The Whole Annulus Computations of Particulate Flow and Erosion in an Axial Fan
Bibliographic record
Abstract
Gas turbine engines operating in a hostile environment, polluted with sand or dust particles, are susceptible to erosion damage, mostly at the front axial fans and compressors. Accurately predicting the erosion pattern and rate due to sand ingestion is one of the major challenges faced by the transportation and power industries. Maintenance costs are scrutinized and intensive research efforts are currently deployed in predictive life assessment tools to minimize the overhaul down time. The conventional prediction methods were usually based on steady-state simulations of gas-phase flows through a single blade passage per blade row to reduce the computational cost. However, the multistage turbomachinery flows are intrinsically subject to unsteadiness, especially due to stator-rotor interactions, which may affect sand particle trajectories even if a one-way coupling method is considered. Furthermore, an unsteady stator-rotor interaction requires a whole-annulus model at great computational cost to avoid simplifications of the geometries or flow physics. To study the effects of the stator-rotor interaction on sand particle trajectories and erosion, an axial fan with inlet guide vanes is investigated, based on the whole annulus computations of both steady and unsteady gas-phase flows, each of which is then followed by a Lagrangian particle tracking step. A numerical algorithm for tracking particles driven by the unsteady gas-phase flow is presented. The comparison of the numerical predictions with the experimental data confirms the validity and necessity of the unsteady computational fluid dynamics (CFD) model in providing adequate predictions of sand erosion in the axial fan.
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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 | 0.000 | 0.001 |
| 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 | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".