Volume 120, Issue 1, July 2006
Index of content:
- NOISE: ITS EFFECTS AND CONTROL 
120(2006); http://dx.doi.org/10.1121/1.2204443View Description Hide Description
Noise sources in an axial flow fan can be divided into fluctuating axial thrust forces and circumferential drag forces. For the popular design of a seven-blade rotor driven by a motor supported by four struts, dragnoise dominates. This study aims to suppress the dragnoise globally by active control schemes. Dragnoise features a rotating dipole and it has to be cancelled by a secondary source of the same nature. This is achieved experimentally by a pair of loudspeakers positioned at right angles to each other on the fan rotational plane. An adaptive LMS feedforward scheme is used to produce the control signal for one loudspeaker and the time derivative of this signal is used to drive the other loudspeaker. The antisounds radiated by the two loudspeakers have a fixed phase relation of forming a rotating dipole. An open-loop control scheme is also implemented for the purpose of comparison and easier implementation in real-life applications. The results show that the globally integrated sound power is reduced by about for both closed- and open-loop schemes. A possible limiting factor for the cancellation performance is found to be the presence of higher order modes of dragnoise.
120(2006); http://dx.doi.org/10.1121/1.2202908View Description Hide Description
Many active noise control(ANC)systems apply the filtered- least mean squares (FXLMS) algorithm for controller adaptation. The accuracy of path models is an important issue in these systems. Since parameter drifting in a noise field may cause model error between the secondary path and its prestored model in an ANCsystem, some ANCsystems employ two adaptive processes for path modeling and controller adaptation respectively. In this paper, a new ANCsystem is proposed with adaptive path modeling and nonadaptive controller design. The proposed ANCsystem is noninvasive without persistent excitations. It avoids the slow convergence and inevitable estimation errors in controller adaptation. A rigorous analysis is presented to prove that the new ANCsystem will converge to an optimal one in the minimum norm sense. Experimental results are presented to verify the performance of the proposed ANCsystem.