Preliminary Model Analysis of Acoustic Noise Levels for Space Station

2014 ◽  
Vol 1016 ◽  
pp. 287-291
Author(s):  
Yao Qi Feng ◽  
Jiang Yang ◽  
Guo Song Feng ◽  
Yao Wu
Keyword(s):  
Acoustic Analysis ◽  
Acoustic Noise ◽  
Spectral Characteristics ◽  
Space Station ◽  
Low Frequency ◽  
Frequency Range ◽  
Analysis Model ◽  
Noise Levels ◽  
Noise Sources ◽  
Modeling And Analysis

This paper presents the modeling and analysis method of acoustic noise levels of whole audible frequency range for Chinese Space Station (CSS) module. UsingBoundaryElementModeling(BEM), the acoustic analysis model of low frequency range for CSS module was established. The analysis model of high frequency range was created by usingStatistical EnergyAnalysis(SEA) method. Based on the established models, the acoustic noise levels in all areas of CSS module were analyzed and the results for some typical areas are provided. Finally, the acoustic contribution of noise sources according to their spectral characteristics is analyzed and the implementation of noise control methods to reduce acoustic levels in CSS module is discussed.

scholarly journals Characterization of Noise Level Inside a Vehicle under Different Conditions

Sensors ◽  
2020 ◽  
Vol 20 (9) ◽  
pp. 2471 ◽  
Author(s):  
Daniel Flor ◽  
Danilo Pena ◽  
Luan Pena ◽  
Vicente A. de Sousa ◽  
Allan Martins
Keyword(s):  
Regression Analysis ◽  
Noise Level ◽  
Acoustic Noise ◽  
Experimental Setup ◽  
Strongly Correlated ◽  
Noise Levels ◽  
Noise Sources ◽  
Statistical Tools ◽  
Window Position

Vehicular acoustic noise evaluations are a concern of researchers due to health and comfort effects on humans and are fundamental for anyone interested in mitigating audio noise. This paper focuses on the evaluation of the noise level inside a vehicle by using statistical tools. First, an experimental setup was developed with microphones and a microcomputer located strategically on the car’s panel, and measurements were carried out with different conditions such as car window position, rain, traffic, and car speed. Regression analysis was performed to evaluate the similarity of the noise level from those conditions. Thus, we were able to discuss the relevance of the variables that contribute to the noise level inside a car. Finally, our results revealed that the car speed is strongly correlated to interior noise levels, suggesting the most relevant noise sources are in the vehicle itself.

scholarly journals Untangling cortico-striatal connectivity and cross-frequency coupling in L-DOPA-induced dyskinesia

2015 ◽  
Author(s):  
Jovana Belic ◽  
Per Halje ◽  
Ulrike Richter ◽  
Per Petersson ◽  
Jeanette Hellgren Kotaleski
Keyword(s):  
Primary Motor Cortex ◽  
Effective Connectivity ◽  
Spectral Characteristics ◽  
Low Frequency ◽  
Frequency Range ◽  
Gamma Frequency ◽  
High Gamma ◽  
Freely Behaving ◽  
Beta Frequency ◽  
Cross Frequency Coupling

We simultaneously recorded local field potentials in the primary motor cortex and sensorimotor striatum in awake, freely behaving, 6-OHDA lesioned hemi-parkinsonian rats in order to study the features directly related to pathological states such as parkinsonian state and levodopa-induced dyskinesia. We analysed the spectral characteristics of the obtained signals and observed that during dyskinesia the most prominent feature was a relative power increase in the high gamma frequency range at around 80 Hz, while for the parkinsonian state it was in the beta frequency range. Here we show that during both pathological states effective connectivity in terms of Granger causality is bidirectional with an accent on the striatal influence on the cortex. In the case of dyskinesia, we also found a high increase in effective connectivity at 80 Hz. In order to further understand the 80- Hz phenomenon, we performed cross-frequency analysis and observed characteristic patterns in the case of dyskinesia but not in the case of the parkinsonian state or the control state. We noted a large decrease in the modulation of the amplitude at 80 Hz by the phase of low frequency oscillations (up to ~10 Hz) across both structures in the case of dyskinesia. This may suggest a lack of coupling between the low frequency activity of the recorded network and the group of neurons active at ~80 Hz.

scholarly journals Identification and Removal of Non-acoustic Noise in Towed Array Sonar Using F-Κ Transform for Enhanced Torpedo Detection

2020 ◽  
Vol 14 ◽  
Keyword(s):  
Vibration Isolation ◽  
Acoustic Noise ◽  
Detection System ◽  
Mechanical Vibration ◽  
Low Frequency ◽  
Acoustic Energy ◽  
Noise Sources ◽  
Towed Array ◽  
Spatio Temporal ◽  
Towed Arrays

Low frequency passive towed array sonar is an essential component in a torpedo detection system for surface ships. Compact towed arrays are used for torpedo detection and they will be towed at higher towing speeds compared to conventional towed array sonars used for surveillance. Presence of non-acoustic noise in towed array sensors at higher towing speeds degrades torpedo detection capability at lower frequencies. High wavenumber mechanical vibrations are induced in the array by vortex shedding associated with hydrodynamic flow over the array body and cable scope. These vibrations are known to couple into the hydrophone array as nonacoustic noise sources and can impair acoustic detection performance, particularly in the forward end fire direction. Lengthy mechanical vibration isolation modules can isolate vibration induced noise in towed arrays, but this is not recommended in a towed array which is towed at high speeds as it will increase the drag and system complexity. An algorithm for decomposing acoustic and non-acoustic components of signals received at sensor level using well known frequency-wavenumber transform (F-K transform) is presented here. Frequency-wavenumber diagrams can be used for differentiating between acoustic and non-acoustic signals. An area of V shape is identified within the F-K spectrum where acoustic energy is confined. Energy outside this V will highlight non-acoustic energy. Enhanced simultaneous spatio-temporal and spatio-amplitude detection is possible with this algorithm. Performance of this algorithm is validated through simulation and experimental data.

On the Accuracy of Dynamic and Acoustic Analysis of Lightweight Panel Structures: A Comparison of ABAQUS and ANSYS

2012 ◽  
Author(s):  
Poul Henning Kirkegaard ◽  
Lars Vabbersgaard Andersen ◽  
Kristoffer Ahrens Dickow
Keyword(s):  
Acoustic Analysis ◽  
Low Frequency ◽  
Coupled Model ◽  
Acoustic Medium ◽  
Building Structures ◽  
Sound Waves ◽  
Frequency Range ◽  
Element Analysis ◽  
Structural Part ◽  
Fully Coupled

During the last couple of years, there has been an increasing focus on the vibro-acoustic performance of built environments due to increasing requirements in building codes regarding impact and airborne sound transmission. Hence, development of efficient and accurate methods for prediction of sound in such buildings is important. In the low-frequency range, prediction of sound and vibration in building structures may be achieved by finite-element analysis (FEA). The aim of this paper is to compare the two commercial codes ABAQUS and ANSYS for FEA of an acoustic-structural coupling in a timber, lightweight panel structure. For this purpose, modal analyses are carried out employing a fully coupled model of sound waves within an acoustic medium and vibrations in the structural part. The study concerns the frequency range 50–250 Hz.

Inversion-Based Feedforward Approach to Broadband Acoustic Noise Reduction

2008 ◽  
Vol 130 (5) ◽  
Author(s):  
Tom C. Waite ◽  
Qingze Zou ◽  
Atul Kelkar
Keyword(s):  
Noise Control ◽  
Feedforward Control ◽  
Acoustic Noise ◽  
Active Noise Control ◽  
Low Frequency ◽  
Control Technique ◽  
Frequency Range ◽  
Nonminimum Phase ◽  
Control Approach ◽  
Active Noise

In this article, an inversion-based feedforward control approach to achieve broadband active-noise control is investigated. Broadband active-noise control is needed in many areas, from heating, ventilation and air conditioning (HVAC) ducts to aircraft cabins. Achieving broadband active-noise control, however, is very challenging due to issues such as the complexity of acoustic dynamics (which has no natural roll-off at high frequency, and is often nonminimum phase), the wide frequency spectrum of the acoustic noise, and the critical requirement to overcome the delay of the control input relative to the noise signal. These issues have limited the success of existing feedforward control techniques to the low-frequency range of [0,1]kHz. The modeling issues in capturing the complex acoustic dynamics coupled with its nonminimum-phase characteristic also prevent the use of high-gain feedback methods, making the design of an effective controller to combat broadband noises challenging. In this article, we explore, through experiments, the potential of inversion-based feedforward control approach for noise control over the 1kHz low-frequency range limit. Then we account for the effect of modeling errors on the feedforward input by a recently developed inversion-based iterative control technique. Experimental results presented show that noise reduction of over 10–15dB can be achieved in a broad frequency range of 5kHz by using the inversion-based feedforward control technique.

CURRENT PROSPECTS FOR ASTROD INERTIAL SENSOR

2008 ◽  
Vol 17 (07) ◽  
pp. 941-963 ◽  
Author(s):  
A. PULIDO PATÓN
Keyword(s):  
Solar System ◽  
Gravitational Waves ◽  
Inertial Sensor ◽  
Low Frequency ◽  
Optical Devices ◽  
Noise Levels ◽  
Noise Sources ◽  
System Parameters ◽  
Relativity Mission

The Astrodynamical Space Test of Relativity using Optical Devices (ASTROD) is a multi-purpose relativity mission concept. ASTROD's scientific goals are the measurement of relativistic and solar system parameters to unprecedented precision, and the detection and observation of low-frequency gravitational waves to frequencies down to 5 × 10-6 Hz. To accomplish its goals, ASTROD will employ a constellation of drag-free satellites, aiming for a residual acceleration noise of (0.3-1) × 10-15 m s-2 Hz-1/2 at 0.1 mHz. Noise sources and strategies for improving present acceleration noise levels are reported.

A Comparison of Beamforming Processing Techniques for Low Frequency Noise Source Identification in Mining Equipment

2009 ◽  
Author(s):  
Hugo E. Camargo ◽  
Patricio A. Ravetta ◽  
Ricardo A. Burdisso ◽  
Adam K. Smith
Keyword(s):  
Source Identification ◽  
Hot Spots ◽  
Noise Source ◽  
Low Frequency ◽  
Adaptive Beamforming ◽  
Frequency Noise ◽  
Mining Equipment ◽  
Frequency Range ◽  
Noise Sources ◽  
Noise Source Identification

In an effort to reduce Noise Induced Hearing Loss (NIHL) in the mining industry, the National Institute for Occupational Safety and Health (NIOSH) is conducting research to develop noise controls for mining equipment whose operators exceed the Permissible Exposure Level (PEL). The process involves three steps: 1) Noise source identification (NSI), 2) development of noise controls, and 3) evaluation of the developed noise controls. For the first and third steps, microphone phased array measurements are typically conducted and data are processed using the conventional beamforming (CB) algorithm. However, due to the size and complexity of the machines, this task is not straight forward. Furthermore, because of the low frequency range of interest, i.e., 200 Hz to 1000 Hz, results obtained using CB may show poor resolution issues which result in inaccuracy in the noise source location. To overcome this resolution issue, two alternative approaches are explored in this paper, namely the CLEAN-SC algorithm and a variarion of an adaptive beamforming algorithm known as Robust Capon Beamformer (RCB). These algorithms were used along with the CB algorithm to process data collected from a horizontal Vibrating Screen (VS) machine used in coal preparation plants. Results with the array in the overhead position showed that despite the use of a large array, i.e., 3.5-meter diameter, the acoustic maps obtained using CB showed “hot spots” that covered various components, i.e., the screen deck, the side walls, the I-beam, the eccentric mechanisms, and the electric motor. Thus, it was not possible to identify which component was the dominant contributor to the sound radiated by the machine. The acoustic maps obtained using the RCB algorithm showed smaller “hot” spots that in general covered only one or two components. Nevertheless, the most dramatic reduction in “hot” spot size was obtained using the CLEAN-SC algorithm. This algorithm yielded acoustic maps with small and well localized “hot” spots that pinpointed dominant noise sources. However, because the CLEAN-SC algorithm yields small and localized “hot” spots, extra care needs to be used when aligning the acoustic maps with the actual pictures of the machine. In conclusion, use of the RCB and the CLEAN-SC algorithms in the low frequency range of interest helped pinpoint dominant noise sources which otherwise would be very hard to identify.

Structural and Acoustic Noise Sources Due to Turbulent Flow Through an Elbow: Formulation of Analysis Methods

2003 ◽  
Author(s):  
Stephen A. Hambric ◽  
L. Joel Peltier ◽  
John B. Fahnline ◽  
David A. Boger ◽  
John E. Poremba
Keyword(s):  
Power Transmission ◽  
Acoustic Noise ◽  
Low Frequency ◽  
Navier Stokes ◽  
Fe Model ◽  
Noise Sources ◽  
Turbulent Fluid Flow ◽  
Fluid Forces ◽  
Piping Systems ◽  
Flow Through

The low-frequency structure- and fluid-borne noise from elbows excited by fluctuating forces within turbulent fluid flow is investigated. Computational Fluid Dynamics (CFD) Reynolds Averaged Navier Stokes (RANS) analyses of the flow through a piping elbow with a radius to diameter ratio of 2.8 compare favorable to measurements made by previous investigators. The CFD RANS solutions are post-processed to estimate the spectra of the fluctuating wall pressures beneath the turbulent boundary layer (TBL) flow. The CFD RANS solutions are also used to identify regions within the core flow that might excite acoustic modes within the piping fluid. A finite element (FE) model of the piping walls is coupled with a boundary element (BE) model of the interior acoustic fluid and is excited by the fluctuating wall and fluid forces estimated from the CFD RANS solutions. The power transmission through the inlet and discharge ports of the elbow is computed and separated into its structure-borne and fluid-borne components. The influence of both structural and acoustic resonances on the power transmission is evident for both excitation mechanisms. The power transmission curves at the elbow ports may be used as source inputs to transfer matrix models of piping systems that contain elbows.

Sign in / Sign up

Export Citation Format

Share Document