Wall Shear Stress Determination in a Small-Scale Parallel Plate Flow Chamber Using Laser Doppler Velocimetry Under Laminar, Pulsatile and Low-Reynolds Number Turbulent Flows
Bibliographic record
Abstract
The parallel plate flow chamber (PPFC) has gained popularity due to its applications in fields such as biological tissue engineering. However, most of the studies using PPFC refer to theoretical relations for estimating the wall shear stress (WSS) and, hence, the accuracy of such quantifications remains elusive for anything other than steady laminar flow. In the current study, a laser Doppler velocimetry (LDV) method was used to quantify the flow in a PPFC (H = 1.8 mm × W = 17.5 mm, Dh = 3.26 mm, aspect ratio = 9.72) under steady Re = 990, laminar pulsatile (carotid Re0-mean = 282 as well as a non-zero-mean sinusoidal Re0-mean = 45 pulse) and low-Re turbulent Re = 2750 flow conditions. A mini-LDV probe was applied, and the absolute location of the LDV measuring volume with the respect to the wall was determined using a signal monitoring technique with uncertainties being around ±27 μm. The uniformity of the flow across the span of the channel, as well as the WSS assessment for all the flow conditions, was measured with the uncertainties all being less than 16%. At least two points within the viscous sublayer of the low-Re turbulent flow were measured (with the y+ for the first point < 3) and the WSS was determined using two methods with the differences between the two methods being within 5%. This paper for the first time presents the experimental determination of WSS using LDV in a small-scale PPFC under various flow conditions, the challenges associated with each condition, and a comparison between the cases. The present data will be useful for those conducting biological or numerical modeling studies using such devices.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".