Light-scattering of tracer particles for liquid flow measurements
Bibliographic record
Abstract
Abstract A variety of modern flow measurement techniques use tracer particles that should accurately follow fluid motions and should scatter sufficient light to be detectable by imagining systems. These two requirements are at odds if they are to be full-filled by varying the tracer size. For this reason, other particle properties such as material, structure, and coating are also considered. While the effect of these properties on the particle response time can be estimated, it is challenging to quantify their effect on the scattered light using the Mie scattering theory. To address this issue, we investigated the light scattering properties of several commercially available tracer particles and provided simple guidelines for selecting appropriate particles. The investigations were carried out using particle images recorded in forward, side, and backward-scatter angles that are typically used in 3D-particle tracking velocimetry. The selected particles represent a wide spectrum of particle sizes and included glass, polymer, and fluorescent particles used in liquid flows. Other properties such as hollow structures and metallic coatings were also investigated. The results showed that glass particles had greater light scattering in the forward-scatter direction, while the polystyrene particles scattered more light in the back-scatter direction. The fluorescent particles had a relatively narrow intensity distribution with a strong side-scatter. We found that silver-coated glass particles had two to four times higher image intensity in the side and back-scatter cameras when compared with uncoated glass particles. The hollow glass particles had a higher forward-scatter compared with the solid glass particles. The recorded images were also used to obtain 3D particle tracks. A large intensity variation was observed along the 3D tracks that was mainly associated with the discretization of particle images on the camera sensor.
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.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".