A review on aerodynamic optimization of turbomachinery using adjoint method
Bibliographic record
Abstract
Improvements in aerodynamic turbomachinery design have gained attraction due to the increased demand for a more sustainable future. Several optimization approaches have been presented and employed in the realm of aerodynamic design. However, among all of them, the adjoint approach has emerged as a hot research topic for aerodynamic optimization in the field of turbomachinery. The ability of this method to efficiently compute the derivatives of objective functions for several design variables has made it a promising optimization tool. This study provides a comprehensive review of all significant studies undertaken since the turn of the 21st century when the adjoint method was employed for the aerodynamic optimization of turbomachinery applications. The application of the adjoint approach in that context is extensively discussed under various aspects, including shape optimization in both steady and unsteady flows, varied eddy viscosity, non-ideal compressible fluid-dynamics, multi-objective and multi-point optimizations, multidisciplinary optimization, coupling adjoint method with other approaches, parametrization methods, and uncertainty quantification. Finally, the review concludes by highlighting key points and outlooks on future developments.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| 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) | 0.005 | 0.002 |
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".