High Altitude Small Engine Test Techniques at the NASA Glenn Propulsion Systems Lab
Bibliographic record
Abstract
A High Altitude Test was performed in the Propulsion Systems Lab (PSL) at the NASA Glenn Research Center using a Pratt and Whitney Canada PW545 jet engine.This engine was tested to develop a highaltitude database on small, high-bypass ratio, engine performance and operability.Industry is interested in the use of high-bypass engines for Uninhabited Aerial Vehicles (UAV's) to perform high altitude surveillance.The tests were a combined effort between Pratt & Whitney Canada (PWC) and NASA Glenn Research Center.A large portion of this test activity was to collect performance data with a highly instrumented low-pressure turbine.Low-pressure turbine aerodynamic performance at low Reynolds numbers was collected and compared to analytical models developed by NASA and PWC.This report describes the test techniques implemented to obtain high accuracy turbine performance data in an altitude test facility, including high accuracy airflow at high altitudes, very low mass flow, and low air temperatures.Major accomplishments from this test activity were to collect accurate and repeatable turbine performance data at high altitudes to within 1 percent.Data were collected at 19,800m, 16,750m, and 13,700m providing documentation of diminishing LPT performance with reductions in Reynolds number in an actual engine flight environment.The test provided a unique database for the development of engine analysis codes to be used for future LPT performance improvements.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".