Driver's Ear Research Articles

Sound prediction of an electric car using numerical simulation and sound synthesis techniques

The automotive industry's transition to electrification raises new challenges to maintain a comfortable acoustics environment both inside and outside the car. This paper presents an innovative way to make an early prediction of the overall sound of a vehicle at driver's ear by mixing noise sources that can be simulated and noise sources measured at component level. This hybrid approach combining Test and Simulation data helps define acoustic and vibration specifications at component level and take decisions on design strategies. For instance, the mounting method of the Electric motor to the body can be decided using a combination of Electric motor numerical simulation and Rolling noise sound synthesis techniques. Realistic signals could be generated and compared with sound quality metrics to choose the best vibration isolation strategy.

An Experimental Performance Assessment of Temporal Convolutional Networks for Microphone Virtualization in a Car Cabin.

In this paper, the experimental results on microphone virtualization in realistic automotive scenarios are presented. A Temporal Convolutional Network (TCN) was designed in order to estimate the acoustic signal at the driver's ear positions based on the knowledge of monitoring microphone signals at different positions-a technique known as virtual microphone. An experimental setup was implemented on a popular B-segment car to acquire the acoustic field within the cabin while running on smooth asphalt at variable speeds. In order to test the potentiality of the TCN, microphone signals were recorded in two different scenarios, either with or without the front passenger. Our experimental results show that, when training is performed in both scenarios, the adopted TCN is able to robustly adapt to different conditions and guarantee a good average performance. Furthermore, an investigation on the parameters of the Neural Network (NN) that guarantee the sufficient accuracy of the estimation of the virtual microphone signals while maintaining a low computational complexity is presented.

Numerical modeling of axial fan noise in fuel-cell electric truck: from component to full-vehicle analysis

This paper presents a comprehensive approach to predict the noise generated by an axial fan in electric vehicles, which is more noticeable due to the absence of combustion engines. Such cooling fans require stronger cooling performance due to increased demand for heat dissipation, resulting in even higher noise levels. A numerical model for fan noise in a fuel-cell electric vehicle (FCEV) is proposed with two steps: component-level and full-vehicle level models. At component level, Lattice Boltzmann Method (LBM) is employed for aeroacoustics simulation, determining the component sound power level (SWL). At full vehicle level, statistical energy analysis (SEA) is utilized to estimate the sound pressure level (SPL) inside the vehicle cabin, particularly at the driver's ear locations. The noise sources in the full vehicle model, were characterized from the component level SWL, as monopole sources. The model is validated by comparing the numerical predictions with the experimental measurements of the SWL in a hemi-anechoic chamber and the SPL in the cabin. Good agreements were obtained between numerical results and measurement results, demonstrating the effectiveness of the model. This allows for faster and more accurate virtual NVH design validations on large and complex systems where prototype measurements are difficult to conduct.

Research on the Active Noise Control Technology for the Reduction of the Air Conditioner Noise in a Vehicle Cabin

In this study, an attempt to reduce the flow-induced noise generated by heating, ventilation, and air-conditioning (HVAC) systems for commercial electric vehicles using feedback active noise control with real-time filter update in frequency domain was conducted to improve the quietness of a running vehicle. In active noise control (ANC), the frequency-domain gradient descent algorithms (FDGD) to adapt the noises was studied. In the experiment, air conditioner noise was measured using a microphone placed at the driver's left ear and referenced by a microphone at a map lamp for ANC. Results shows that in the 1/3-octave band, the sound pressure level in driver's left ear was 7.2 dB smaller at maximum when the control was operated. The effective frequency bands were between 50 and 150 Hz. Its reduction was 4.7 dB on average.

Fluctuating aerodynamic noise radiated from longitudinal vortex in the flow field with turbulence

The paper focuses on fluctuating aerodynamic noise of automobiles, that is, time-varying wind noise which can significantly affect driver annoyance during at high cruising speed, independent of average loudness. One of the main aerodynamic noise sources is considered as longitudinal vortex behind the front pillar of a vehicle located near driver's ear. The mechanism of fluctuating aerodynamic noise was investigated from quantitative and qualitative viewpoints. Firstly, regarding quantitative noise level, wind-tunnel experiments and numerical simulations have been conducted for the longitudinal vortex around the delta wing subjected to the wake of a circular cylinder. The both results showed that the aerodynamic noise increases around the vortex-shedding frequency by 10 to 20 dB. Numerical analysis investigated the effect of the flow with turbulence on the generation of aerodynamic noise. Karman vortex emitted from the cylinder induces Aeolian tone with 58 Hz. The analysis revealed the noise sources with respect to time derivatives of both vorticity and surface pressure fluctuations, which lead to the increase in noise level around 58 Hz. Secondly, concerning noise quality, modulation spectrum analysis also clarified that the vortex-shedding frequency 58 Hz shifts to the lower frequency 10 Hz, which is considered as the fluctuating aerodynamic noise.

Research on seat active noise control of driver's cab ground platform based on far-point secondary sound sources

Driver's cab noise has an impact on the long-term driving environment and physical health of drivers. The locomotive driver's cab has significant low-frequency noise due to structure-borne sound, which is suitable for effective suppression by the active noise control system. However, on the one hand, the seat headrest system hinders the driver's movement space, on the one hand, the active noise control for space zones in the driver's cab has limited noise reduction effect due to the large control area. Therefore, a ground research test platform of driver's cab for active noise control was designed, and its structure and control method were introduced in this study. Based on the near-point seat active noise reduction control system, the coherence of the reference acoustic signal of the chair to the acoustic signal at the driver's left ear is very close with the one on the floor, and both of their active noise reduction in the full frequency band from 50 Hz to 5000 Hz are very close (about 4.5 dB (A)). In order to alleviate the interference of the headrest system installed with secondary sound sources on the driver's operating space, a far-point active noise reduction control system was proposed. The results showed that when two reference microphone signals from the seat back and floor position are used at the same time, the active noise reduction next to the driver's left ear in the full frequency band of 50 Hz to 5000 Hz is 4.0 dB(A), which is still significant. This provides a useful reference for the engineering application of active noise control systems in rail driver's cabs.

On the reduction of the flow-induced noise using porous material plates with high acoustic transmissibility

This study investigates the efficacy of closed-structure porous materials for reducing flow-induced noise, given their high acoustic transmissibility and low fluid permeability. A multifaceted approach encompassing wind tunnel experiments, numerical simulations, and practical applications to heating, ventilation, and air-conditioning (HVAC) systems in commercial electric vehicles was adopted. Wind tunnel experiments demonstrated a significant reduction, over 20 dB, in the tonal noise generated from a rectangular cylinder and an increase in its frequency, attributable to changes in the vortex structure and velocity fluctuations in the wake of the cylinder. The numerical simulation of fluid permeation and pressure wave conditions on the surface of the porous material as a boundary condition aligned with the experimental outcomes, suggesting that such permeation conditions could lead to noise and drag reduction. Practical testing showed a 5 dB noise reduction at the driver's ear position when porous materials were attached to a portion of an electric vehicle's HVAC system. The noise reduction was associated with changes in velocity fluctuations in the flow field, aligning with the wind tunnel experimental findings. This study provides promising insights into the development of effective noise reduction strategies using porous materials.

An Efficient Hybrid Computational Process for Interior Noise Prediction in Aeroacoustic Vehicle Development

Numerical methodologies for aeroacoustic analyses are increasingly crucial for car manufacturers to optimize the effectiveness of vehicle development. In the present work, a hybrid numerical tool based on the combination of a delayed detached-eddy simulation and a finite element model, which relies on the Lighthill's acoustic analogy and the acoustic perturbation equations, is presented. The computational aeroacoustics is performed by the software OpenFOAM and Actran, concerning respectively the CFD and the FEM. The aeroacoustic behavior of the SUV Lamborghini Urus at a cruising speed of 140 km/h has been investigated. The main aerodynamic noise phenomena occurring in the side mirror region in a frequency range up to 5 kHz are discussed. The numerical simulations have been verified against the measurements performed in the aeroacoustic wind tunnel of the University of Stuttgart, operated by FKFS. The predicted exterior noise propagation into the far field has been validated by comparing the sound pressure level with the experimental data measured by exterior microphones, which were located outside the turbulent region beside the wake of the side mirror. Furthermore, the noise transmission into the cabin through the side window has been modeled. Simulation results have been validated by means of interior microphones installed on the driver seat. Both the exterior and the interior noise predictions show very good correlations with experiments. Lastly, a comprehensive investigation of the most critical aeroacoustic sources has been carried out. The numerical tool has been proven to be in good accordance with the microphone array with respect to the distribution of the sound pressure level in the proximity of the side mirror. Besides, the main vortex structures involved in the generation mechanisms of wind noise have been investigated by a CFD analysis. The entire CAA process has been proven to be accurate and suitable for combined analysis between the generation mechanisms of wind noise and the resulting transfer into the interior cabin to the driver's ear as well.

Factor analysis of power seat noise using blocked force

In recent years, operating noise generated by various devices in cabin has been focused and becomes noise reduction target due to the reduction of vehicle running noise. In this study, we then conducted the noise evaluation and investigated the noise factors of a power seat at the operating condition. At first, the operating noise at around driver's ear position and vibration of the seat frame were measured using an artificial head microphones and acceleration sensors. As a result, the noise and vibration at 400-500 Hz band was found to be large and the reduction was essential to decrease the noise. Subsequently, Component TPA was applied to understand the main factor of the large vibration of the frame at the target frequency. The result shows the vibration was increased largely due to the resonance between the rotational order input force from the driving motor and the natural frequency of the frame. As an instance of the countermeasure, small weight was added to the resonated part of the frame to avoid the resonance and the vibration and noise was observed to decrease at the target frequency band.

Vision-based driver ear recognition and spatial reconstruction

How to identify the ear hole more accurately and more quickly are critical for the active noise reduction in the road vehicle. Obscurations, causing by the long hair, the earphone, and some kind of caps, make the spatial location and orientation of the driver’s ears almost impossible. To solve these two problems, a new method was proposed to replace the original image scaling detection by combining the Haar-like algorithm with the scale size of the variable detection window in the recognition process. A depth positioning camera is added to the ear recognition, which may offer more accurate point vectors for the driver’s two ear holes. A simplified spatial reconstruction model was developed to assist the search when the piercing was occluded. After calculating the image-camera-cab projection mapping relationship, only four feature points are enough for the model’s data requirement. The test results show that the detection Frame Per Second (FPS) was improved to 8.8, and the ear hole’s positioning error is less than 0.1 cm. The proposed system can locate ear holes even with 100% obscurations. The research may have potential application as a front-end subsystem for the active noise reduction and voice interaction systems, improving the quality of acoustic control in cars.

Noise Reduction in Tractor Cabs Using Coupled Resonance Acoustic Materials

The noise level in a tractor cab directly affects the driving experience. Reducing the noise level in a tractor cab is an important means of improving the image of the product brand and protecting the physical and psychological health of employees. To reduce medium- and low-frequency noise in a tractor cab, the influence of the installation of coupled resonance acoustic materials (CRAMs) on tractor driver ear noise is investigated in this study. Theories for sound absorption by perforated plates and sound insulation by thin membranes are used to design a CRAM composed of a perforated plate, elastic membrane and additional mass. The acoustic impedance and sound absorption coefficient of the CRAM are obtained by an equivalent circuit method, and the resonance frequency of a local resonance system composed of an elastic membrane and additional mass is calculated. The effects of different hole diameters and porosities of the perforated plate on the CRAM sound absorption coefficient are compared. The results of tests performed using impedance tubes over a wide frequency range of 50 Hz~1600 Hz show that the maximum sound absorption coefficient is 0.99 and several transmission loss peaks of 15 dB~26 dB. Measurement of the acoustic response of the CRAM installed on the cab front panel shows that an ear noise reduction of approximately 1.3 dB(A) for a tractor working speed of 2200 rpm. The results show that the designed CRAM has a relatively good effect on reducing driver ear noise over a wide frequency band and is a feasible scheme for noise reduction in a tractor cab.

Acoustic analysis of a single-cylinder diesel engine using magnetized biodiesel-diesel fuel blends

Fuels have important effects on the quality parameters of engines such as noise pollution and vibration. Diesel fuel are used in wide range of applications especially in compression ignition engines. The objective of the present research is evaluation of the noise emitted from a single-cylinder diesel engine using magnetized biodiesel-diesel fuel blends. In general, samples were provided with different percentages of the biodiesel-diesel blends as diesel (100, 95, 90, and 80%) and biodiesel (0, 5, 10, and 20%) by applying magnetic field (0, 5300 and 7000 G) to fuel line known as DxByMz. The measurements were done on a power tiller engine at a 10 cm distance from driver ear with three replications. The statistical approach in time domain and signal processing in frequency domain were applied for data analysis. The results of the variance analysis approved significant differences between the studied fuel blends and magnetic levels at 1% probability level. The highest and lowest average value of sound pressure level was corresponded to D100B0M0 and D80B20M5300, respectively. The results in frequency domain showed that the maximum sound pressure level values were in the frequency range of 31.5–200 Hz for all fuel blends and magnetic levels. The frequencies related to the maximum sound values varied by changing biodiesel percent and magnetic level.

Detection of driver manual distraction via image-based hand and ear recognition

Driving distraction is a leading cause of fatal car accidents, and almost nine people are killed in the US each day because of distracting activities. Therefore, reducing the number of distraction-affected traffic accidents remains an imperative issue. A novel algorithm for detection of drivers’ manual distraction was proposed in this manuscript. The detection algorithm consists of two modules. The first module predicts the bounding boxes of the driver's right hand and right ear from RGB images. The second module takes the bounding boxes as input and predicts the type of distraction. 106,677 frames extracted from videos, which were collected from twenty participants in a driving simulator, were used for training (50%) and testing (50%). For distraction classification, the results indicated that the proposed framework could detect normal driving, using the touchscreen, and talking with a phone with F1-score 0.84, 0.69, 0.82, respectively. For overall distraction detection, it achieved F1-score of 0.74. The whole framework ran at 28 frames per second. The algorithm achieved comparable overall accuracy with similar research, and was more efficient than other methods. A demo video for the algorithm can be found at https://youtu.be/NKclK1bHRd4.

Assessing acoustic emission in 1055I John Deere combine harvester using statistical and artificial intelligence methods

Agricultural mechanisation is accompanied by several challenges, one of them is the noise pollution caused by machinery. Noise pollution has undesirable effects on humans such as temporary or permanent loss of hearing, decrease in working efficiency and increase in accidents. The aim of this study is to assess noise pollution in the 1055I John Deere combine harvester. The tests were performed at different engine speeds, gear positions and sound measuring locations. The obtained data were analysed in the form of factorial test based on a completely randomised design. An artificial neural network model was created to predict the sound level in the combine. The results of variance analysis for the effects of the main factors on the level of sound were significant at probability level of 1%. The sound intensity reaching the driver's ear based on frequency analysis is equal to 84.16 dB at the frequency of 4000 Hz. The mean square error and the correlation coefficient for the best neural network with ten neurons in the hidden layer were obtained. The permitted time duration of driving the harvester was calculated to be less than 2 h.

Channel self-adjusting filtered-x LMS algorithm for active control of vehicle road noise

Current active road noise control (ARNC) systems, configured with the standard filtered-x least mean square (FxLMS) algorithm, are not sufficient enough to yield an ideal noise reduction over a broad frequency range. This is because ARNC systems generally employ multiple reference signals, which has an inherent limitation of the channel-dependent convergence behaviour due to dynamic characteristics amongst reference signals. In this study, an effective ARNC system with the channel self-adjusting FxLMS (CSFxLMS) algorithm by incorporating the self-adjusting parameter on each reference signal path is proposed, which is to minimise the effect of its dynamics characteristics in different reference channels. To validate the effectiveness of the proposed algorithm, numerical simulations using measured road noise response is conducted. Results show that the performance of the proposed CSFxLMS algorithm is significantly better as compared to the conventional FxLMS algorithm, and is able to achieve around 5 dBA reductions at the driver's ear position.

Analysis of structure-borne noise in vehicles by modifying the stiffness of joint parts in the suspension system and vehicle body

This study aims to change and improve the transmitted force to the vehicle body, the noise transfer function (NTF), and vehicle interior noise by modifying the stiffness of joint parts, which are the local areas of the subframe and body in white (BIW) connected to isolators. We adopted frequency response function (FRF)-based substructuring in order to understand the effects of the stiffness of joint parts for reducing the vibration in the transfer path of a “chassis-isolator vehicle body”. In addition, a process that could be used to predict the vehicle interior noise was developed based on FRF-based substructuring. Using the FRF-based substructuring technique, the transmitted force from the chassis to the vehicle body caused by the increased stiffness of the joint parts in the chassis and vehicle body was calculated. Subsequently, the NTF from the joint parts of a vehicle body to the driver's ear location was calculated. In the chassis, the transmitted force to the vehicle body increased or decreased with increasing stiffness of the joint parts of the chassis, and the vehicle interior noise showed similar tendency of the transmitted force. In the vehicle body, the transmitted force to the vehicle body increased when the stiffness of the joint parts of the BIW was increased. However, as the noise transfer function was reduced beyond the increase in the transmitted force, the vehicle interior noise decreased. The role of the isolator stiffness is dominant for determining change of the transmitted force, which affects the vehicle interior noise. Therefore, in order to reduce the vehicle interior noise, the stiffness of isolator and joint parts should be set in consideration of the transmitted force from the chassis to the vehicle body.

Acoustic Analysis of a Single Cylinder Diesel Engine Using Biodiesel Fuel Blends

Abstract Fuel type has a direct effect on the quality of IC engine's combustion phenomenon. One of the most important quality parameters that can be fluctuated by fuel type is engine noise. The purpose of this study is to analyze the noise parameter of a diesel engine using B0, B5 (5% vol., biodiesel and 95% vol., diesel blends), B10, B15, B20, B25 and B30 biodiesel–diesel blends. This study was carried out at stationary position and at three positions such driver's left ear position (Drivers Left Ear Position-DLEP), 1.5 meter (1.5 meter Away From Exhaust-MAFE) and 7.5 meters (7.5 meter Away From Exhaust-MAFE) away from exhaust at 6 engine speeds (1200, 1400, 1600, 1800, 2000 & 2200 RPM). The results proved that the lowest and highest Sound Pressure Level (SPL) of power tiller takes place at B10, and B30 respectively. The SPL increased by 7.8 dB for increasing engine speed from 1200 to 2200 RPM. The test results showed that the average SPL at DLEP was 4.3 dB higher than 7.5 MAFE position. The dominant frequency of engine noise was315 Hz that exhaust structure is the source of it. In this frequency SPL of B10 was, 23%lower than thefuelB30 (a mixture of 30percentbiodiesel and70percentdieselfuel, respectively). The slightest and strongest sound by using theB10, B30 fuel mixture was produced respectively.

Numerical modeling and active noise control of impact road noise inside a vehicle compartment

Even though active noise control (ANC) technique has been widely investigated and proven its efficiency for low-frequency vehicle interior noise control, current applications are mainly for the steady noise process such as powertrain noise and uniform road noise. When impulsive road disturbances due to road bumps or potholes are present, they may hinder the performance of the ANC system. One of the reasons is that the prevalent ANC algorithm, namely the filtered- x least mean square (FxLMS) algorithm, has been developed assuming that the signals follow the normal distribution. The impact road noise due to a sudden impact may exhibit a non-Gaussian characteristic. Hence, the FxLMS algorithm may not be appropriate for controlling this type of impulsive noise. In this study, a robust ANC system configured with a modified FxLMS (MFxLMS) algorithm by incorporating thresholds on reference and error signal paths is proposed for impact road noise control. To aid in the control system design, the spectral-based substructuring technique is implemented to develop a coupled vehicle system model to simulate the impulsive interior acoustic response due to road unevenness. The vehicle passenger compartment is simplified as a 3-dimensional flexible-panel backed cavity model, which is coupled with the tire-wheel system modeled as a flexible ring element and rigid wheel through the suspension system represented by a spring-damper model. The tire-road interaction is modeled as a set of flexible elements with certain stiffness over a half-cosine wave road bump. Numerical simulation results show that the proposed ANC system can effectively deal with the interior impact road noise without instability issue that may occur in the existing FxLMS algorithm. In fact, results show that approximately 8 dB reductions are achieved at the driver's ear position.

Simulation Analysis and Experiment Research on Vehicle Interior Adaptive Active Noise Control System

In the MATLAB/Simulink environment through the simulation analysis, has determined the NFXLMS algorithm of the filter order and the convergence factor control parameters in the vehicle adaptive active noise control system used; adaptive active interior noise control system was constructed in a light bus, the tested vehicle engine was conducted noise cancellation control when its work in 1080rpm and 2400rpm; the results show that, the overall amount of noise reduction of driver's right ear position respectively 9.7dB (Lin) and 8.9dB (Lin).

A Study on Noise Test and Control of Combine Harvester

Faced at the present situation that the noise level of most combine harvesters was generally higher than the national standard limit in our country, experiments carried out to reduce the driver's ear noise, which chosen 4LZ-5.0E type tracked combine harvester as experimental prototype. The method of noise sources separation was used to test the whole machine on different working conditions. Based on the contribution analysis to obtain the contribution of main working parts for the driver's ear noise and identify the main noise sources. Through designing the reasonable structure of sound insulation for engine and installing the sound insulation plate on the left of driver, testing results showed that the noise of the driver's ear was reduced 3.1dB (A).