An improved model for predicting the efficiency of hydraulic propeller turbines
Bibliographic record
Abstract
Field performance testing of hydraulic turbines is undertaken to define the head-power-discharge relationship that identifies the peak operating point of the turbine. This relationship is essential for the efficient operation of a hydraulic turbine. Unfortunately, in some cases it is not feasible to field test turbines because of time, budgetary, or other constraints. Gordon (2001) proposed a method of predicting and (or) simulating the performance curve for several types of turbines. However, a limited data set was available for the development of his model for certain types of turbines. Moreover, his model did not include a precise method of developing performance curves for rerunnered turbines. Manitoba Hydro operates a large network of hydroelectric turbines, which are subject to periodic field performance testing. This provided a large data set with which to refine the model proposed by Gordon (2001). Furthermore, since these data include rerunnered units, this provides an opportunity to refine the effects of rerunnering. Analysis shows that the accuracy of the refined model is within 2% of the performance test results for an "old" turbine, while for a newer turbine or a rerunnered turbine the error is within 1%. For both an old turbine and a rerunnered turbine, this indicates an accuracy improvement of 3% over the original method proposed by Gordon (2001).Key words: hydraulic turbine, efficiency, simulation modeling
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 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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".