Browse Topic: Interior noise
In the automotive industry, controlling noise transmission through vehicle components is essential for passenger comfort and regulatory compliance. Traditionally, Transmission Loss (TL) is estimated using simplified CAD-based metrics, which lack accuracy at high frequencies and for complex assemblies. Modeling complex vehicle components introduces challenges, such as representing fluid-structure and trim interactions, with spatially varying trim thicknesses. This study presents an industrial application implementing the Virtual SEA (Statistical Energy Analysis) method to evaluate TL for a firewall. The study discusses strategies for subsystem adaptation and analytical trim modeling, highlighting the importance of managing spatial averaging effects. The proposed workflow integrates laboratory measurements of trim materials, advanced subsystem definition, diffuse sound field (DSF) excitation and radiation in free-field condition. Virtual SEA results are systematically validated against Finite Element Method (FEM) simulations (where the frequency range allows) and experimental data. Virtual SEA demonstrates strong agreement with FEM, especially at mid and high frequencies where FE starts to be cumbersome, confirming its suitability for industrial Noise, Vibration, and Harshness (NVH) applications. While some limitations remain—such as the inability to fully model mixed-component subsystems—ongoing research and practical workarounds are proposed. In conclusion, the Virtual SEA approach enables accurate and efficient TL prediction for vehicle components up to higher frequencies that FEM can achieve, supporting NVH targets and facilitating knowledge transfer to engineering teams. This work advances simulation-based acoustic transparency analysis for modern automotive design.
Vehicle electrification and accelerated development cycles create a need for virtual Noise, Vibration and Harshness (NVH) development tools which are fast, precise and, seamlessly interchangeable between development sites, suppliers and OEMs. Component-based Transfer Path Analysis (C-TPA), standardized in ISO 20270:2019, enables independent component characterization and integration with virtual models to predict sound and vibration in new assemblies, referred to as Virtual Prototype Assemblies (VPA). However, conventional measurements are labor-intensive, typically restricted to a small number of samples, and overlook production variability. This paper introduces a fully automated, ISO 20270-compliant C-TPA system for non-rigid test benches, featuring a pre-instrumented test fixture with multiple vibration shakers and sensors automatically linked to a data acquisition system for immediate processing. Components can be characterized within minutes, with blocked forces directly integrated into a VPA workflow, replacing time-intensive in-vehicle testing with a repeatable, operator-independent bench procedure. A case study on an automotive steering system demonstrates the method’s accuracy, repeatability, and efficiency, along with its ability to predict realistic interior sound pressure levels and capture production variability, enabling robust virtual NVH evaluation early in the development cycle.
Recent studies indicate that the door system plays a significant role in the interior noise levels of newly developed vehicles. This research investigates the noise transmission paths through the door system and identifies effective strategies for improvement through a combination of door buck testing and simulation. Specifically, in this study, the finite element method (FEM) was employed for door buck simulation, and the model was validated against vibration test results. Subsequently, acoustic analysis tools were utilized to correlate with noise testing, thereby establishing a process to ensure simulation accuracy. The sound insulation performance for the main areas of the door was experimentally evaluated, and a simulation model with good correlation to these test results was developed. By utilizing both experimental and simulation results, the principal transmission paths were identified, and appropriate improvement strategies for these paths were investigated. The validated improvement strategies are intended to be applied in the development of next-generation vehicles.
In November 2024, Blue Ridge Research and Consulting and Archer Aviation performed acoustic flight tests of the pre-production version of Midnight, Archer Aviation’s full-scale, multirotor electric vertical takeoff and landing (eVTOL) aircraft. The flight tests included concurrent community noise and cabin noise measurements of Midnight across a range of flight conditions. This paper describes the flight test design, measurement instrumentation, and empirical analysis methods used to assess steadiness and repeatability, develop acoustic hemispheres, and identify aeroacoustic sources on Midnight. The acoustic measurements reveal that tonal noise from the propellers is dominant during hover, broadband noise from the propellers and airframe is dominant during cruise, and both tonal and broadband noise components are important during transition. The geometric arrangement of Midnight's propellers influences the acoustic directivity. Source separation using the Vold-Kalman filter reveals that the rear propellers produce higher tonal sound levels than the forward propellers, but broadband noise is the dominant contributor to the overall sound level in forward flight. The paper concludes with lessons learned and recommendations for future acoustic flight tests.
A computational study using the Volume of Fluid (VOF) method in SimericsMP+ was conducted to investigate fuel sloshing in automotive fuel tanks under both crash and sudden stop conditions. The SEALs method was employed to rapidly generate the fuel tank mesh, enabling efficient simulation setup. At the outset, a benchmark sloshing case was simulated and compared against experimental data, showing excellent agreement to validate the simulation method. This simulation method was then applied to the fuel tank sloshing scenarios mimicking crash and sudden stop conditions. The study initially focused on a crash scenario in which fuel waves impact valves, pumps, and other internal structures. Capturing these localized impact forces is critical for evaluating the risk of component failure and potential leakage. A baffle-equipped tank was simulated and compared with sensor data. Results show that the computed shock forces on valves and baffles closely matched the measurements, demonstrating the high accuracy of the CFD method in predicting crash safety performance and confirming the effectiveness of baffles in reducing fuel wave impacts. The validated framework was then applied to four new unbaffled tank designs to assess NVH performance during low-speed sudden stop maneuvers. Pressure fluctuations on tank walls, which are directly linked to cabin noise, were analyzed and compared against reference pressure measurements from physical tests to ensure compliance with NVH standards. Simulations revealed significant pressure peaks in certain designs, indicating sub-optimal acoustic performance and highlighting how the absence of internal columns or baffles amplifies wave propagation and surface loading. The computational strategy presented in this study provides a powerful tool for evaluating both crash safety and NVH behavior early in the design process. By delivering accurate predictions before physical prototypes are built, it helps guide fuel tank design development, reduces reliance on costly testing, and minimizes the risk of late-stage design failures.
Passenger expectations for quiet and acoustically comfortable vehicle interiors have increased significantly, driven by advancements in electric vehicles and premium audio systems. Acoustic comfort affects perceived quality, communication ease, and overall driving experience. This paper presents a simulation-driven methodology to predict and optimize interior noise performance during the early design phase, focusing on high-frequency acoustic transfer functions and trim material absorption properties. Traditional NVH development relies heavily on physical testing, which is time-consuming and costly. Early-stage predictive tools are essential to evaluate acoustic performance before prototype availability. High-frequency noise (1kHz–12kHz) is particularly challenging due to complex reflections and absorption behavior. Acoustic trims play a critical role in shaping the cabin’s sound field, and their properties must be optimized to achieve desired sound quality. A novel simulation approach is developed using Raytracing (Beam + Particle) to model sound propagation within the vehicle cabin. The method calculates ATFs between point sources (e.g., door panels) and receiver positions (passenger ears), enabling spatially resolved acoustic analysis. This supports early design evaluations by predicting how changes in geometry and materials affect perceived noise levels. Using HEEDS, a DOE-based optimization is performed on frequency-dependent absorption properties of acoustic trims. The trim package includes carpet, headliner, seats, doors, and firewall. The optimization targets mid-to-high frequency ranges where material behavior significantly influences sound quality. Multiple design iterations are evaluated to identify configurations that minimize intrusive noise and enhance tonal balance. A full-vehicle correlation study is conducted to validate the simulation results. Measured ATFs from a physical prototype are compared with simulated data. The acoustic trim package used in the prototype includes all major components. The Raytracing-based ATF model shows strong correlation with measured data. The methodology enables early identification of design choices that degrade or enhance acoustic comfort.
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 structure panels were identified and thereafter structural enablers were employed to attenuate the booming phenomenon. The approach shown here has the potential to identify and optimize BEV noise in a more comprehensive and effective way.
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 to calculate interior noise and evaluate the AVAS audibility inside the cabin. This approach has the advantage of overcoming the limitations of SEA in simulating exterior paths, which would otherwise require creating exterior cavities, overriding certain properties of connected cavities, and modifying coupling loss factors to emulate diffraction. In addition, simple auralization of the interior noise due to AVAS is presented for different sound package configurations.
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. These findings can help tyre development engineers devise more effective and quicker noise reduction strategies for EVs with minimal compromise on other tyre performance aspects.
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 vibration levels are reduced by ~ 40%. Sound quality parameter which needs to be considered during the horn selection is explained. The modal criteria which must be taken into account during development phase to avoid the horn cracking noise and tactile vibration is also proposed.
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 simulation model designed to investigate vehicle interior noise & Articulation index and associated correlation against test measurements for various real- world operating scenarios. The SEA simulation model was meticulously developed with close attention given to structural representation which allowed to consider the structure borne excitations along with air borne noise sources during the analysis. The interior trims & sound insulation pack were also in detailed in the model. Both static & dynamic real-world operating scenarios of vehicle or load cases are demonstrated to validate the model against test measurements. The contribution study was performed to determine dominant noise sources and weaker transfer paths for improvement of Articulation index & interior noise quality of vehicle.
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.
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 and full vehicle has been built using the software FE Codes. Noise Transfer Function (NTF) and Panel contribution analysis (PCA) and sensitivity study has been executed to identify the critical panels, systems and assemblies. Once critical panels are identified, machine learning based NTF Optimization has been executed at the cabin level to minimize the NTF levels at the early stage of design. Model finalized based on the machine learning has been integrated with the full vehicle level model. Lastly the structure borne noise has been predicted at the operator ear level using the derived loads across the bearings as the input. Predicted NTF and structure borne noise levels has been compared with the physical measurement data to ensure good correlation. With the design modifications on the identified parts and assemblies based on the sensitivity study lower structure borne noise levels has been achieved in the virtual environment, which assists to shorten the lead time.
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 root cause analysis was conducted, including Transfer Path Analysis (TPA), evaluation of compressor fixation points stiffness, and dynamic noise signature analysis. The investigation revealed that the metallic noise was a combination of moaning and whining sounds, primarily caused by internal excitation forces within the compressor. These forces generated dominant excitations at high operating pressures, resulting in the observed tonal noise. Several countermeasures were explored, including changes to the compressor pulley ratio to decouple engine firing frequency excitations, modifications to the AC pipe bends to reduce excitation forces, and optimization of acoustic mass and compressor mounting stiffness. Collaboration work has been done with the supplier focused on fine-tuning the Mass Flow Control Valve (MFCV) settings [6] and adjusting compressor shaft tolerance. The most effective solutions were the compressor pulley ratio change and the modification of the compressor’s planetary plate angle, which together achieved an improvement of approximately 6 dB(A) in compressor order noise, significantly reducing customer-perceived annoyance. As a result, key NVH (Noise, Vibration, and Harshness) design rules have been established and implemented
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 instead of changing the blower speed. This approach helped eliminate resonance at that operating point, reducing noise in the cabin. By applying the Campbell diagram tool, HVAC noise can be minimized, resulting in a quieter cabin and an improved driving experience.
Carbon/epoxy stiffened panels are being increasingly used in transport rotorcraft. The reduced mass density and high stiffness of carbon/epoxy composites can lead to higher levels of vibration relative to comparable metallic structures, which themselves can have vibrations and interior noise high enough to damage the hearing of crew and passengers. The current investigation explores a method to reduce the vibration of carbon/epoxy stiffened panels by introducing thickness tapers known as acoustic black holes (ABHs). The ABH feature is integrated into either the stiffeners or plate of a representative stiffened panel configuration. A finite element (FE) parametric study was used to guide designs that reduce the vibration of the panel without compromising the compressive buckling capability or mass of the panel. FE studies showed that a 30 ply to 12 ply thickness taper longitudinally oriented in the blade stiffener can reduce vibrations and increase compressive buckling capability. Carbon/epoxy panels were manufactured using a low-cost out-of-autoclave material with simple molding. Experimental testing concluded that integrating the ABH into the stiffeners longitudinally helped to reduce the broadband vibration by 5 dB and increase the buckling load (+4.3%) and collapse load (+16.5%) without increasing the mass greatly compared to a traditional baseline design.
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 accurately reflect real-world impressions? This paper compares virtual test drive results with road test results and explores to what extent this unique method can be utilized to improve the EVSE tuning process.
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. The external fluctuating pressure fields are simulated using computational fluid dynamics based on the Lattice Boltzmann Method (LBM). The vehicle exterior and interior vibro-acoustic coupling and transmission are simulated using subsystems modeled by the finite-element (FEM), boundary element methods (BEM) and statistical energy analysis (SEA). The analysis results are discussed, and a contribution analysis is proposed to identify potential improvements.
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 limitations. Considering the testing car, the main noise source of exterior is from the wheelhouse region, then follows the rearview mirror with much lower sound energy. However, noise from the mirror contributes most to the vehicle interior, while the contribution from wheelhouse region ranks the second place. In addition, windshield wipers and door handle can do perceptible contributions to vehicle interior noise at some characteristic frequency bands.
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.
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 restored when no signs of performance degradation were present after 30 simulated years of normal loading conditions at extreme temperatures.
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.
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 source identification. The surface vibration pattern over the surface of the virtual speaker is first extracted based on measured sound pressure data. The acoustic response in a free field generated by the virtual speaker is validated by comparing the sound pressure level from direct measurements and from numerical prediction. The validated virtual speaker with vibration pattern is then applied in a full vehicle model to predict interior sound field. Investigated the interior noise due to speaker with its directivity considered. Inverse numerical acoustics used to retrieve the surface normal velocities on the acoustic model. The technique allows to back calculate the operational vibrations based on operational near field pressure measurements. Near field pressure measurements are required to capture all acoustic waves (radiated waves + evanescent waves). More mid-field pressure measurements were taken to verify the correctness of the suggested method. A good agreement is discovered when the measurements are compared to the re-computed field.
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 doors at the problematic frequency. Based on ODS, the components having higher deflections at the problematic frequency is identified. Modifications are proposed to improve the dynamic stiffness of the structure at the problematic frequency and the contribution of each modification for cabin noise reduction is discussed. Solutions like tuned mass dampers (TMD) on suspension components are explored and the critical parameter which should be considered to get maximum reduction in noise with TMD is also discussed. With all the modifications, the noise levels are reduced by 5 dB (A) at problematic frequency. NVH criteria which should be considered related to suspension system to avoid boom noise concern in electric vehicle is also discussed.
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.
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