Interior Noise Control Research Articles

Interior noise characteristics, source identification and its quantification contribution of 400 km/h high-speed train in different operating environments

Noise level is one of the core technical indicators of high-speed trains, especially for a speed of 400 km/h. Noise source identification and its contribution quantification are key for interior noise control. This article takes a certain high-speed train running up to 400 km/h as the research object. Firstly, based on the interior noise spectrum and experimental results of source identification based on spherical harmonic function, the main source locations and energy distribution inside the train are determined. Secondly, through polynomial fitting, the correlation between vibration and noise in various areas inside and outside the train and speed is determined. Subsequently, by calculating the energy contribution through area integration, the noise contribution of each interior region is determined, and then the variation law of the noise contribution of each region with the speed is determined, and the quantitative contributions of various areas inside the train are given when the high-speed train is running at 400 km/h. Finally, the vibration and noise inside the train, aerodynamic noise on the train body surface, and noise in the bogie area in open lines and tunnel operating environments are compared and analyzed, as well as their variation laws with speed. The main noise sources inside the train under two types of operating environments are identified, and the contribution rates of noise sources in different areas inside the train are analyzed, further studying the interior noise and vibration transmission characteristics. The results show that, when the high-speed train is running at 400 km/h in open line operating environment, the significant frequency range of interior noise is 40 Hz ∼ 2000 Hz, and dominant interior noise sources are located in the roof and floor. In the tunnel operating environment, the significant frequency range of interior noise is 160 Hz ∼ 1000 Hz, and the primary sources of interior noise are predominantly located in the left window and floor. For the sidewall area, the interior noise comes mainly from the vibration of the inner sidewall when in open line operating environment, while in tunnel operating environment, the interior noise comes mainly from the aerodynamic excitation of the body surface.

Artificial neural network-based sound insulation optimization design of composite floor of high-speed train

Increasing the speed of high-speed trains requires the lightweight design of vehicles to meet the economic and ecological efficiency requirements of such trains. However, these objectives conflict with the interior noise control in high-speed trains because the sound insulation of panel structures follows the mass law principle. The train floor, the main train body structure of the high-speed train, is vital for interior noise control because its sound insulation performance directly affects the interior noise levels. Owing to the complexity of the composite floor system, reliable measurement and accurate estimation of its sound insulation performance are often time-consuming and laborious. To address this situation, this study proposes an artificial neural network (ANN)-based model to predict the sound insulation characteristics of a composite floor. First, a sound insulation model of the composite floor is built based on statistical energy analysis (SEA). The sound insulation performance of 200 cases of composite floors is calculated by varying the dimensions of the extruded floor, thickness of the webs, sound-absorbing material, and wooden floor to formulate a sound insulation database of composite floors. Next, an ANN model is introduced and trained on the sound insulation database. The sound insulation prediction results obtained using the ANN model are compared to the prediction results obtained using the experiment to validate its effectiveness. Subsequently, the NSGA-II optimization method is used to optimize the sound insulation structure of the composite floor. Compared with the regular composite floor structure, the optimized structure reduced the mass of the composite floor by 10.93 kg and increased the weight of the sound insulation ( Rw) by 6.3 dB. The proposed method can be an effective, economical, and efficient tool for vehicle designers and can help promote the sound insulation optimization design of high-speed train composite floors.

A novel feedforward narrow-broadband hybrid active noise control system for excavator interior noise control

Excavator interior noise is a mixture of engine noise and impulsive noise produced by working device. Hybrid narrow-broadband active noise control (HANC) systems can efficiently eliminate the traditional mixed broad-narrowband noise. However, these HANC systems have drawbacks such as high computational complexity, unable to cope with impulsive noise and inability to obtain the best performance due to parameter selection method. To solve these problems, a novel feedforward narrow broadband HANC system consisting of four subsystems, namely, sinusoidal noise canceller (SNC) subsystem, robust broadband active noise control (RBANC) subsystem, delay narrowband active noise control (DNANC) subsystem, and supporting error calculating subsystem, is proposed. Combining the advantages of delayed least mean square algorithm in DNANC subsystem and Versoria criterion-based filtered-x least mean square algorithm in RBANC subsystem, both the computational efficiency and the robustness when encountering impulsive noise of the proposed HANC system are efficiently improved. In addition, the performance of the proposed HANC system is further improved by changing the key parameters selection method form trial-and-error method to improved particle swarm optimization (IPSO) algorithm with active factor. Both the superiority of the proposed HANC system over existing counterparts and the advantage of the IPSO algorithm with active factor in determining the key parameters are demonstrated by extensive simulations. Real-time HANC experiments in semi-anechoic chamber based on measured excavator interior mixed noise are conducted in this article. Experimental results illustrate that the proposed HANC system achieves good noise elimination performance and also being robust when encountering impulsive noise.

Active vibration control using minimum actuation power: Multiple primary sources controlled by multiple secondary sources

Minimum Actuation Power (MAP) is a novel active vibration control strategy that minimizes the total input power into the structure by monitoring the input power from the secondary source. In a previous paper, we presented the theory for MAP for a single primary source controlled by a single secondary source and demonstrated the application of MAP for rotorcraft interior noise control. In this paper, we extend the theoretical framework for MAP for multiple primary sources (excitation) controlled by multiple secondary sources (control). We show that the input power from the secondary sources is zero only when the secondary sources are located such that the phase of the cross-mobility term for each primary–secondary pair is same. This condition puts a constraint on the location of the secondary sources with respect to the primary sources so that the input power from the secondary sources is zero. We present simulations for a simply supported plate excited by two primary sources and controlled by a single secondary source that validate the theoretical findings. We also study the effect of phasing between the primary sources on MAP control performance and show that the maximum power reduction is obtained when the phase difference between the primary sources is zero. Experimental results are provided that demonstrate the feasibility of the MAP theory.

Interior noise prediction of inter-coach space of high-speed maglev trains based on wavenumber decomposition on aerodynamic excitation

Current research on noise and vibration control of high-speed maglev trains pays more attention to far-field noise, while the level of interior noise has a direct impact on the ride comfort and should be placed equal weight on. In this paper, inter-coach space, one of the main pressure fluctuation sources of a specific type of high-speed maglev train with a design speed of 600 km·h−1, is taken as the research object. The turbulent and acoustic components of wall pressure fluctuations (WPF) are separated based on a wavenumber-frequency analysis approach, and then each component is applied as different forms of source input to the vibroacoustic model, namely, finite element method-boundary element method (FEM-BEM) and statistical energy analysis (SEA) for low- and high-frequency ranges respectively, to investigate the contribution of both components to interior acoustic cavity in all frequency range. It can be seen quantitatively from the results that the amplitude of turbulent component is generally much higher than that of the acoustic one, but it can be vice versa when it comes to the interior response. The conclusion drawn in this paper are able to provide guidance for future researches on more targeted interior noise control of high-speed maglev trains.

Enhanced selective delayless subband algorithm independent of primary disturbance configuration for multi-channel active noise control system in vehicles

The selective delayless subband structure stands out as a promising algorithmic choice for the multi-channel active control of vehicle interior noise, particularly in the context of road noise. This type of algorithm reduces the eigenvalue spread of the autocorrelation matrix of the signal by decomposing the signal into subbands, and the desired subbands are activated selectively, thus achieving a significant performance improvement while consuming less computational resources compared with the traditional fullband algorithms. Nevertheless, the effectiveness of the subband algorithm appears to be closely tied to the configuration of the primary disturbance signal. In cases where the primary disturbance signal encompasses both broadband and tonal components, the performance advantage of the subband algorithm becomes constrained. In this paper, we conduct a thorough comparative analysis of two extensively employed subband algorithms, namely the Morgan and the Milani methods, through simulations employing data obtained from a multi-channel active noise control (ANC) headrest system. The results indicate that the Morgan method outperforms the Milani method when configured optimally. Subsequently, we propose an enhanced version of the subband algorithm based on the Morgan method. The enhanced algorithm incorporates an additional sinusoidal noise canceller (SNC) subsystem and a narrowband active noise control (NANC) subsystem based on local secondary path modeling to address tonal components, while the selective subband structure is employed for controlling broadband components. In addition, the computational complexity of the algorithm is analyzed. The effectiveness of the proposed algorithm is validated through numerous simulations and the ANC tests conducted on an actual vehicle utilizing the multi-channel ANC headrest system. The performance of the proposed algorithm surpasses that of traditional selective subband and fullband algorithms, and balanced binaural noise reduction is achieved.

Auxiliary active noise control system based on signal reconstruction

Faced with the problem of existing active control systems failing to obtain a reference signal and reducing or deteriorating the effectiveness of interior noise control, an Auxiliary Active Noise Control system (AANC) based on signal reconstruction technology is proposed, which main contains offline update of multi-network weights and online AANC controller. Firstly, considering the non-linear and non-stationary of the interior noise, and aiming to balance the modeling efficiency and accuracy of algorithms, based on the data fusion and compression sensing technology, a signal reconstruction model for multi-networks based on decomposition optimization is design; Then, in the online AANC controller, the offline calculation of the weight of signal reconstruction model is used to obtain the reconstruction reference signal of the control position, and based on the reconstructed signal, the controller achieves adaptive suppression of passenger ear-sides noise through the VSS-LMS algorithm. Finally, the effectiveness of the proposed AANC is verified using real vehicle data. The results show that the proposed system achieves the balance the modeling efficiency and accuracy, and is superior to existing ANC system in terms of robustness, guaranteeing the stable operation of the interior noise control.

Bayesian inference for the interior noise problem of railway vehicles

Design and control of interior noise of transportation systems, such as rollingstock, is important from the standpoint of comfort. Therefore, a technical field called "NVH" has been established and various noise reduction methods have been developed. On the other hand, noise reduction measures conflict with other performances such as weight reduction and space saving. In many cases, there is a large uncertainty in the causal relationship between noise control measures and their effects. Therefore, the conventional deterministic method leads to designing on the overly safe side for achieving the noise target value, which is not desirable from the viewpoint of other performance. One solution to these problems is the introduction of probabilistic design decision making, which is used in such as the evaluation of the seismic resistance of nuclear power plants and the reliability of bearings. The authors have developed a Bayesian inference method for estimating the transfer function level , and contribution of noise source type, with the aim of introducing probabilistic decision making into interior noise design. The estimation accuracy was verified by applying the method to artificially generated data. As a result, it was confirmed that the estimation was possible at a practical level.

Design of full vehicle PBNR based on side windows with embedded acoustic black holes

Acoustic black holes (ABH) have been proven to be a lightweight structure with wide application prospects in wave tuning, energy recovery, and vibration and noise reduction. The side window glass is one of the important noise propagating paths into vehicle interior. In this paper, a new side window glass based on ABHs is designed and the influence of the new side window glass on the full vehicle PBNR is analyzed. A full vehicle SEA model is established and verified using the PBNR test. The transmission paths contributing the most noise energy to the driver's head cavity under excitations at different positions are analyzed. The results show that the side window glass is the main weak component needed to be optimized in the analysis frequency band. The influence of the new type of side window glass on the whole vehicle PBNR is studied based on the full vehicle SEA model. It is observed that by comparing with the traditional glass, the new glass can significantly improve the PBNR at the coincidence frequency of glass under different paths and meanwhile reduce the weight of the glass. It can provide a new technology for improving vehicle interior noise control.

Study on interior noise control of minibus based on structural acoustic radiation

Aiming at the problem of excessive low-frequency noise inside a minibus, the interior noise control scheme was studied to improve the NVH (Noise, Vibration, and Harshness) performance of the vehicle based on the characteristics of the structural acoustic radiation. With acoustic contribution as the evaluation index, the ability of the structure to radiate noise to the vehicle was studied from the perspective of modal participation and panel contribution. In this study, the legacy finite element model of the vehicle was firstly established, and the model was validated through the modal test of the BIW (Body in White), and the noise transfer function analysis was carried out to identify the critical frequency points. Furthermore, the improved super element method (SEM) was used to establish the super element simulation model of the vehicle, the simulation analysis of the modal contribution and panel contribution was carried out for the critical frequency points, and the panels that provide the main contribution were determined. In addition, a single-factor experimental study was carried out on the position of the critical panel by using the dynamic vibration absorbers with different performance parameters, so as to verify the accuracy of the simulation results of the panel acoustic contribution for the critical frequency. Finally, the structural optimization schemes of the critical panels were designed and verified by experiments. It was indicated that optimizing the acoustic radiation characteristics of the structure by improving the body panel structure to reduce interior noise was feasible.

Developing a modelling approach for sound propagation through turbulent boundary layer and applying to train pantograph

Sound propagation through turbulent boundary layer (TBL) is of great significance in transports where a TBL is attached to their external surfaces, for instance, air planes and high speed trains. Precise predictions of sound propagation through TBLs to their external surfaces can be extremely useful for their corresponding interior noise control. It is well known that a TBL is classified as an inhomogeneous flow medium and existing approaches are not capable to well predict its influence on sound propagation. Insight of sound propagation through TBLs is required but relevant research on this topic is limited in the literature. In this work, a modelling approach is developed to predict the influence of TBLs on sound propagation. In the proposed approach, the ray tracing method is employed to predict the evolution of sound waves when they are passing through a TBL, with which the equivalent phase speed of sound can be obtained and used to calculate the the Doppler factor so that the traditional methods can be extended to predict sound distribution in inhomogeneous flow media. The validation of this modelling approach is performed by using two cases. The first case is to predict the convective effect of sound propagation in a uniform mean flow and the predicted results are compared with analytical solutions. The second case is sound propagation through a TBL and the predictions obtained from this proposed approach are compared with numerical simulations obtained from the finite element (FE) method. In both cases, the convective effect obtained from this proposed approach is close to the analytical solution or the FE prediction. After that, the proposed approach is used to predict the sound propagation from a high speed train pantograph to the external surfaces of the train with considering the influence of the TBL. The corresponding results are verified by using FE simulations. This work shows that the proposed approach is effective and efficient, and also, it enhances the understanding of sound propagation in inhomogeneous flow media.

Active sound quality control of vehicle interior noise using a dual sampling-rate active noise equalization algorithm

In-vehicle active sound quality control (ASQC) system is an advanced application of active noise control (ANC) technology, which always suppresses the interior sound pressure or improves the psychoacoustic features. However, the connection between the sound quality control state and the vehicle's working state is normally ignored, and the passenger's sound perception typically expects a linear relationship between the vehicle speed and sound loudness. This paper presents a dual sampling-rate active noise equalization algorithm. The low sampling-rate signal is used to improve system computing efficiency, the high sampling-rate signal is used to improve the ASQC effect, and their conversion communication uses a hold (low to high) and a sampler (high to low). The data relating to the ASQC system was collected, and some sound quality evaluations were conducted for determining two appropriate sampling-rate values. Combining a genetic algorithm and the corresponding ASQC simulation system, the algorithmic optimal convergence coefficients and gain coefficients were further determined at different engine speeds. The verification results of ASQC using this proposed algorithm show that the loudness of interior noise is effectively suppressed; the nonlinear index is reduced to 1.37, improving by 37% relative to the original noise and by 20% compared to the ANC system.

Rail Roughness Acceptance Criterion Based on Metro Interior Noise

Severe rail roughness leads to a series of problems in metro systems, particularly the vehicle noise problem. To ensure a better acoustic environment, rail roughness control is therefore one of the main concerns for the metro operators. But the existing roughness acceptance criteria are not suitable for metro interior noise control. It is an appropriate method to determine the rail roughness limit based on interior noise. A rail roughness acceptance criterion based on metro interior noise is accordingly proposed in this paper. The relationship between rail roughness and interior noise can be derived with wheel-rail noise as link. With this objective, a combined test and simulation method is adopted. A validated wheel-rfigil noise prediction model is thus established to determine the relationship between rail roughness and wheel-rail noise. Moreover, the transfer function of wheel-rail noise to interior noise is developed based on extensive field test. Using this method, the noise sensitivity to roughness wavelength and acceptance criteria at different speeds and track structures are investigated. Finally, an eclectic rail corrugation acceptance criterion on curved track is suggested in consideration of practical application.

Adaptive nonlinear ANC system based on time-domain signal reconstruction technology

When the primary reference signal obtained by the existing Active noise control (ANC) system is not accurate, the control effect of interior noise will be reduced or even fail. Considering the fault-tolerate and robustness of the system, the study proposes an adaptive nonlinear ANC system for interior noise, which contains noise signal decomposition, multi-network reconstruction model and Variable step-size LMS (VSS-LMS) algorithm. The noise signal decomposition method is used to address the non-stationary of the interior noise; Based on the signal components, the multi-network model for the noise signal reconstruction of passenger ear-sides is designed, which is pre-trained by a restricted Boltzmann machine for improved reconstruction accuracy and realize the adaptive extraction of signal features; And then based on the reconstruction signal components, the controller weights of corresponding components are adaptively updated by the VSS-LMS algorithm to control the passenger ear-sides noise. The effectiveness of the proposed adaptive nonlinear ANC system is validated using noise signal sources collected from a vehicle. Compared with the different ANC systems, the proposed system is superior in terms of fault-tolerant and robustness, which can guarantee stable work of the interior noise control.

An investigation into high-speed train interior noise with operational transfer path analysis method

Passengers' demands for riding comfort have been getting higher and higher as the high-speed railway develops. Scientific methods to analyze the interior noise of the high-speed train are needed and the operational transfer path analysis (OTPA) method provides a theoretical basis and guidance for the noise control of the train and overcomes the shortcomings of the traditional method, which has high test efficiency and can be carried out during the working state of the targeted machine. The OTPA model is established from the aspects of "path reference point-target point" and "sound source reference point-target point". As for the mechanism of the noise transmission path, an assumption is made that the direct sound propagation is ignored, and the symmetric sound source and the symmetric path are merged. Using the operational test data and the OTPA method, combined with the results of spherical array sound source identification, the path contribution and sound source contribution of the interior noise are analyzed, respectively, from aspects of the total value and spectrum. The results show that the OTPA conforms to the calculation results of the spherical array sound source identification. At low speed, the contribution of the floor path and the contribution of the bogie sources are dominant. When the speed is greater than 300 km/h, the contribution of the roof path is dominant. Moreover, for the carriage with a pantograph, the lifted pantograph is an obvious source. The noise from the exterior sources of the train transfer into the interior mainly through the form of structural excitation, and the contribution of air excitation is non-significant. Certain analyses of train parts provide guides for the interior noise control.

Development of hybrid layered structures based on natural fabric reinforced composites and warp knitted spacer fabric for acoustic applications

This study deals with the development of hybrid layered structures combining natural fabric reinforced composite plates and warp knitted spacer fabric for acoustic applications, and the evaluation of the sound absorption performances. Vacuum infusion technique was used to produce the composite plates. Jute and linen woven fabrics were used as reinforcing materials, and they were impregnated with epoxy resin. The composite plates were combined with warp knitted spacer fabric in different stacking sequences in three-layer structures. All samples were subjected to the measurement of sound absorption property using impedance tube method. The combinations of a single layer and double layers of warp knitted spacer fabric with natural fabric reinforced composite in the appropriate sequences were found to provide superior sound absorption coefficients (SAC) compared to non-hybrid layered structures. Based on the overall evaluation regarding SAC, noise reduction coefficient (NRC), and weight of the structure, the sample with the best performance was regarded as the double layers of spacer fabric backed with a jute fabric reinforced composite plate. The integration of natural fabric reinforced composites with warp knitted spacer fabric had better sound absorption performance compared to the glass fabric reinforced composites, and they were considered to have the potential of being used in interior noise control mainly in vehicles and buildings.

THEORETICAL ANALYSIS OF SEMI-ACTIVE NOISE CONTROL

significant reduction of disturbing noise can be achieved by passive, semi-active and fully active control approaches. Passive noise treatments such as dynamic vibration absorber are very robust and can be applied to obtain a broadband performance. Active noise control systems are designed to control harmonic or broadband noise. They are very effective, if the control volume is small as known from single-input/single-output systems used in active headphones. However, if distributed control is required, the control profit is not scalable, because the required multiple-input/multiple output systems must be adjusted to specific acoustic modes as known from the active control of propeller-aircraft interior noise. Semi-active control that is based on the principle of dissipation allows to combine several single-input/single-output systems without coupling. Thus semi-active approaches are capable to solve the problem of scalability. The present paper reports on a specific approach that is based on a dynamic absorber attached to a vibrating structure and coupled with a dissipating electrical network. The electrical components of this network can be adjusted to the mechanical impedance to realize dissipation. To focus on the performance principle, the theoretical investigations are presented in a dimensionless analysis.

Vibro-acoustic modelling of high-speed train composite floor and contribution analysis of its constituent materials

The floor is crucial for the interior noise control in a high-speed train. However, due to its large size (25 m × 2.87 m × 0.16 m) and complex structure (consists of multiple layers of different materials), there is no appropriate approach for floor modelling. The vibro-acoustic characteristics of the composite floor and the contributions of its constituent materials are yet to be studied. In this study, both in-situ measurements and simulation analyses were conducted to address this issue. First, the sound insulation of the floor was measured in a large semi-anechoic room using a real high-speed coach. Then, the floor vibration transmission of the same coach was measured on a high-speed line with the train running at speeds from 250 km/h to 350 km/h. Second, a systematic vibro-acoustic modelling method of the composite floor has been proposed. The modelling steps from the single structure or material to the whole floor are presented and discussed in detail. The complete model is validated by the experimental results. Finally, using the verified vibro-acoustic model of the composite floor, the contributions of its constituent materials are analysed. The results show that the spectral characteristics of the predicted results are consistent with those of experimental results, and their amplitude difference is small. The modelling method and the model are reasonable and reliable. Compared with sound insulation and absorption materials, the wooden keel has a more significant influence on the sound insulation and vibration transmission of the composite floor. Therefore, for noise and vibration control of the composite floor, the focus should be on the design of the wooden keel including the number, arrangement, and material.

Hybrid time–frequency algorithm for active sound quality control of vehicle interior noise based on stationary discrete wavelet transform

Active sound quality control (ASQC) of vehicle interior noise is to reshape the sound spectrum by considering human perception. But current ASQC method mainly controls the interior noise amplitude, ignoring the time–frequency characteristics, and active control of sharpness is a problem. According to psychoacoustic indices, the original noise needs to be decomposed into subbands to obtain targeted control. Thus, this study presents a hybrid time–frequency domain active noise equalization (TFD-ANE) algorithm based on stationary discrete wavelet transform. For get a supporting effect, a variable step-size FxLMS (VS-FxLMS) and a normalized frequency-domain block FxLMS are inspected and determined for each subband noise suppression. It can be found NFB-FxLMS can control higher frequency noise well, while VS-FxLMS just shows better control effect on the lowest frequency subband. To verify the effectiveness of proposed method, ASQC simulations are performed by using the basic ANE, VS-ANE, NFB-ANE and TFD-ANE algorithms. Moreover, three psychoacoustic indices, loudness, sharpness, and roughness, with the utmost impact on the interior sound quality, are considered to quantify the noise improvement effect. Results show that TFD-ANE achieves the optimum performance, where the loudness and roughness are further reduced by more than 38% and 46% compared with other three algorithms. In terms of sharpness, the ANE and VS-ANE are ineffective, and the NFB-FxLMS cannot improve the sharpness of nonstationary interior noise. However, the proposed TFD-ANE can reduce the sharpness by approximately 40%, which suggests a promising approach to the ASQC of vehicle interior noises and other sound-related fields in engineering.

Mechanism of interior noise generation in high-speed vehicle based on anti-noise operational transfer path analysis

Owing to the continuous development of the automobile industry, increasingly stringent performance requirements for noise, vibration, and harshness of automobiles are being presented. Interior noise control in high-speed vehicles has not been adequately addressed, owing to the complex mechanism of noise generation. As simulations performed previously focused on vehicle wind noise and tyre noise cannot adequately predict the effect on passenger ear-side noise, these issues are investigated in this study. Their effects on passengers are investigated using transfer path analysis. An anti-noise operational transfer path analysis is proposed to study noise generated in high-speed vehicles. The established anti-noise operational transfer path analysis model can eliminate crosstalks between noise source signals of different transmission paths. The model is validated by comparing the measured and calculated values of the anti-noise operational transfer path analysis model. The coherence of the input noise signal and the ear-side noise signal of the passenger is assessed using coherence analysis. By calculating and categorising the contributions of different noise sources in different locations and types, the main noise sources affecting passenger comfort are determined. The result indicates that the main noise sources affecting the passenger’s ear-side noise change from engine noise to left-A wind noise and tyre radiation noise with increasing vehicle speed, in which the proportion also increase. The proposed anti-noise operational transfer path analysis is suitable for the interior-noise analysis of high-speed vehicles, and this study may serve as a reference for future studies regarding active and passive noise control in high-speed vehicles.