Dynamic pressure as a measure of gas turbine engine (GTE) performance
Bibliographic record
Abstract
Utilizing in situ dynamic pressure measurement is a promising novel approach with applications for both control and condition monitoring of gas turbine-based propulsion systems. The dynamic pressure created by rotating components within the engine presents a unique opportunity for controlling the operation of the engine and for evaluating the condition of a specific component through interpretation of the dynamic pressure signal. Preliminary bench-top experiments are conducted with dc axial fans for measuring fan RPM, blade condition, surge and dynamic temperature variation. Also, a method, based on standing wave physics, is presented for measuring the dynamic temperature simultaneously with the dynamic pressure. These tests are implemented in order to demonstrate the versatility of dynamic pressure-based diagnostics for monitoring several different parameters, and two physical quantities, dynamic pressure and dynamic temperature, with a single sensor. In this work, the development of a dynamic pressure sensor based on micro-electro-mechanical system technology for in situ gas turbine engine condition monitoring is presented. The dynamic pressure sensor performance is evaluated on two different gas turbine engines, one having a fan and the other without.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".