Coarse-graining characterization of the room flow circulations due to a fan-array wind generator
Bibliographic record
Abstract
Fan-array wind generators (FAWGs) are being employed for unmanned aerial vehicle testing. Such testing requires uniform blowing generated from the FAWGs. However, achieving uniform blowing is impeded by the wall effects within the enclosed room. These wall effects also lead to complex flow circulations. Understanding the room flow circulations can provide insight into restoring the uniformity of FAWG blowing. In this study, a coarse-graining characterization methodology is proposed to extract the room flow circulations. The key enabler is discretizing the flow domain into regular boxes as coarse-grained units and reducing the continuous flow field to flow transfers among the units. The flow circulation structure is characterized by kinematic features, i.e., the flow loop paths. The methodology is demonstrated on a numerical simulation of the room flow generated by the world's largest FAWG in the Shenzhen unmanned aerial vehicle test center. First, an analysis of the room flow kinematics shows a deflection and velocity decay of the jet-like flow. Second, two- and three-dimensional kinematic feature identifications indicate that horizontal circulations dominate the room flow. Third, two triangular prisms are introduced to manipulate the whole room circulations to improve the flow characteristics in the drone testing region. The right-angle prism reduces the flow deflection and enhances the flow activity in the test region by orienting flow circulations from horizontal to vertical. Meanwhile, the acute-angle prism creates complex flow circulations. The proposed methodology facilitates the identification and improvement of kinematic features and contributes to the physical understanding of a flow circulation structure in complex configurations.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| 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) | 0.001 | 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".