Development of a control strategy to reproduce the start-ups of hydraulicturbines with a closed-loop test stand
Bibliographic record
Abstract
The energy available for extraction by a hydraulic turbine in a power plant depends on the height difference between the upstream and downstream water.This energy, referred henceforth as the gross head, can be considered constant throughout all operating regimes including transient regimes such as start-up.Starting with the runner at rest, the start-up sequence is initiated with a command to open the guide vanes, leading a flow of water through the turbine which, in turn, generates an angular acceleration of the runner.Start-up sequences are designed to minimize both the time required for the runner to reach the generator synchronous speed and the mechanical stresses in the runner.Those sequences are influenced by different parameters such as the gross head, the inertia of the rotating parts as well as the opening sequence of the guide vanes.In order to study start-up sequences on a reduced scale model, these parameters must be scaled and controlled to maintain homology.Within the Tr-FRANCIS project underway at Universit Laval, the fluid-structure interactions during start-up for a model Francis turbine installed in a closed-loop test stand are studied using experimental measurements.Those investigations require homologous start-up sequences with respect with the 130 MW prototype turbine.To reproduce the typical prototype startup sequence, the guide vanes opening time and the inertia of the rotating parts must be adjusted using dimensional analysis.The gross head at the test section must also be kept constant.Since the model turbine is installed in a closed-loop test stand where the head at the test section is obtained through the use of a pump, a specific test stand control strategy must be developed to maintain a constant head while the guide vanes and the discharge are increasing.This strategy includes the design of a flywheel with an adjustable inertia on the shaft line and the modification of the test stand's control scheme.The presentation will discuss about the design process of the flywheel and present the modification of the control scheme.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".