Browse Topic: Interior noise
This paper focuses on the cabin sound quality refinement and the tactile vibration reduction during horn application in the electric vehicle. A loud cracking sound inside the cabin and higher accelerator pedal vibration are perceived while operating the horn. Sound diagnosis is carried out to find out the frequencies causing the cracking noise. Transfer path analysis is conducted to identify the nature of noise and the predominant path through which forces transfer. Based on finding from TPA, various recommendations are evaluated which reduced the noise to a certain extent. Operational Deflection Shape (ODS) is conducted on the horn mounting bracket and on the body to identify the component having higher deflection at the identified frequencies. Recommendations like DPDS improvement on the horn bracket and the body is assessed and the effect of each outcome is discussed. With all the recommendations proposed, the cabin noise levels are reduced by ~ 8 dB (A) and the accelerator pedal
Higher road noise is perceived in the cabin when the test vehicle encounters road irregularities like bump or pothole in the public roads. The transfer of transient road inputs inside the body caused objectionable cabin noise. Measurements are conducted at different road surfaces to identify the patch where the objective data well correlated with the noise measured at the public road. Wavelet analysis is carried out to identify the frequency zones since the events are transient in nature. TPA is carried out in time domain to identify the nature of the noise and the dominant path through which the transient road forces are transferring inside the body. Based on the outcome of TPA, various countermeasures like reduction of dynamic stiffness of suspension bushes, TMDs on the path are proposed to reduce the structure borne noise. Criteria which need to be considered for reduction of cabin noise due to transient road inputs is also discussed.
Unlike internal combustion engine (IC Engine) vehicles, the rapidly growing electric vehicle (EV) market demands tyres with superior yet often conflicting performance characteristics. The increased weight of EVs, due to their heavy batteries, necessitates robust tyres with reinforcement and higher inflation pressure. Conversely, increased wear due to higher initial torque and the need for lower rolling resistance to extend range, combined with the requirement for better grip for improved handling, call for advanced compound and tread pattern designs. EV tyres need to be stiffer, lighter, and low hysteresis, making it very hard to reduce low-frequency (20-200 Hz) interior noise that was previously masked by engine noise. This study investigates the low-frequency (20-200 Hz) structural-borne interior noise performance of EV tyres using both experimental and simulation tools. By wisely tuning the tyre's stiffness, mass, and damping properties, the necessary noise targets can be achieved
Vehicle interior noise is a crucial assessment criterion for automotive NVH. It has a significant effect on customer opinions about the quality of a vehicle. Articulation Index (AI) is one of the key sound metrics used to describe speech intelligibility and quantifies the middle and high frequency spectra associated to the internal noise of vehicle. In reality, Vehicle operating under dynamic condition experiences various air-borne noise sources such as tire rolling noise, powertrain noise, intake-exhaust noise & wind noise along with structure borne excitations such as powertrain vibrations, suspension vibrations. It is very challenging to predict cumulative effect of all these excitations to interior noise level and Articulation Index (AI) of vehicle over complete frequency range. The statistical energy analysis (SEA) is a well-known methodology being used to simulate & predict mid & high frequency noise. Objective of this paper is to present the process of development of a SEA
In the absence of engine noise, road-induced noise has become a major concern specifically for Battery Electric Vehicles (BEVs), impacting Sound Pressure Level (SPL) for both drivers and passengers. Under the influence of random road load inputs, structural vibrations which transfer from road and tire to suspension to vehicle body, the cabin interior noise, particularly at lower frequencies, is significantly affected. To improve the road-induced low-frequency structure-borne noise behaviour, which frequently perceptible as ‘booming noises’, a study was carried out to assess predominant noise sources present in vehicle and to suggest refinements in reducing the noise levels. By considering random excitations of road profile through tire patch using CD-Tire model, vehicle interior noise was computed. Subsequently, to get insight of dynamic behaviour of vehicle, various diagnostic assessments to understand the influence from structure and paths were deployed. Major contributors from body
The interior noise and thermal performance of the passenger compartment are critical criteria for ensuring driving comfort [1]. This paper presents the optimization of air conditioning (AC) compressor noise, specifically for the low-powered 1.0 L - ICE engine paired with a 120 cc IVDC compressor. This combination is quite challenging due to the high operational load & higher operating pressure. To enhance better in-cabin cooling efficiency, compressor’s operating efficiency must be improved, which necessitates a higher displacement of the compressor. However, increased displacement results in greater internal forces which leads to more structure-borne induced noise inside the cabin. For this specific configuration, the compressor operating pressure reached up to 25 bars under most driving conditions. During dynamic driving scenario, a metallic tonal noise from the compressor was reported in a compact vehicle segment. It is reported as very annoying to passengers inside. A comprehensive
In recent days, cabin variants in the tractor are preferred by the farmers for the Coziness and longer field hour operation with less fatigue. Noise perceived by customer is the most important factor taken into account during the design stage, as it’s directly linked with operator’s comfort. Observed noise levels has to be within the defined limits as per national/international standards Overall cabin noise levels is contributed by the structure borne noise below 630 Hz. Structure borne noise is the noise typically radiated by the door, roof, windshield, floor, fender and structure assembly due to the engine excitation through the transmission housings and backstories. This paper depicts the process of tractor cabin structure borne noise prediction in the virtual environment. Firstly, Engine bearing loads and axle bearings has been extracted in the virtual stage from the vehicle level driveline model using commercially available MBD software. The finite element (FE) model of the cabin
In electric and hybrid vehicles, sound package optimization can follow a classical, proven, and structured approach for real-world loads, while also considering new transmission paths that might differ from those in traditional internal combustion engine vehicles. However, AVAS-induced interior noise is sometimes underestimated and therefore not taken into account during the optimization process. Nevertheless, especially at very low speeds, the presence of the AVAS can be perceived as unwanted noise inside the vehicle, potentially compromising interior comfort. In this study, a hybrid boundary element–statistical energy analysis (BEM – SEA) approach is applied to an SEA dual-motor electric vehicle demonstrator model equipped with a baseline, standard sound package to assess AVAS-induced interior noise. A standard AVAS actuator is modeled with a BEM model to compute the sound pressure levels on the exterior subsystems of the vehicle. These results are then transferred to the SEA model
Noise generated by a vehicle’s HVAC (Heating, Ventilation, and Air Conditioning) system can significantly affect passenger comfort and the overall driving experience. One of the main causes of this noise is resonance, which happens when the operating speed of rotating parts, such as fans or compressors, matches the natural frequency of the ducts or housing. This leads to unwanted noise inside the cabin. A Campbell diagram provides a systematic approach to identifying and analyzing resonance issues. By plotting natural frequencies of system components against their operating speeds, Test engineers can determine the specific points where resonance occurs. Once these points are known, design changes can be made to avoid them—for example, adjusting the blower speed, modifying duct stiffness, or adding damping materials such as foam. In our study, resonance was observed in the HVAC duct at a specific blower speed on the Campbell diagram. To address this, we opted to optimize the duct design
This ARP provides two methods for measuring the aircraft noise level reduction of building façades. Airports and their consultants can use either of the methods presented in this ARP to determine the eligibility of structures exposed to aircraft noise to participate in an FAA-funded Airport Noise Mitigation Project, to determine the treatments required to meet project objectives, and to verify that such objectives are satisfied.
Wind noise is one of the largest sources to interior noise of modern vehicles. This noise is encountered when driving on roads and freeways from medium speed and generates considerable fatigue for passengers on long journeys. Aero-acoustic noise is the result of turbulent and acoustic pressure fluctuations created within the flow. They are transmitted to the passenger compartment via the vibro-acoustic excitation of vehicle surfaces and underbody cavities. Generally, this is the dominant flow-induced source at low frequencies. The transmission mechanism through the vehicle floor and underbody is a complex phenomenon as the paths to the cavity can be both airborne and structure-borne. This study is focused on the simulation of the floor contribution to wind noise of two types of vehicles (SUV and Sports car), whose underbody structure are largely different. Aero-Vibro-acoustic simulations are performed to identify the transmission mechanism of the underbody wind noise and contribution
Mechanical light detection and ranging (LiDAR) units utilize spinning lasers to scan surrounding areas to enable limited autonomous driving. The motors within the LiDAR modules create vibration that can propagate through the vehicle frame and become unwanted noise in the cabin of a vehicle. Decoupling the module from the body of the vehicle with highly damped elastomers can reduce the acoustic noise in the cabin and improve the driving experience. Damped elastomers work by absorbing the vibrational energy and dispelling it as low-grade heat. By creating a unique test method to model the behavior of the elastomers, a predictable pattern of the damping ratio yielded insight into the performance of the elastomer throughout the operating temperature range of the LiDAR module. The test method also provides an objective analysis of elastomer durability when exposed to extreme temperatures and loading conditions for extended periods of time. Confidence in elastomer behavior and life span was
This study introduces a computational approach to evaluate potential noise issues arising from liftgate gaps and their contribution to cabin noise early in the design process. This computational approach uses an extensively-validated Lattice Boltzmann method (LBM) based computational fluid dynamics (CFD) solver to predict the transient flow field and exterior noise sources. Transmission of these noise sources through glass panels and seals were done by a well-validated statistical energy analysis (SEA) solver. Various sealing strategies were investigated to reduce interior noise levels attributed to these gaps, aiming to enhance wind noise performance. The findings emphasize the importance of integrating computational tools in the early design stages to mitigate wind noise issues and optimize sealing strategies effectively.
A test and signal processing strategy was developed to allow a tire manufacturer to predict vehicle-level interior response based on component-level testing of a single tire. The approach leveraged time-domain Source-Path-Contribution (SPC) techniques to build an experimental model of an existing single tire tested on a dynamometer and substitute into a simulator vehicle to predict vehicle-level performance. The component-level single tire was characterized by its acoustic source strength and structural forces estimated by means of virtual point transformation and a matrix inversion approach. These source strengths and forces were then inserted into a simulator vehicle model to predict the acoustic signature, in time-domain, at the passenger’s ears. This approach was validated by comparing the vehicle-level prediction to vehicle-level measured response. The experimental model building procedure can then be adopted as a standard procedure to aid in vehicle development programs.
Sound source identification based on beamforming is widely used today as a spatial sound field visualization technology in wind tunnel experiments for vehicle development. However, the conventional beamforming technique has its inherent limitation, such as bad spatial resolution at the low frequency range, and limited system dynamic range. To improve the performance, three deconvolution methods CLEAN, CLEAN-SC and DAMAS were investigated and applied to identify wind noise sources on a production car in this paper. After analysis of vehicle exterior wind noise sources distribution, correlation analysis between identified exterior noise sources and interior noise were conducted to study their energy contribution to vehicle interior. The results show that the algorithm CLEAN-SC based on spatial source coherence shows the best capability to remove the sidelobes for the uncorrelated wind noise sources, while CLEAN and DAMAS, which are based on point spread functions have definite
The implementation of active sound design models in vehicles requires precise tuning of synthetic sounds to harmonize with existing interior noise, driving conditions, and driver preferences. This tuning process is often time-consuming and intricate, especially facing various driving styles and preferences of target customers. Incorporating user feedback into the tuning process of Electric Vehicle Sound Enhancement (EVSE) offers a solution. A user-focused empirical test drive approach can be assessed, providing a comprehensive understanding of the EVSE characteristics and highlighting areas for improvement. Although effective, the process includes many manual tasks, such as transcribing driver comments, classifying feedback, and identifying clusters. By integrating driving simulator technology to the test drive assessment method and employing machine learning algorithms for evaluation, the EVSE workflow can be more seamlessly integrated. But do the simulated test drive results
Automotive audio components must meet high quality expectations with ever-decreasing development costs. Predictive methods for the performance of sound systems in view of the optimal locations of loudspeakers in a car can help to overcome this challenge. Use of simulation methods would enable this process to be brought up front and get integrated in the vehicle design process. The main objective of this work is to develop a virtual auralization model of a vehicle interior with audio system. The application of inverse numerical acoustics [INA] to source detection in a speaker is discussed. The method is based on truncated singular value decomposition and acoustic transfer vectors The arrays of transfer functions between the acoustic pressure and surface normal velocity at response sites are known as acoustic transfer vectors. In addition to traditional nearfield pressure measurements, the approach can also include velocity data on the boundary surface to improve the confidence of the
Customers are expecting higher level of refinement in electric vehicle. Since the background noise is less in electric vehicle in comparison with ICE, it is challenging for NVH engineers to address even minor noise concerns without cost and mass addition. Higher boom noise is perceived in the test vehicle when driven on the coarse road at a speed of 50 kmph. The test vehicle is rear wheel driven vehicle powered by electric motor. Multi reference Transfer Path Analysis (TPA) is conducted on the vehicle to identify the path through which maximum forces are entering the body. Based on the findings from TPA, solutions like reduction in the dynamic stiffness of the suspension bushes are optimized which resulted in reduction of noise. To reduce the noise further, Operational Deflection Shape (ODS) analysis is conducted on the entire vehicle to identify the deflection shapes of all the suspension components and all the body panels like floor, roof, tailgate, dash panel, quarter panel and
Over the past twenty years, the automotive sector has increasingly prioritized lightweight and eco-friendly products. Specifically, in the realm of tyres, achieving reduced weight and lower rolling resistance is crucial for improving fuel efficiency. However, these goals introduce significant challenges in managing Noise, Vibration, and Harshness (NVH), particularly regarding mid-frequency noise inside the vehicle. This study focuses on analyzing the interior noise of a passenger car within the 250 to 500 Hz frequency range. It examines how tyre tread stiffness and carcass stiffness affect this noise through structural borne noise test on a rough road drum and modal analysis, employing both experimental and computational approaches. Findings reveal that mid-frequency interior noise is significantly affected by factors such as the tension in the cap ply, the stiffness of the belt, and the properties of the tyre sidewall.
The influence of moisture adsorption, prior braking, and deceleration rate on the low-speed braking noise has been investigated, using copper-free disc pads on a passenger car. With increasing moisture adsorption time, decreasing severity of prior braking or increasing deceleration rate, the noise sound level increases for the air-borne exterior noise as well as for the structure-borne interior noise. The near-end stop noise and the zero-speed start-to-move noise show a good correlation. Also, a good correlation is found between the noise measured on a noise dynamometer and on a vehicle for the air-borne noise. All the variables need to be precisely controlled to achieve repeatable and reliable results for dynamometer and vehicle braking groan noise tests. It appears that the zero-speed start-to-move vehicle interior noise is caused by the pre-slip vibration of the brake: further research is needed.
Squeak and rattle (SAR) noise audible inside a passenger car causes the product quality perceived by the customer to deteriorate. The consequences are high warranty costs and a loss in brand reputation for the vehicle manufacturer in the long run. Therefore, SAR noise must be prevented. This research shows the application and experimental validation of a novel method to predict SAR noise on an actual vehicle interior component. The method is based on non-linear theories in the frequency domain. It uses the Harmonic Balance Method (HBM) in combination with the Alternating Frequency/Time Domain Method (AFT) to solve the governing dynamic equations. The simulation approach is part of a process for SAR noise prediction in vehicle interior development presented herein. In the first step, a state-of-the-art linear frequency-domain simulation estimates an empirical risk index for SAR noise emission. Critical spots prone to SAR noise generation are located and ranked. In the second step, the
The transition from ICE to electric power trains in new vehicles along with the application of advanced active and passive noise reduction solutions has intensified the perception of noise sources not directly linked to the propulsion system. This includes road noise as amplified by the tire cavity resonance. This resonance mainly depends on tire geometry, gas temperature inside the tire and vehicle speed and is increasingly audible for larger wheels and heavier vehicles, as they are typical for current electrical SUV designs. Active technologies can be applied to significantly reduce narrow band tire cavity noise with low costs and minimal weight increase. Like ANC systems for ICE powertrains, they make use of the audio system in the vehicle. In this paper, a novel low-cost system for road induced tire cavity noise control (RTNC) is presented that reduces the tire cavity resonance noise inside a car cabin. The approach is cheap in terms of computational effort (likewise ICE order
Summary: With the electrification of powertrains, noise inside vehicles has reached very satisfactory levels of silence. Powertrain noise, which used to dominate on combustion-powered vehicles, is now giving way to other sources of noise: rolling noise and wind noise. These noises are encountered when driving on roads and freeways and generate considerable fatigue on long journeys. Wind noise is the result of turbulent and acoustic pressure fluctuations created within the flow. They are transmitted to the passenger compartment via the vibro-acoustic excitation of vehicle surfaces such as windows, floorboards, and headlining. Because of their mechanical properties, windows are the surfaces that transmit the most noise into the passenger compartment. Even though acoustic pressure is much weaker in amplitude than turbulent pressure fluctuations, it still accounts for most of the noise perceived by occupants. This is because its wavelength is closer to the characteristic wavelengths of
Tire/Road noise is a dominant contribution to a vehicle interior noise and requires significant engineering resources during vehicle development. A process has been developed to support automotive OEMs with road noise engineering during vehicle design and development which has test as its basis but takes advantage of simulation to virtually accelerate road noise improvement. The process uses noise sources measured on a single tire installed on a test stand in a chassis dynamometer. The measured sources are then combined with vehicle level transfer functions calculated using a Finite-Element model for structure-borne noise and a Statistical Energy Analysis (SEA) model for airborne noise to predict the total sound at the driver’s ears. The process can be applied from the initial stages of a vehicle development program and allows the evaluation of vehicle road noise performance as perceived by the driver long before the first prototype is available. This process is also extensible to
While conventional methods like classical Transfer Path Analysis (TPA), Multiple Coherence Analysis (MCA), Operational Deflection Shape (ODS), and Modal Analysis have been widely used for road noise reduction, component-TPA from Model Based System Engineering (MBSE) is gaining attention for its ability to efficiently develop complex mobility systems. In this research, we propose a method to achieve road noise targets in the early stage of vehicle development using component-level TPA based on the blocked force method. An important point is to ensure convergence of measured test results (e.g. sound pressure at driver ear) and simulation results from component TPA. To conduct component-TPA, it is essential to have an independent tire model consisting of wheel-tire blocked force and tire Frequency Response Function (FRF), as well as full vehicle FRF and vehicle hub FRF. In this study, the FRF of the full vehicle and wheel-tire blocked force are obtained using an in-situ method with a
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