Pressure calculation for flows with moving surface boundaries from particle tracking velocimetry (PTV)
Bibliographic record
Abstract
The examples of flow conditions, where an object of a fixed or deformable body moves in a fluid, or the interface between the flow phases instantaneously changes its topology, are numerous in industry and natural sciences. The advent of particle image velocimetry (PIV) [1] and particle tracking velocimetry (PTV) [2] enabled the measurement of the instantaneous velocity fields in these types of complicated flow fields. As a next step, several methodologies have been developed in the past decade to calculate the pressure fields from PIV or PTV data [3,4]. These methods were developed based on the assumption of a stationary flow domain, with surface boundaries that are fixed and independent of time. This makes the current pressure calculation methods inapplicable to a flow domain with deformable moving surface boundaries. Also, for most of the two-phase flows, the capillary forces are significant and the pressure drop over the two-phase interface must be considered. Therefore, the current pressure calculators require an improvement in the formulation of the algorithms to account for the deformable volume conditions and the effect of the surface tension force. For the calculation of pressure from sparse PTV velocity data, firstly, a tessellation method is required to interconnect the irregularly spaced vectors in the flow field using a highquality mesh grid. The mesh must be dynamic and adjust itself to the moving boundaries. This tessellation method has already been developed by the current authors [5]. As the next step, equations of motion for a deformable C.V. need to be coupled with the tessellation method to calculate the instantaneous pressures in a two-phase flow field, with a moving interface, which will be the ultimate goal of the current study.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".