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Record W2734813898

Extension of Particle Image Velocimetry to Full-Scale Turbofan Engine Bypass Duct Flows

2017· dissertation· en· W2734813898 on OpenAlex
William M. George

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

aboutThe title or abstract carries a Canadian signal from the geographic lexicon.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueVTechWorks (Virginia Tech) · 2017
Typedissertation
Languageen
FieldEngineering
TopicTurbomachinery Performance and Optimization
Canadian institutionsnot available
Fundersnot available
KeywordsTurbofanParticle image velocimetryDuct (anatomy)Particle tracking velocimetryComputer sciencePhysicsMechanicsAerospace engineeringEngineeringTurbulenceMedicine
DOInot available

Abstract

fetched live from OpenAlex

Fan system efficiency for modern aircraft engine design is increasing to the point that bypass duct geometry is becoming a significant contributor and could ultimately become a limiting factor. To investigate this, a number of methods are available to provide qualitative and quantitative analysis of the flow around the loss mechanisms present in the duct. Particle image velocimetry (PIV) is a strong candidate among experimental techniques to address this challenge. Its use has been documented in many other locations within the engine and it can provide high spatial resolution data over large fields of view. In this work it is shown that these characteristics allow the PIV user to reduce the spatial sampling error associated with sparsely spaced point measurements in a large measurement region with high order gradients and small spatial scale flow phenomena. A synthetic flow featuring such attributes was generated by computational fluid dynamics (CFD) and was sampled by a virtual PIV system and a virtual generic point measurement system. The PIV sampling technique estimated the average integrated velocity field about five times more accurately than the point measurement sampling due to the large errors that existed between each point measurement location. Despite its advantages, implementation of PIV can be a significant challenge, especially for internal measurement where optical access is limited. To reduce the time and cost associated with iterating through experiment designs, a software package was developed which incorporates basic optics principles and fundamental PIV relationships, and calculates experimental output parameters of interest such as camera field of view and the amount of scattered light which reaches the camera sensor. The program can be used to judge the likelihood of success of a proposed PIV experiment design by comparing the output parameters with those calculated from benchmark experiments. The primary experiment in this work focused on the Pratt and Whitney Canada JT15D-1 aft support strut wake structure in the bypass duct and was comprised of three parts: a simulated engine environment was created to provide a proof of concept of the PIV experiment design; the PIV experiment was repeated in the full scale engine at four fan speeds ranging from engine idle up to 80% of the maximum corrected fan speed; and, finally, a CFD simulation was performed with simplifying assumptions to provide insight and perspective into the formation of the wake structures observed in the PIV data. Both computational and experimental results illustrate a non-uniform wake structure downstream of the support strut and support the hypothesis that the junction of the strut and the engine core wall is creating a separate wake structure from that created by the strut main body. The PIV data also shows that the wake structure moves in the circumferential direction at higher fan speeds, possibly due to bulk swirl present in the engine or a pressure differential created by the support strut. The experiment highlights the advantages of using PIV, but also illustrates a number of the implementation challenges present, most notably, those associated with consistently providing a sufficient number of seeding particles in the measurement region. Also, the experiment is the first to the author's knowledge to document the use of PIV in a full scale turbofan engine bypass duct.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.337
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.004
GPT teacher head0.229
Teacher spread0.224 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it