From active grids to fan-array wind generators: A review of turbulence generation, control, and artificial intelligence integration in wind tunnels
Bibliographic record
Abstract
Wind tunnel testing has undergone a significant transformation. Traditionally, it focused on generating uniform, low-turbulence flows for clean aerodynamic testing of aircraft models. Early advancements broadened this scope to include homogeneous and isotropic turbulence, as well as atmospheric boundary layers, which are crucial for civil engineering applications. However, modern demands, ranging from civil engineering to the low-altitude economy (e.g., drones), now require the ability to reproduce complex, spatiotemporally rich turbulence. This need has driven the emergence of novel approaches that enable more precise, adaptable, and programable flow generation. The review is structured to examine advances in active turbulence generation methods, multi-fan driving modes, control system architectures, and the emerging role of artificial intelligence in flow modulation. As a representative realization, we present a detailed case study of the Harbin Institute of Technology-Shenzhen (HIT-Sz), multi fan wind tunnel (MFWT), a high-resolution 768-fan array system capable of hybrid modulation, gust replication, and real-time feedback-driven control. These advancements position MFWTs as critical experimental platforms for next-generation aerodynamic testing, marking a significant shift toward robust and adaptable simulation of realistic atmospheric conditions for unmanned aerial vehicles (UAVs) and civil structures operating in complex, turbulent environments.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".