An Adjoint-Based Multi-Point Optimization Method for Robust Turbomachinery Design
Bibliographic record
Abstract
This paper introduces a multi-point design capability to discrete adjoint-based aerodynamic shape optimization for multi-stage turbomachines. The developed optimization framework allows to improve a compressor or turbine design not only for a certain operating point, but enables the inclusion of additional off-design operation points, therefore guaranteeing a robust design and annihilating the risk of improving the configuration for a specific design point while deteriorating the overall operability of the turbomachine. To keep the computational cost to a minimum, at every design cycle the flow and adjoint solutions are first calculated and stored for each operating point. This approach ensures that the subsequent finite-difference approximation of the residual sensitivity with respect to the design variables is obtained at a cost nearly independent of the number of investigated operating points. The objective function gradient is then assembled as a weighted sum of the sensitivities calculated for the different operating points. The developed multi-point optimization method is applied to a single-stage transonic compressor and both the back pressure and the rotor wheel speed are varied to investigate the use of adjoint-based design methods to efficiently explore robust turbomachinery designs.
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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 | 0.001 | 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 | 0.001 |
| 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".