Computational Fluid Dynamics Model for Sensitivity Analysis and Design of Flow Conditioners
Bibliographic record
Abstract
Flow conditioners are used to measure flow rate more accurately. The sensitivity of flow measurement devices to swirling flows and not fully developed flows are subjects of concerns to flowmeter manufacturers as well as industries. Inaccurate flow measurement occurs in the presence of swirl flow and when the flow velocity profile is not fully developed. Distorted profiles occur when the piping configuration upstream of the flow measurement devices changes. Certain length of straight piping upstream of a flow meter is required to achieve acceptable flow velocity profile for expected flow meter accuracy. In some installations, it is not realistic to run lengths of piping to reach an acceptable flow velocity profile. Introducing flow conditioners into the system reduces piping needed to reach fully developed flow and significantly weaken swirling flows. In this study, a Computational Fluid Dynamics (CFD) model is developed and validated which is used to investigate systematically the sensitivity of various parameters for perforated flow conditioners. Published data and an experimental setup was used to verify the CFD model.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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".