An efficient optimization design method for centrifugal compressor blades
Bibliographic record
Abstract
To improve the time-consuming problem of centrifugal impeller optimization design, an efficient optimization design method is proposed. A bionic evolutionary algorithm based on a multi-surrogate model is established, model management strategies and geometric properties of the sigmoid function are investigated, and the penalty mechanism of infeasible solutions is revealed, which finally increases the speed of iterations and improves the optimum solution of constraint optimization problem for centrifugal compressor blades. Combining multi-surrogate model evolutionary algorithm, surface parameterization method, and CFD for aerodynamic optimization of blade shape, the optimization results shows that the isentropic efficiency increased by 1.9%, the mass flow rate increased by 4.61%, the total pressure ratio increased by 0.81%, and the computational time was reduced by 54.9%. The sigmoid-based multi-surrogate model evolution algorithm improves the isentropic efficiency by 1.19% and the total pressure ratio by 0.63% compared with the unconstrained multi-surrogate model evolution algorithm, while the sigmoid-based multi-surrogate model evolution algorithm improves the isentropic efficiency by 1.16% and the total pressure ratio by 0.55% compared with the feasible point dominance constrained multi-surrogate model evolution algorithm, verifying the efficiency of the sigmoid-based multi-surrogate model evolution algorithm.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 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".