Fluid Flow Regulation for Pipeline System with Additive Gaussian Noises
Bibliographic record
Abstract
This work addresses fluid flow regulation for a pipeline system subjected to additive Gaussian noises in plant and measurement. A discrete output regulator is proposed for the pipeline system modelled by two coupled hyperbolic partial differential equations (PDE) with boundary input, disturbance, and output of interest. In particular, the continuous PDE model is discretized in time via the Cayley-Tustin transformation without any spatial approximation or model reduction. Based on the internal model principle, discrete regulation equations are formulated and employed for output regulator design by using the discrete-time plant model and exogenous system (exo-system). Considering the prohibitive cost of measuring the spatially distributed states of the stochastic pipeline model and the unavailability of state information of the exo-system, Kalman filter and Luenberger observer are designed respectively for the output regulator design. A simulation study on a gasoline pipeline model demonstrates the applicability of the proposed method.
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How this classification was reachedexpand
Full frame distilled prediction
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 | 0.000 |
| 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) | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".