Calibration and Validation of a Novel Particle Rebound and Deposition Model
Bibliographic record
Abstract
Abstract Environmental particles (e.g., sand, volcanic ash and other airborne dust) ingested into a gas turbine engine can significantly reduce engine performance, and even lead to complete power loss. This is particularly true for helicopters during taking-off, hovering, and landing. To predict this degradation quantitatively, a novel particle rebound/deposition model has been developed based on measured particle rebound characteristics and non-dimensional parameter analysis from more than seventy particle deposition tests relevant to engine hot-sections. The model was calibrated/validated with the experimental data, where sand particles impinged on square ceramic coupons at a velocity of 215 m/s and a temperature range of 1300–1580 K. Numerical simulations were carried out for these testing cases, and the particle impact rate on the coupon for each test case was obtained with a user defined function. With the particle impact rates on the coupon, the experimental particle deposition rates based on the total released particles, and the operating conditions, the coefficient of a defined model formulation was obtained. The calibrated rebound and deposition model was compiled and linked to the flow solver, and the predicted results were in good agreement with the experimental data. With added model functions, the distributions of deposited particle parameters on the coupon surface are provided.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".