Browse Topic: Interior noise
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.
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
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.
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
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
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.
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.
This Aerospace Information Report (AIR) is limited in scope to the general consideration of environmental control system noise and its effect on occupant comfort. Additional information on the control of environmental control system noise may be found in 2.3 and in the documents referenced throughout the text. This document does not contain sufficient direction and detail to accomplish effective and complete acoustic designs.
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.
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
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
To meet vehicle interior noise targets and expectations, components including those related to electric vehicles (EVs) can effectively be treated at the source with an encapsulation approach, preventing acoustic and vibration sources from propagating through multiple paths into the vehicle interior. Encapsulation can be especially useful when dealing with tonal noise sources in EVs which are common for electrical components. These treatments involve materials that block noise and vibration at its source but add weight and cost to vehicles – optimization and ensuring the material used is minimized but efficient in reducing noise everywhere where it is applied is critically important. Testing is important to confirm source levels and verify performance of some proposed configurations, but ideal encapsulation treatments are complex and cannot be efficiently achieved by trial-and-error testing. Simulation is a key supporting tool to guide location, thickness, and properties of
In the acoustic study of the interior noise of a vehicle, whether for structure-borne or air-borne excitations, knowing which areas contribute the most to interior noise and therefore should be treated as a priority, is the main goal of the engineer in charge of the NVH. Very often these areas are numerous, located in different regions of the vehicle and contribute at different frequencies to the overall sound pressure level. This has led to the development of several “Panel Contribution Analysis” (PCA) experimental techniques. For example, a well-known technique is the masking technique, which consists of applying a “maximum package” (i.e., a package with very high sound insulation) to the panels outside of the area whose contribution must be measured. This technique is pragmatic but rather cumbersome to implement. In addition, it significantly modifies the dynamics and internal acoustics of the vehicle. In another well-known technique, the contribution of a certain area is defined
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
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
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
During the pure electric vehicle high speed cruise driving condition, the unsteady air flow in the chassis cavity is susceptible to self-sustaining oscillations phenomenon. And the aerodynamic oscillation excitation could be coupled with the cabin interior acoustic mode through the body pressure relief vent, the low frequency booming noise may occur and seriously reduces the driving comfort. This paper systematically introduces the characteristics identification and the troubleshooting process of the low frequency aerodynamic noise case. Firstly, combined with the characteristics of the subjective jury evaluation and objective measurement, the acoustic wind tunnel test restores the cabin booming phenomenon. The specific test procedure is proposed to separate the noise excitation source. Secondly, according to the road test results, it is inferenced that the formation mechanism of low frequency noise is the self- sustaining oscillation with the underbody shedding vortex feedback
Electric vehicles (EV) are much quieter than IC engine powered vehicles due to less mechanical components and absence of combustion. The lower cabin noise in electric vehicles make customers sensitive to even small noise disturbances in vehicle. Road boom noise is one of such major concerns to which the customers are sensitive in electric vehicles. The test vehicle is a front wheel driven compact SUV powered by electric motor. On normal plain road, noise levels are acceptable but when the vehicle has been driven on coarse road, the boom noise is perceived, and the levels are objectionable. Multi reference Transfer Path Analysis (MTPA) is conducted to identify the path through which maximum forces are entering the body. Based on MTPA, modifications are proposed on the suspension bushes and the noise levels were assessed. Operational Deflection Shape (ODS) analysis is conducted on entire vehicle components like suspension links, sub frame, floor, roof, and doors to identify the
Customers expect more advanced features and comfort in electric vehicles. It is challenging for NVH engineers to reduce the vibration levels to a great extent in the vehicle without adding cost and weight. This paper focuses on reducing the tactile vibration in electric vehicle when AC is switched ON. Vibration levels were not acceptable and modulating in nature on the test vehicle. Electric compressor is used for cabin cooling and battery cooling in the vehicle. Compressor is connected to body with the help of isolators. Depending upon cooling load, the compressor operates between 1000 rpm and 8000 rpm. The 1st order vibration of compressor was dominant on tactile locations at all the compressor speeds. Vibration levels on steering wheel were improved by 10 dB on reducing the dynamic stiffness of isolators. To reduce the transfer of compressor vibration further, isolators are provided on HVAC line connection on body and mufflers are provided in suction and discharge line. With the
This paper focuses on reducing abnormal noise originating from suspension when driving on rough road at the speed of 20 kmph. The test vehicle is a front wheel driven monocoque SUV powered by four cylinder engine. Cabin noise levels are higher between 100 to 800 Hz when driven on rough road at 20 kmph. Vibration levels are measured on front and rear suspension components, front and rear subframe, subframe connections on body to identify the noise source locations. Since the noise levels are dominant only in certain rough patches at very narrow band of time, wavelet analysis is used for identification of frequency at which the problem exist. Based on wavelet analysis, it is identified that the vibration levels are dominant on front lower control arm (LCA). The dynamic stiffness of LCA bushes is reduced by ~ 40% to improve the isolator performance which reduced the noise levels by ~ 9 dB (A) at the problematic frequency band. Modal analysis is conducted on front suspension components to
In this study, a novel assessment approach of in-vehicle speech intelligibility is presented using psychometric curves. Speech recognition performance scores were modeled at an individual listener level for a set of speech recognition data previously collected under a variety of in-vehicle listening scenarios. The model coupled an objective metric of binaural speech intelligibility (i.e., the acoustic factors) with a psychometric curve indicating the listener’s speech recognition efficiency (i.e., the listener factors). In separate analyses, two objective metrics were used with one designed to capture spatial release from masking and the other designed to capture binaural loudness. The proposed approach is in contrast to the traditional approach of relying on the speech recognition threshold, the speech level at 50% recognition performance averaged across listeners, as the metric for in-vehicle speech intelligibility. Results from the presented analyses suggest the importance of
Startups are famous for moving quickly. Vinfast may want to slow things down. It was only 2019 when the Vietnamese company built its first cars, rebodied versions of gasoline BMWs that became hits in its home market. Vinfast speedily developed four electric SUVs, including the inaugural VF8 that SAE Media drove in southern California. At the same time, a cargo ship docked near San Francisco, carrying nearly 2,000 VF8s for customers in California and Canada. The next day, Vinfast announced plans to go public via a SPAC merger. And Vinfast recently broke ground on a $4 billion factory in North Carolina, targeting 150,000 units of annual capacity and more than 7,000 jobs.
Traditionally vehicles are designed for wind noise under ideal steady wind conditions. But, passenger comfort is affected by high modulation of cabin noise while cruising in traffic due to variations of instantaneous wind speed and direction from driving through large-scale turbulence. In consequence, designing a vehicle for the best performance in a low-turbulence wind tunnel may lead to issues during on-road conditions. To predict the interior noise corresponding to on-road turbulence, a simulation approach is proposed combining an upstream turbulence flow simulation with an SEA vehicle model. This work is an extension of existing well validated procedures for steady wind conditions. Time-segmented transient loads on panels and steady-state structural acoustics transfer functions are combined, producing interior noise results for a series of overlapping time segments. This interior noise prediction, as a function of time, captures the modulation of wind noise results, which are then
Vehicle weight reduction is important to improve the fuel mileage of Internal Combustion Engine (ICE) vehicles and to extend the range of Electric Vehicles (EVs). Glass Fiber Reinforced (GFR) Composite (Polyamide) brackets provide significant weight reductions at a competitive part price. Traditionally, metal brackets are designed to surpass a target natural frequency and static stiffness. Composite brackets are inherently less stiff and have lower natural frequencies. However, composite brackets also have higher material damping than metal brackets, and good isolation performance can be achieved. The key to integrating composite brackets into the vehicle design is to perform adequate analysis to ensure that the noise and vibration performance at the vehicle level meets expectations. In this paper, case studies are presented for two different vehicles – a Clevis bracket for an IC Engine vehicle, and an electric motor mount bracket. For each case, measurement data is used to develop
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