Multi-harmonic active structural acoustic control of a helicopter main transmission noise using the principal component analysis

Commercial active noise and vibration control systems for vehicles are gradually developing, mainly in Japan, for both active noise control of interior sound and active control engine mounts [1]. There have been four Japanese commercial applications: • Active control for booming noise by Nissan in 1991, • Active vibration mount for idle noise and vibration by Toyota in 1997, • Active vibration mount for diesel engines by Nissan in 1998, • Active control for low-frequency road noise by Honda in 2000. Other manufacturers have, of course, developed similar systems, but these have not progressed to production applications. Additionally, active control engine mufflers using cancelling loudspeakers have been successfully demonstrated. A new type of active muffler, using a controlled oscillating butterfly valve in the exhaust line has been developed recently. The future of active control in vehicles is dependent on customers who are willing to pay the higher price for additional noise and vibration control, so that cost reductions are the essential factor in further commercial products. Trends towards fuel efficiency and lighter vehicles run counter to reducing noise and vibration. This has given new interest in active noise control and active control engine mounts as a solution to these conflicting issues.

Application of active structural acoustic control (ASAC) for helicopter cabin noise reduction is investigated using both analytical modeling and experimental tests. With ASAC, forced structural inputs from piezoelectric actuators bonded directly to the structure are used to cancel noise. These actuators are small, lightweight, inexpensive, and easily installed on aircraft surfaces, and were found to provide the necessary control authority to cancel cabin tonal noise when installed on thin sound-radiating surfaces. An analytical model of helicopter fuselage was developed and validated using experimental transfer function data. Estimates of noise reductions with different actuator configurations were determined numerically. An optimum actuator configuration was used for flight tests. . An ASAC system was developed and tested on an MD900 Explorer helicopter in flight. Cabin noise reductions were demonstrated with this system. INTRODUCTION With rapidly growing applications for rotorcraft worldwide, both passengers and crewmembers need quiet, comfortable transportation similar to that of commercial fixed wing aircraft. Helicopter interiors are significantly noisier (20 to 30 dB) than those of commercial fixed wing aircraft. Traditionally, aircraft interior noise reduction has been achieved with passive materials and devices, which are usually effective in the high frequency region, and in cases where the cabin noise is generated by aero / thermodynamic sources (e.g., rotors, engines, turbulence, etc.). However, significant weight penalties generally prevent them from being useful in the lowto mid-frequency region, and where the noise sources are mechanically generated (e.g., drive system, gearbox, etc.). In helicopters, for example, noise radiated into the cabin is typically transmitted initially as gearbox vibrations through the airframe structure before it radiates into the acoustic cavity. * Boeing Technical Fellow, Boeing-Long Beach, Member AIAA a Senior Engineer, Boeing-Mesa t Boeing Technical Fellow, Boeing-Mesa, t Roanoke Electric Steel Professor, AIAA Assoc. Fellow Presented at the 40th AIAA Aerospace Sciences Meeting at Reno, NV, January 14-17, 2002. Copyright ©The American Institute of Aeronautics and Astronautics, Inc. All rights reserved Controlling these vibrations before they become airborne sound could prove to be a more practical solution with potentially lower weight penalties than those normally incurred by purely passive techniques. Alternative means of aircraft cabin noise reduction using active control within the airframe structure were investigated for potential application to the uniquely complex helicopter cabin noise problem. Because there has been a lack of an integrated approach in applying advanced noise control approaches to new and existing rotorcraft, an analytical methodology was required for evaluation and design optimization of active (and passive) noise control concepts for helicopter interiors. Advanced noise control technologies, and the analytical means to optimize their integration into new and existing rotorcraft, were necessary to achieve acceptable levels of comfort for passengers and crew. The need for low cost, low weight rotorcraft interior noise control methods exist for both civilian and military helicopters. Military helicopters demand low noise crew stations to meet speech intelligibility requirements for reliable communication which can directly impact mission effectiveness. Specific crew station noise control requirements are specified in the U. S. Army MIL-STD-1294A. Lower interior noise levels on commercial rotorcraft will improve speech intelligibility for crewmembers, 1 American Institute of Aeronautics & Aeronautics (c)2002 American Institute of Aeronautics & Astronautics or Published with Permission of Author(s) and/or Author(s)' Sponsoring Organization. thereby improving flight safety. Reduced passenger cabin noise levels will improve passenger acceptance and provide a competitive advantage in the worldwide commuter aircraft infrastructure. HELICOPTER CABIN NOISE A large part of helicopter cabin noise is transmitted as vibratory responses through structural components before it is radiated into the enclosed space. The transmission gearbox is generally considered the major source of interior noise in helicopters, The main objective of this 3-year effort, which started in 1997, was to develop and demonstrate in flight an Active Structural Acoustic Control (ASAC) system, which reduces midfrequency transmission noise on helicopters [1]. The MD900 Explorer helicopter, shown in Figure 1, was selected as the candidate flight test vehicle for evaluating Figure 1. MD900 Explorer helicopter. and demonstrating the potential for active structural acoustic control. It is a 6250 lb., twin engine helicopter with a NOTAR®-equipped anti-torque system. Its main 0

Active control for vehicle interior noise based on DWT-FxLMS algorithm using a piezoelectric feedback system

Suppressing interior sound radiation in helicopters, fixed- wing aircraft and land vehicles is a very important problem. It has been studied quite extensively in the past few decades. There are two mainstream methods for this problem: active noise cancellation (ANC) using loudspeakers and sound radiation reduction via structural controls (often called active structural acoustic control or ASAC). An ANC system often requires an impractically high dimensionality to achieve the level of global noise reduction in a three dimensional volume that ASAC systems with a relatively low dimensionality are capable of, while actuators for structural control systems are power intensive and less reliable. This paper presents an acoustic boundary control method that may reserve the advantages of both ANC and ASAC. Numerical simulation results of interior noise control are presented to demonstrate the ability of the acoustic boundary control to cancel sound fields due to different primary sources. A discussion is also presented on the spatial characteristics of the acoustic boundary control as a function of frequency. An interesting phenomenon is discovered that may have significant implications to the actuator grouping studies.

Active control of road noise considering the vibro-acoustic transfer path of a passenger car

Hybrid noise control is the combination of both active noise and passive noise control. For high-frequency noise, we generally use passive noise control technique, and for low-frequency noise of less than 200 Hz, we use active noise control technique. Passive noise control is achieved by introducing passive techniques such as enclosures, physical barriers, and silencers to attenuate the undesired noise, whereas active noise control (ANC) is achieved by introducing a cancelling “anti-noise” signal generated by a processor or electronics circuit. Digital filters can be used for this purpose with an appropriate adaptive algorithms such as FXLMS, FXNLMS, FBLMS, SE-FXLMS, and FULMS. The development of an ANC simulation model can be divided into two parts: the modelling of primary noise and the modelling of secondary or counter noise. The anti-noise generated by the ANC system can be modelled only if we have the information of primary noise. For successful simulation of secondary noise, we have to model the control system and its interaction with the surrounding acoustical space. This article describes the practical application of active noise control as well as passive noise control to a server room, greatly reducing the level of noise in the whole area through appropriate adaptive algorithms. The results obtained using different algorithms are compared and analyzed.

Passive and active interior noise control of box structures using the structural intensity method

The theorem of principal component analysis (PCA) was proposed by Pearson in 1901. As an important statistics method, it was applied by Hotelling in the research of correlation structures of random multi-variates in 1933. Oja proposed a fast algorithm for computing the eigenvalue and eigenvector of the covariance matrix and a Hebb learning rules-based neural networks model for adaptive extractions of maximum principal components in 1982[1,2]. Afterwards Sanger proposed a neural network model for adaptive extraction of multiple principal components. Kung proposed an APEX (Adaptive Principal Component Extraction) multiple principal component learning algorithm[3], which improved the efficiency of the PCA method. Related to linear transform, the basic idea of PCA is: Obtain a group of new features from the original features through linear transform, which keeps the same number of features, and the first few features contain the main information of the original features. Then by keeping the main information, the number of features decreases, and signal subspace is separated from noise subspace, so some of noise can be removed. Specifically, by the orthogonal eigenvector matrix of autocorrelation matrix of samples, we can reconstruct signals and remove correlations, then find the sample with the largest variance and also the highest energy.

A number of projects have been carried out in Nanjing University to implement active noise control technologies, which include active control of transformer noise, active sound barrier, active noise control in communication chassis, active noise control in natural ventilation windows, active control of large impulsive noise in headset and active noise control in a train compartment. Unlike our previous research, these projects are all funded by industries and the aim is not for doing academic research, but to make commercial prototypes. Various challenges in the implementation of active noise control technologies in these projects to make commercial products are reported and discussed, and main issues to make successful commercial active noise control products are pointed out.

All-pass filtered x least mean square algorithm for narrowband active noise control

The authors present a delayed least mean square (LMS) algorithm which introduces delay in the reference input for active periodic noise control applications. Results show a reduction in out-of-band overshoot and coefficient quantization noise for fixed-point implementation. Computer simulations show an average of 5 dB overshoot constraint and 10 dB quantization noise reduction.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>

An enhanced multiple-input multiple-output (MIMO) filtered-x least mean square (FXLMS) algorithm using improved virtual secondary path is proposed as the basis for an active noise control (ANC) system for treating vehicle powertrain noise. This new algorithm is developed to overcome the limitation caused by the frequency-dependent property of the standard FXLMS algorithm and to reduce the variation of convergence speed inherent in multiple-channel cases, in order to improve the overall performance of the control system. In this study, the convergence property of the proposed algorithm is analyzed in the frequency domain in order to yield a better understanding of the physical meaning of the virtual secondary path. In practice, because of the arrangement and sensitivities of the actuators (speakers), transducers (microphones), and physical environment, the magnitude response of the main secondary paths can be very different from each other. This difference will cause difficulty in the overall convergence of the algorithm, which will result in minimal attenuation at some of the channels. The proposed channel equalized (CE) virtual secondary path algorithm is designed to tackle this difficulty by equalizing the mean magnitude level of the main secondary paths and by adjusting other secondary paths correspondingly to keep the coupling effects among the control channels unchanged. The performance of the proposed algorithm is validated by analyzing a two-input two-output active powertrain noise control system.

An active impulsive noise control algorithm with self-p-normalized method

The method of reducing automotive intake noise can be classified by passive and active control techniques. However, passive control has a limited effect of noise reduction at low frequency range (below 500 Hz) and is limited by the space of the engine room. However, active control can overcome these passive control limitations. The active control technique mostly uses the Least-Mean-Square (LMS) algorithm, because the LMS algorithm can easily obtain the complex transfer function in real-time, particularly when the Filtered-X LMS (FXLMS) algorithm is applied to an active noise control (ANC) system. However, the convergence performance of the LMS algorithm decreases significantly when the FXLMS algorithm is applied to the active control of intake noise under rapidly accelerating driving conditions. Therefore, in this study, the Co-FXLMS algorithm was proposed to improve the control performance of the FXLMS algorithm during rapid acceleration. The Co-FXLMS algorithm is realized by using an estimate of the cross correlation between the adaptation error and the filtered input signal to control the step size. The performance of the Co-FXLMS algorithm is presented in comparison with that of the FXLMS algorithm. Experimental results show that active noise control using Co-FXLMS is effective in reducing automotive intake noise during rapid acceleration.

This paper presents a new active control method of sound radiated from a double-walled structure. The purpose of this study is to control of sound radiation power level using both of active noise control and active vibration control. First, modal coupling method is introduced which is required to model a vibro-acoustic coupling system. Then, optimal control laws of active noise control and active vibration control for minimizing the sound radiation power are derived in a vector form. Next, some numerical simulations are carried out to demonstrate the validity of the proposed method. As a result of the analysis, reduction of the sound power level is succeeded by feedforward control. Some numerical results show that active noise control is more effective than active vibration control for minimizing the sound power level. In addition, it is shown that by combination of the both control methods, improvement of a control performance is achieved. Furthermore, it is found that control effect rely on the relationship between placement of control sound sources and the disturbance location, while placement of control vibration source does not have significant impact on the control performance.