portable power applications.
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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".