Efficient Modeling Strategy of an Axial Compressor Fan-Stage Under Inlet Distortion
Bibliographic record
Abstract
The front fan of a turbofan aircraft engine often operates under distorted inlet flow conditions. This distortion is caused by either flight operating conditions, such as a crosswind or boundary layer ingestion, or due to its nacelle installation. These flow conditions negatively impact the aerodynamic performance of the compression system. Moreover, the asymmetry of the flow causes non-uniform circumferential pressure distortions which can trigger a strong aeromechanical response in the fan blades. Numerical simulation can contribute to the design process if it can accurately predict the aerodynamic performance penalties and the loads experienced by the fan blades, thereby identifying potential problems early in the design phase. This requires accurate accounting of the pressure loads on the fan from the upstream inlet distortion and the potential effect of the downstream stator row. The loads are inherently transient in nature, requiring solutions on the full wheel geometry. However, full wheel modeling is expensive and not practical early in the design cycle. In this work, an efficient modeling strategy is proposed for an axial compressor fan with a downstream stator row (NASA Stage 67, rotor/stator) undergoing inlet distortion. A multi-frequency frozen gust analysis using the Fourier-Transformation (FT) pitch-change method is utilized to solve this flow problem on a reduced geometry (two rotor-passages only). A once-per-revolution inlet distortion modeled as a cosine variation in total pressure is imposed upstream of the rotor. The influence of the stator row on the fan is accounted for within a transient simulation by imposing a 360 degree profile at the exit of the rotor. The profile from the stator row is obtained previously from a steady-state simulation using a multiple mixing-plane approach. In this approach the stator potential flow and the pressure variation in the stator row due to inflow distortion are accounted for. The paper compares the reduced geometry model with full wheel transient predictions, thereby demonstrating the efficiency of the proposed method both in terms of accuracy and solution speedup. Important aerodynamic performance parameters as well as flow field solution monitors are compared to assess the viability of this modeling strategy.
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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".