Reconstruction and Reduction of Realistic Manufacturing Error Field for Uncertainty Quantification of Transonic Axial Compressor Rotor
Bibliographic record
Abstract
Abstract Turbo compressors are inevitably subject to geometric and operational uncertainties that could induce performance degradations. This vulnerability is particularly pronounced in transonic compressor rotors due to the presence of sharp shock waves, which are highly susceptible to uncertainties. While the importance of uncertainty quantification (UQ) of compressors during the design phase has received growing recognition, the existing methodologies still face a grand challenge in modeling the geometric uncertainties related to manufacturing error fields. To address this challenge, we first propose an accurate geometric reconstruction method of realistic manufacturing error fields, in which kernel density estimation and correlation constraint sampling are combined to reconstruct manufacturing error fields with arbitrary probability distributions and geometric correlations. Then, a bilayer and bidirection dimension reduction method is proposed to treat the reconstructed model for reduced input uncertainties. The proposed methods are verified and validated by multipoint UQ of the well-known axial compressor rotor, i.e., NASA Rotor 37, under multisource uncertainties from blade manufacturing error fields as well as the total pressure, total temperature, and turbulence intensity of the incoming flow. From the statistics of 126 high-quality blades, each with 600 measurement points, it is confirmed that a large portion obey arbitrary probabilistic distributions rather than Gaussian distributions, and strong spatial correlations exist across the blade manufacturing error field. Both characteristics can be accurately reconstructed through the combination of kernel density estimation and correlation constraint sampling methods. Moreover, the bilayer and bidirection dimension reduction method is capable of reducing the dimensionality of manufacturing error field to 2–17 for Rotor 37, depending on the working points and performance parameters. The UQ results of Rotor 37 indicate that the geometric and operational uncertainties are mainly responsible for the mean offset and scatter of rotor performance variations, respectively. The geometric uncertainties due to manufacturing errors exert a negative impact on rotor performance, especially at near-stall point, manifested by increased suction-surface separation flow losses due to the forward shift of the shock wave. The proposed method in this work is quite generic and can be used in UQ and robust aerodynamic design optimization of turbomachinery components.
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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 | 0.000 | 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 | 0.000 |
| 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".