Hot-wire and PIV characterisation of a novel small-scale turbulent channel flow facility developed to study premixed expanding flames
Bibliographic record
Abstract
We demonstrate a small-scale channel flow facility with passive and active turbulence generators for the study of spherically expanding turbulent flames. Traditionally, this canonical flame type is investigated in stagnant cylindrical combustion chambers with imposed turbulence. Incorporating a convective flow allows for a wider variety of flowfields and turbulence conditions to be studied. We compare the turbulence properties of our novel facility with cylindrical chambers and large-scale wind tunnels, discussing the design and validation strategy along with quantifiable turbulence properties. Measurements are made utilising hot-wire anemometry (HWA) and particle image velocimetry (PIV). The turbulent properties are analysed for a range of Reynolds numbers ( 130–480, computed from streamwise RMS velocities and transverse Taylor microscales). Comparing PIV results with HWA, emphasise is put on the achievement of sensible estimates for small-scale quantities relevant to mixing. The facility is demonstrated to have the requisite capability to study expanding premixed flame kernels under the influence of intense turbulence in a windtunnel-like configuration with no precedence in the literature. Based on several criteria, it is shown that homogeneous isotropic turbulence can be achieved in much smaller dimensions than expected from classic windtunnel studies.
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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".