Challenges in Dynamic Pressure and Stress Predictions at No-Load Operation in Hydraulic Turbines
Bibliographic record
Abstract
Some of the potentially most damaging continuous operating conditions for hydraulic turbines are the no-load (NL) conditions. At NL conditions the flow passes through the turbine without power generation, but with non-negligible flow rate, and therefore all the potential energy in the flow has to be dissipated. This takes place through a mechanism where the runner channels are partially pumping, thus generating large scale unsteady vortex structures which, by their nature, break down into smaller and smaller vortices until energy dissipation occurs at the smallest scales. This type of flow, dominated by its turbulent character, is inherently difficult to simulate by means of numerical methods since turbulence model and numerical dissipation have a major influence. The resulting dynamic loads on the runner are largely of stochastic nature, exciting a broad band of frequencies and thus, almost always interact with at least one deformation mode. The presented investigations are aimed at predicting the effect of the unsteady NL pressure loads on the fatigue life of a Francis turbine runner. A combination of computational fluid dynamics (CFD) and finite element analysis (FEA) methods has been employed. The results from transient CFD simulations are presented. Comparison of the results with prototype strain gauge measurements at no load conditions shows that the stochastic nature and the approximate range of the dynamic stresses can be predicted.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".