Determination of applicable input range for approximating a nonlinear FGR furnace around the design point
Bibliographic record
Abstract
In this paper, the nonlinear dynamic characteristics of a FGR furnace have been analysed around the furnace design point. Based on the steady-state results of full-scale nonlinear CFD simulations, the maximal allowable range on the variations of the furnace inputs can be determined, once for the maximal error bound between nonlinear system and its linear counterpart is specified. It is interesting to note that for a reheating furnace, the nonlinearities associated with the heat load are less severe than that associated with NO emission. With due consideration of the established input signal linear ranges, the linearized dynamic models of the furnace are derived by applying system identification technologies using the data generated from the CFD simulations. Analysis and validation of the models are also carried out. It is concluded that this technique is applicable to weak nonlinear systems around the design point. The results of the analysis provide additional insights on the nature of the nonlinearities as well as guidelines for selecting the input amplitude if system identification techniques are used. So long as the amplitudes of the probing signals satisfy the respective input constraints, the obtained linearized models will be applicable around the design point. Subsequently, these models can be used to design feedback controllers to maintain the furnace operated around the design point.
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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 | 0.001 | 0.002 |
| 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 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".