Application of active control techniques to the turbomachine fan noise reduction

1999 ◽  
Vol 105 (2) ◽  
pp. 1350-1350
Author(s):  
J. Julliard ◽  
Ch. Lozachmeur ◽  
D. Berge
Keyword(s):  
Noise Reduction ◽  
Active Control ◽  
Fan Noise ◽  
Control Techniques

Active control of fan noise

2008 ◽  
Vol 17 (2) ◽  
pp. 163-169 ◽  
Author(s):  
Nobuhiko Yamasaki ◽  
Hirotoshi Tajima
Keyword(s):  
Active Control ◽  
Fan Noise

The Use of Damping Based Semi-Active Control Algorithms in the Mechanical Smart-Spring System

2017 ◽  
Vol 140 (2) ◽  
Author(s):  
Wander Gustavo Rocha Vieira ◽  
Fred Nitzsche ◽  
Carlos De Marqui
Keyword(s):  
Active Control ◽  
Control Algorithm ◽  
Control Strategies ◽  
Vibration Reduction ◽  
Flow Analysis ◽  
Coupled Systems ◽  
Control Technique ◽  
Control Algorithms ◽  
Control Techniques ◽  
Spring Mechanism

In recent decades, semi-active control strategies have been investigated for vibration reduction. In general, these techniques provide enhanced control performance when compared to traditional passive techniques and lower energy consumption if compared to active control techniques. In semi-active concepts, vibration attenuation is achieved by modulating inertial, stiffness, or damping properties of a dynamic system. The smart spring is a mechanical device originally employed for the effective modulation of its stiffness through the use of semi-active control strategies. This device has been successfully tested to damp aeroelastic oscillations of fixed and rotary wings. In this paper, the modeling of the smart spring mechanism is presented and two semi-active control algorithms are employed to promote vibration reduction through enhanced damping effects. The first control technique is the smart-spring resetting (SSR), which resembles resetting control techniques developed for vibration reduction of civil structures as well as the piezoelectric synchronized switch damping on short (SSDS) technique. The second control algorithm is referred to as the smart-spring inversion (SSI), which presents some similarities with the synchronized switch damping (SSD) on inductor technique previously presented in the literature of electromechanically coupled systems. The effects of the SSR and SSI control algorithms on the free and forced responses of the smart-spring are investigated in time and frequency domains. An energy flow analysis is also presented in order to explain the enhanced damping behavior when the SSI control algorithm is employed.

scholarly journals Active control of axial and centrifugal fan noise

2013 ◽  
Author(s):  
Scott D. Sommerfeldt ◽  
Kent L. Gee
Keyword(s):  
Active Control ◽  
Centrifugal Fan ◽  
Fan Noise

Design, Dynamics and Active Control of Micro Interferometers for Low Noise Parallel Operation

2008 ◽  
Author(s):  
Omkar Karhade ◽  
Levent Degertekin ◽  
Thomas Kurfess
Keyword(s):  
Noise Reduction ◽  
Range Of Motion ◽  
Active Control ◽  
Control Algorithm ◽  
Cutoff Frequency ◽  
Low Noise ◽  
Experimental Results ◽  
Ambient Vibration ◽  
Limited Range Of Motion ◽  
Tunable Gratings

Micromachined Scanning Grating Interferometer (μSGI) array offers a viable solution to the high resolution, large bandwidth, non-contact and high throughput metrology. Parallel active control of μSGIs is necessary to reduce the effect of positioning errors and ambient vibration noise. To achieve individual control of the μSGIs, the gratings in the μSGI are micromachined on Silicon membranes, which can be electrostatically actuated. These tunable gratings are designed to have sufficient range of motion (∼400nm) and sufficient bandwidth (∼50kHz) for effective noise reduction. The tunable gratings are fabricated successfully using Silicon on Insulator wafers with a two mask process. A novel recurrent calibration based control algorithm is designed to actively control the tunable gratings. The novel algorithm is implemented digitally using FPGA on an array of μSGIs simultaneously. The algorithm compensates for the non-linearities of the actuator and problem due to limited range of motion. A system model is built to design and analyze the control algorithm and is verified by experimental results. Experimental results show 100 times noise reduction at low frequencies and 6.5kHz noise reduction cutoff frequency. A resolution of 1×10−4 nmrms/√Hz is achieved by implementation of this algorithm on μSGI.

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