Review of Active Techniques for Aerospace Vibro-Acoustic Control

Gardonio, Paolo

doi:10.2514/2.2934

Cited by 76 publications

(39 citation statements)

References 44 publications

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“…Simultaneous achievement of high stiffness and high damping would greatly benefit many applications, particularly large scale structures. To address this issue, a variety of vibration control systems has been developed such as active, semiactive, passive, and hybrid systems 6,7 .…”

Section: Introductionmentioning

confidence: 99%

Optimal synthesis of passive adaptive structural networks for damping and stiffness improvement

Lee

Semperlotti

2014

SPIE Proceedings

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The increasing interest in engineering systems with excellent mitigation of vibration and shock has required novel design approaches which provide systems with passive adaptive performance across the different fields of engineering. In this paper we present a novel modular design concept to synthesize passive adaptive structures upon a varied loading condition. This concept interconnects linear and nonlinear structural elements depending on the scale and performance requested to the final structural system. To realize this concept we developed a structural synthesis tool integrated with the genetic algorithm. The design optimization problem is formulated considering the prescribed design requirement on stiffness and damping performance. This tool optimally synthesizes the structural network by assembling the available constitutive elements in the set and successfully obtains passive adaptive assembly upon a varied loading condition in terms of vibration amplitude and frequency.

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Section: Introductionmentioning

confidence: 99%

Optimal synthesis of passive adaptive structural networks for damping and stiffness improvement

Lee

Semperlotti

2014

SPIE Proceedings

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“…Since then, ANC techniques have been of a considerable research interest. There have been plenty of ANC studies in aircraft [6][7][8][9] and car cabin applications [10][11][12][13][14][15][16][17][18][19]. Aircraft propeller and car engine are typical low frequency noise sources that are considered relatively easy to control by ANC techniques, as engine rotation can be used as a reference signal to the system that controls secondary source actuators.…”

Section: Introductionmentioning

confidence: 99%

An optimal local active noise control method based on stochastic finite element models

Airaksinen

Toivanen

2013

Journal of Sound and Vibration

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An optimal local active noise control method based on stochastic finite element models Airaksinen, T.; Toivanen, J.Airaksinen, T. & Toivanen, J. (2013). An optimal local active noise control method based on stochastic finite element models. Journal of Sound and Vibration, 332 (2013) pp.6924-6933. doi:10.1016/j.jsv.2013 AbstractA new method is presented to obtain a local active noise control that is optimal in stochastic environment. The method uses numerical acoustical modeling that is performed in the frequency domain by using a sequence of finite element discretizations of the Helmholtz equation. The stochasticity of domain geometry and primary noise source is considered. Reference signals from an array of microphones are mapped to secondary loudspeakers, by an off-line optimized linear mapping. The frequency dependent linear mapping is optimized to minimize the expected value of error in a quiet zone, which is approximated by the numerical model and can be interpreted as a stochastic virtual microphone. A least squares formulation leads to a quadratic optimization problem. The presented active noise control method gives robust and efficient noise attenuation, which is demonstrated by a numerical study in a passenger car cabin. The numerical results demonstrate that a significant, stable local noise attenuation of 20-32 dB can be obtained at lower frequencies (< 500 Hz) by two microphones, and 8-36 dB attenuation at frequencies up to 1000 Hz, when 8 microphones are used.

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“…If L error microphones are used, the vector of complex acoustic pressure at each sensor can be written as [21] p ψ x a p Bq s (1) where p T px 1 ; px 2 ; : : : px L ; ψ x is an L × N matrix for which the element l; n is the value of the shape of the normal mode n at the location of the l error microphone; a p is the vector of the N primary complex mode amplitudes; q s is the vector of the M secondary sources strengths; and B is an N × M matrix that relates mode amplitudes with secondary point sources according to…”

Section: A Minimization Of the Squared Pressurementioning

confidence: 99%

“…Since the interior noise spectrum is dominated by tones occurring at multiples of the blade-passage frequency (BPF), the reduction of interior noise at these discrete frequencies can provide a significant improvement. Hence, the application of active techniques is very suitable in cabin noise control of turboprops and a good amount of research has been carried out in this field (see [1] for a review of the different active control strategies). Usually, a global reduction of the cabin noise is pursued and the best estimator of the efficiency of the active control system is the reduction of the acoustic potential energy.…”

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confidence: 99%

Optimization of the Active Control of Turboprop Cabin Noise

Garbí

Palacios

Balastegui

et al. 2015

Journal of Aircraft

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In this paper, a modified cost function is proposed in order to achieve the maximum noise attenuation using a set of secondary sources for a harmonically excited sound field. The modified cost function drives the error signal to the optimally attenuated sound field instead of minimizing the squared pressure. Moreover, changing the value to which the error signals must be driven allows change of the control strategy from global to local. The modified cost function requires the knowledge of the attenuated sound field, which is a condition that is well suited to narrowband noises, as is the case of turboprops. A numerical example of the application of the cost function is carried out using a finite element model/boundary element model of a real turboprop, with the goal of minimizing the interior sound field in the cabin to about 17 m 3 . A maximum averaged attenuation of 7 dB at blade-passage frequencies is achieved using six secondary sources and six error sensors, and 11 dB around the head of a seated crew member if the control system is tuned to achieve local control. = acoustic pressure after minimizing the weighted potential energy p Ω = acoustic pressure inside the local volume p 0 = acoustic pressure after minimizing the potential energy q s = secondary source strength q sJ = secondary source strength to minimize the cost function q sΩ = secondary source strength to minimize the local potential energy q s0 = secondary source strength to minimize the potential energy q α0 = secondary source strength to minimize the weighted potential energy V = volume of the enclosure x = location of error sensors y = location of sources α = potential energy weighting factor ΔE p0 = attenuation loss ρ 0 = air density ϕ = phase of the acoustic pressure ψ = acoustic mode shape Ω = local volume ω = angular frequency ω n = eigenfrequency

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Review of Active Techniques for Aerospace Vibro-Acoustic Control

Cited by 76 publications

References 44 publications

Optimal synthesis of passive adaptive structural networks for damping and stiffness improvement

Optimal synthesis of passive adaptive structural networks for damping and stiffness improvement

An optimal local active noise control method based on stochastic finite element models

Optimization of the Active Control of Turboprop Cabin Noise

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