Multi-Objective Optimization of Runner Blades Using a Multi-Fidelity Algorithm
Bibliographic record
Abstract
A robust multi-fidelity design optimization methodology has been developed to integrate advantages of high- and low-fidelity analyses and alleviate their weaknesses. The aim of this methodology is to reach more efficient turbine runners with respect to different constraints, in reasonable computational time and cost. In such a framework, an inexpensive low-fidelity (inviscid) solver handles most of the computational burden by providing data for the optimizer to evaluate objective functions and constraint values in the low-fidelity phase. An open-source derivative-free optimizer, NOMAD, explores the search space. Promising candidates are selected among all feasible solutions using a filtering process. The proposed filtering process accounts for Pareto optimal solutions and considers solutions which are different in the design variable space and are dominant in their local territories. A high-fidelity (viscous) solver is used outside the optimization loop to accurately evaluate filtered solutions. Accurate information achieved by high-fidelity analyses is also employed to recalibrate the low-fidelity optimization. The developed methodology demonstrated its ability to redesign a Francis turbine blade for a given best efficiency operating condition. The original and optimized cases were evaluated and compared for a complete range of operating conditions by calculating the efficiency curves and losses of different components. The optimal blade has provided an efficient runner for the given operating conditions considering the design constraints.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".