Inference of draft tube flow parameters in one-dimensional hydropower unit models using Kalman filters
Bibliographic record
Abstract
Abstract In this paper, we propose a Bayesian approach to infer the parameters of 1D hydro-acoustic models of hydroturbine draft tubes. Our method combines sparse and noisy pressure data from monitoring systems with non-linear Kalman filters (KFs), effectively merging data and physics to estimate the underlying dynamics of the hydraulic system. We demonstrate the proof of concept using synthetic data from a SIMSEN model of a 140 MW Francis turbine unit, which includes a lumped model of the draft tube flow under part-load conditions. SIMSEN simulations with predefined model parameters are performed at specific stationary operating conditions, in resonance and non-resonance conditions. The resulting pressure signals serve as groundtruth for the inference step. During inference, the draft tube model parameters are considered unknown and then estimated using a Kalman filter. Our results show that this approach successfully retrieves the targeted parameters with low uncertainty, paving the way for real-time calibration of hydropower units physics-based digital twins using monitoring data.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.003 |
| Scholarly communication | 0.000 | 0.002 |
| 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".