Actively controlled transverse gas injection
Bibliographic record
Abstract
In general the problem of feedback control for an unsteady fluid flow is nonlinear. Especially challenging is the control of mixing processes in a hot, potentially reactive environment. Because of the high temperatures present, realistic sensors can only operate mainly in cool regions, e.g., away from flames and hot exhaust regions. Consequently, there is often a large time lag between the time at which an actuator would modify, for example, a fuel or dilution air jet's characteristics in a combustion chamber, and the time at which a sensor would measure the effect of this action on the jet's mixing and/or reaction processes. During this time lag, flow dynamics, mixing, and combustion chemistry, if present, are dominated by nonlinear effects. A goal of the present study is to develop control strategies to optimize the mixing characteristics associated with the actively driven jet in crossflow. As a consequence of the differences between signal generator input function and jet exit velocity temporal variation (which is the actuation for the flowfield), it becomes necessary to design for the jet actuator a feedback controller which is distinct from the plant controller used for the overall experiment. Moreover, in developing a controller for the transverse jet problem there is an interesting trade-off between the complexity of actuating and sensing.
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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 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".