Active control strategies for outdoor near-surface wind field simulation in a multiple-fan wind tunnel

This paper presents a multiple-fan wind tunnel based on active control technology. The wind tunnel has 6 swivel plates which can rotate in -90°~+90° at the entrance. Each swivel plate consists of 8 fans. The wind field in the wind tunnel can be changed by controlling the fan speeds and the angles of swivel plates. We use a master-slave control structure, instead of the previously used lumped structure, to realize the separate control of each fan as well as each swivel plate. After the fan speed is improved, new control strategies are proposed according to the uniform assumption of the outdoor wind field and the effects of fans' voltages and rotation angles on wind speed and direction. Experiments' results based on several statistical indicators indicate that the new active control strategies, compared with the preciously used stochastic control strategy, can better simulate the outdoor near-surface wind fields.

[1]  H. Ishida,et al.  Chemical Sensing in Robotic Applications: A Review , 2012, IEEE Sensors Journal.

[2]  Yang Wang,et al.  Odor source localization using a mobile robot in outdoor airflow environments with a particle filter algorithm , 2011, Auton. Robots.

[3]  Li Fei Review of Active Olfaction , 2006 .

[4]  Riccardo Buccolieri,et al.  Dispersion study in a street canyon with tree planting by means of wind tunnel and numerical investigations – Evaluation of CFD data with experimental data , 2008 .

[5]  Lino Marques,et al.  Divergence-based odor source declaration , 2013, 2013 9th Asian Control Conference (ASCC).

[6]  Jing Gu,et al.  An artificial moth: Chemical source localization using a robot based neuronal model of moth optomotor anemotactic search , 2006, Auton. Robots.

[7]  Yuji Matsuda,et al.  Turbulence control in multiple-fan wind tunnels , 1997 .

[8]  Alcherio Martinoli,et al.  A comparison of casting and spiraling algorithms for odor source localization in laminar flow , 2008, 2008 IEEE International Conference on Robotics and Automation.

[9]  Akira Nishi,et al.  Turbulence generated by a wind tunnel of multi-fan type in uniformly active and quasi-grid modes , 2006 .

[10]  A Nishi,et al.  A Computer-Controlled Wind Tunnel , 1993 .

[11]  Zeng Ming,et al.  Multiple-fan active control wind tunnel for outdoor near-surface airflow simulation , 2015, 2015 34th Chinese Control Conference (CCC).

[12]  A. Nishi,et al.  Computer-controlled wind tunnel for wind-engineering applications , 1995 .

[13]  Paul F. M. J. Verschure,et al.  Moth-Like Chemo-Source Localization and Classification on an Indoor Autonomous Robot , 2011 .

[14]  Kamal Poddar,et al.  Experiments on integral length scale control in atmospheric boundary layer wind tunnel , 2011 .

[15]  António Manuel Santos Pascoal,et al.  A distributed formation-based odor source localization algorithm - design, implementation, and wind tunnel evaluation , 2015, 2015 IEEE International Conference on Robotics and Automation (ICRA).

[16]  Akira Nishi,et al.  Reproduction of wind velocity history in a multiple fan wind tunnel , 2001 .

[17]  Lino Marques,et al.  Experimental studies on chemical concentration map building by a multi-robot system using bio-inspired algorithms , 2012, Autonomous Agents and Multi-Agent Systems.

[18]  A. Nishi,et al.  Active control of turbulence for an atmospheric boundary layer model in a wind tunnel , 1999 .