On the Application of Particle Image Velocimetry for Turbofan Engine Flow Quantification
Bibliographic record
Abstract
Abstract Given the robustness and maturity of contemporary particle image velocimetry (PIV) methods, detailed flow measurements are now possible in actual turbine engine environments. For instance, flow non-uniformity measurements are possible in fan outlet gas-paths by adapting PIV hardware to the restricted access afforded in such applications. In the present work, a framework is proposed and demonstrated for planning and executing stereoscopic PIV measurements in the fan outlet duct of a Pratt & Whitney Canada JT15D-1A research turbofan engine. Two case studies have been carried out by following this framework in order to demonstrate two different imaging methods — conventional lens/camera coupling and endoscopic imaging. A key step within the planning and execution framework is risk reduction, and this step resulted in considerable refinement of the methods in both cases. For instance, in endoscopic imaging, the risk reduction provided a new fluorescent paint application for obtaining near wall data not previously possible. The data obtained in the engine experiments have been used to quantify the uncertainty for both imaging methods, not surprisingly revealing 50% greater average uncertainties for endoscopic PIV versus conventional lens/camera imaging. The results from the case studies indicate that both imaging methods may be practically and economically implemented for detailed measurements in fan outlet ducts, and application demands may be carefully considered, and risks reduced, using the framework proposed.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".