Simultaneous flow and particle measurements for multiphase flows in hydraulic engineering: A review and synthesis of current state
Bibliographic record
Abstract
While multiphase flows are abundant in both natural environments and engineering applications, their analysis and quantification present challenges. In particular, simultaneously measuring the flow field and quantifying particle behavior pose many challenges. Methods based on Particle Image Velocimetry (PIV) and Particle Tracking Velocimetry (PTV) principles have been used in the last two decades to measure flow fields and dispersed (particle) phases, respectively. Despite the extensive application of these principles to multiphase flow, there are numerous approaches and techniques used to synchronize and combine PIV and PTV measurements. Combined PIV and PTV data acquisition also requires consideration of phase discrimination to obtain distinct data for the fluid and dispersed phases. In the literature, various methods have been proposed and applied to achieve phase discrimination. This review paper aims to consolidate and classify various phase discrimination techniques used in specialized applications in hydraulic engineering. These methods are categorized into optical (spectral, temporal, and hybrid) and post-processing methods, with a particular emphasis on their applicability within the realm of hydraulic engineering. Moreover, this review expands on several emerging PIV/PTV technologies and applications, where the combination of equipment and algorithms has led to significant strides in the non-intrusive measurement of multiphase flow. By consolidating and critically evaluating current methods for particle discrimination, this paper aims to enhance the scientific community's understanding of simultaneous phase velocity measurements, thereby setting the stage for advancements in multiphase flow visualization techniques. • Review of phase discrimination methods based on the underlying techniques from optical to post-processing. • Optical phase discrimination method highlighted as most prevalent, but pose technology and detection limitations. • Options for phase discrimination in processing are discussed, usable alone or with optical phase discrimination. • Examination of emerging PIV/PTV technologies in phase analysis.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 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".