STRUCTURAL ANALYSIS OF HIGH-INDEX DAE FOR PROCESS SIMULATION
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Bibliographic record
Abstract
This paper deals with the structural analysis problem of dynamic lumped process high-index differential algebraic equations (DAE) models. The existing graph theoretical method depends on the change in the relative position of underspecified and overspecified subgraphs and has an effect to the value of the differential index for complex models. In this paper, we consider two methods for index reduction of such models by differentiation: Pryce's method and the symbolic differential elimination algorithm rifsimp. They can remedy the above drawbacks. Discussion and comparison of these methods are given via a class of fundamental process simulation examples. In particular, the efficiency of Pryce's method is illustrated as a function of the number of tanks in process design. Moreover, a range of nontrivial problems are demonstrated by the symbolic differential elimination algorithm and fast prolongation.
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Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it