The Development of a Large-Scale Particle Tracking Velocimery System for Wake Analysis of Wind-Loaded Structures
Bibliographic record
Abstract
The current study examines the capabilities of large-scale particle tracking velocimet.ry (LS-PTV) in fully resolving the wake behind a wind-loaded structure. LS-PTV measurements within a 16m 3 volume were acquired behind a 0.75m diameter sphere. The study uses a spherical obstruction as a base case to prove the feasibility of measuring wind- turbine wakes. The Reynolds number of the flow was approximately Re =1 x 10 5 . The temporal longevity of paths increased at a rate of 0.0073D/Uo per sphere diameter, indicating that the seeding particles have the ability to withstand the shear forces present in the wake. Furthermore, the mean freest.ream-velocity deficit, profiles, the st.reamwise Reynolds stress profile, and the wake-deficit, decay obtained using the LS-PTV system agreed with studies performed by Amoura et. al. [1] and Eames et. al. [2], thereby demonstrating the system's ability to accurately quantify the mean flow. Finally, st.eady-flow, ramp-up and ramp-down events were identified within the data from the time trace of the freest.ream flow. The corresponding wake structures behind the sphere during the three events were characterized using the realtime spatial measurements achievable by the LS-PTV system. During steady-flow conditions, pat.hlines exhibited high mixing and high curvature within one diameter downstream of the sphere, whereas pat.hlines further downstream were comparatively straight. In contrast., the straightening of pathlines occurred further upstream during ramp-up and ramp-down events, indicating that, high freest.ream-velocity events such as gusts have an organizing effect, on the wake, which attenuates shedding structures.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".