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Turbulence enhancement of a fan array wind generator using geometric texturing and optimization-based control

Gengshou Cao, Tamir Shaqarin, Zhutao Jiang, Yutong Liu, Yiqing Li, Nan Gao, Xiaozhou He, Bernd R. Noack

TL;DR

This work evaluates how to maximize turbulence intensity in a Fan Array Wind Generator (FAWG) by comparing discrete geometric texturing patterns and optimization-based control (PSO-TPME) under hot-wire feedback. Using a 10×10 FAWG and complementary hot-wire and PIV measurements, the study shows that checkerboard textures produce a uniform turbulence field with $Tu \approx 0.14$, while PSO-TPME achieves higher local turbulence up to $Tu \approx 0.28$ but with degraded spatial uniformity. The findings reveal a clear trade-off between global uniformity and local turbulence maximization in active turbulence generation, and they highlight the need for denser sensing to realize uniform high-intensity turbulence over larger regions. The results provide guidelines for designing FAWGs to simulate extreme turbulent conditions for UAV stability testing and suggest future work on distributed control to harmonize mean flow and fluctuation fields.

Abstract

Fan array wind generators (FAWG) are designed to generate a rich set of turbulent flows reminiscent of those found in natural environments. In this study, we experimentally investigate a square FAWG consisting of 10x10 individually controllable fans with 4 cm width and a maximum velocity of 17 m/s. The goal is to maximize the turbulence intensity in the test region. Two approaches for fan operation are investigated: first, geometric texturing of the duty cycle distribution, and second, maximization of the turbulence intensity at selected hot-wire sensors with particle-swarm optimization. We find that geometric texturing (specifically a checkerboard pattern) yields a robust, uniform turbulence field (Tu ~ 0.14) driven by jet interactions. Conversely, particle swarm optimization achieves higher local turbulence (Tu ~ 0.28) but significantly sacrifices spatial uniformity. This study underscores the trade-off between local maximization and global uniformity in active turbulence generation.

Turbulence enhancement of a fan array wind generator using geometric texturing and optimization-based control

TL;DR

This work evaluates how to maximize turbulence intensity in a Fan Array Wind Generator (FAWG) by comparing discrete geometric texturing patterns and optimization-based control (PSO-TPME) under hot-wire feedback. Using a 10×10 FAWG and complementary hot-wire and PIV measurements, the study shows that checkerboard textures produce a uniform turbulence field with , while PSO-TPME achieves higher local turbulence up to but with degraded spatial uniformity. The findings reveal a clear trade-off between global uniformity and local turbulence maximization in active turbulence generation, and they highlight the need for denser sensing to realize uniform high-intensity turbulence over larger regions. The results provide guidelines for designing FAWGs to simulate extreme turbulent conditions for UAV stability testing and suggest future work on distributed control to harmonize mean flow and fluctuation fields.

Abstract

Fan array wind generators (FAWG) are designed to generate a rich set of turbulent flows reminiscent of those found in natural environments. In this study, we experimentally investigate a square FAWG consisting of 10x10 individually controllable fans with 4 cm width and a maximum velocity of 17 m/s. The goal is to maximize the turbulence intensity in the test region. Two approaches for fan operation are investigated: first, geometric texturing of the duty cycle distribution, and second, maximization of the turbulence intensity at selected hot-wire sensors with particle-swarm optimization. We find that geometric texturing (specifically a checkerboard pattern) yields a robust, uniform turbulence field (Tu ~ 0.14) driven by jet interactions. Conversely, particle swarm optimization achieves higher local turbulence (Tu ~ 0.28) but significantly sacrifices spatial uniformity. This study underscores the trade-off between local maximization and global uniformity in active turbulence generation.

Paper Structure

This paper contains 16 sections, 8 equations, 16 figures, 3 tables, 1 algorithm.

Table of Contents

  1. Introduction
  2. METHODOLOGY
  3. Fan-Array Wind Generator
  4. Actuation of the fan array
  5. Hot-Wire Anemometry and Particle Image Velocimetry
  6. RESULTS AND DISCUSSION
  7. Geometric texturing pattern
  8. PSO-TPME Optimization-Based Control Pattern
  9. Conclusions
  10. PSO-TPME
  11. Adaptive Fitness-Based Auto-terminated Classifier
  12. Good particles:
  13. Fair particles:
  14. bad particles:
  15. Elitism and Targeted Position-Mutation
  16. ...and 1 more sections

Figures (16)

  • Figure 1: Fan array wind generator
  • Figure 2: Geometric texturing pattern:Black: active; White: inactive.
  • Figure 3: Principal sketch of the optimization-based control.
  • Figure 4: Visualization of hot-wire anemometer measurement points. The width of the 10$\times$10 fan-array is denoted by W
  • Figure 5: PIV results of the geometric texturing patterns. The first column illustrates the fan activation configurations: black denotes fully activated fans, white represents inactive (powered-off) fans, and gray indicates fans operating at 50% power. The second column shows the mean velocity fields measured in the PIV plane, while the third column presents the corresponding turbulence intensity distribution. In all color maps, blue indicates lower values and red indicates higher values.
  • ...and 11 more figures