Browse Topic: Noise measurement
In vehicles with electrified powertrains, high-frequency tonal noise components have become increasingly prominent and can be perceived as particularly annoying by the driver. While recent advancements in international standardization — such as ECMA-74 [1] and ECMA-418 [2] — have led to powerful new algorithms for tonal noise visualization and analysis, including Tonality-Heatmaps, the measurement side still lacks sensor setups that adequately reflect the spatial sensitivity of noise, especially for tonal components. This challenge is amplified in enclosed vehicle cabins, where room modes create local minima and maxima that become increasingly dense at higher frequencies. As a result, even small head movements can lead to noticeable differences in perceived tonal noise. Current measurement approaches do not sufficiently account for this spatial variability. This contribution addresses the absence of tailored solutions for the driver’s position by introducing an improved microphone arrangement that significantly reduces the uncertainty of measured noise levels. The proposed setup considers spatial variability without compromising comfort or crash safety requirements. By enhancing the precision of tonal noise quantification, this approach provides noise-vibration-harshness (NVH) engineers with a valuable complement to modern software-based tonal analysis methods. The paper discusses the technical implementation constraints and demonstrates the comparability of the new measurement technique with conventional setups.
Tire exterior noise has become increasingly critical in vehicle acoustics due to two key developments: updated pass-by noise regulations, which amplify the relative contribution of tire noise, and the rise of Battery Electric Vehicles (BEVs), which lack traditional powertrain noise. Design trends in BEVs—such as increased vehicle mass from battery packs and the widespread use of large-diameter, wide, low-profile tires—further intensify tire noise due to stiffer constructions and altered contact dynamics. A common method for predicting tire noise is the source-transfer-receiver model, where the tire is represented by a set of monopoles with volume velocity Q derived from near-field measurements. Acoustic propagation is modeled via p/Q transfer functions. Despite its simplifications, this approach is practical for vehicle development, enabling clear separation between source and transfer mechanisms and facilitating targeted noise control strategies. In previous work, we proposed a rigorous framework to optimize both the spatial distribution and strength of the monopole sources. Positions were identified using an L1-norm regularization via the Lasso algorithm, promoting sparsity and physical interpretability. Strengths were estimated using an L2-norm Tikhonov regularization, which stabilizes the solution against measurement noise. While the Tikhonov regularization parameter was previously tuned manually through trial and error, we now enhance predictive accuracy by selecting it via a cross-validation technique, ensuring a more robust and data-driven optimization. Besides this, compared to the previous work the approach here is validated for the prediction of both indoor and outdoor pass-by noise, as well as for multiple tire types providing different noise levels. Results demonstrate the method’s robustness, accuracy, and applicability for acoustic development in modern vehicle platforms.
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.
This study experimentally examines the effect of forced boundary layer (BL) transition on the aerodynamic and aero-acoustic performance of a low Reynolds number rotor in hover. An APC 15×4E two-bladed rotor was tested in three configurations: clean, upper-surface trip (U.S.T.), and combined upper- and lower-surface trip (U.S.T./L.S.T.). Surface oil flow visualization was used to characterize the BL structure. A hover test rig was used to measure the static thrust and torque. Acoustic measurements were conducted in an anechoic chamber, with tonal and broadband noise components separated during post-processing. Results show that surface trips effectively force BL transition, increasing turbulent attachment over the blade. Tripped configurations reduced thrust and increased torque but mitigated Reynolds-number sensitivity. Forced transition reduced the tonal noise for all but one case. For the broadband noise, the forced transition increased the noise in the frequency range where turbulent boundary layer-trailing edge (TBLTE) mechanisms dominate, while decreasing the noise in the frequency range where laminar boundary layer vortex shedding (LBL-VS) occurs.
This study investigates the acoustic performance of a single rotor representative of those seen on multi-passenger UAM-sized vehicles, focusing on the effects of blade count, disk loading, solidity, and tip Mach number in both hover and propeller operating conditions. Using PSU-WOPWOP and ANOPP2, unweighted and A-weighted overall sound pressure levels (OASPL) are computed in-plane for 2- and 5-bladed rotors across a range of design parameters and operating conditions. Unweighted results show that reducing blade count significantly increases total noise levels (14.1 dB on average) and reduces sensitivity to design parameters. In contrast, A-weighted results demonstrate that broadband noise dominates perceived acoustic performance and shows a decreased sensitivity to blade count (1.9 dBA average difference). Minimum noise levels occur at tip Mach numbers ranging from 0.35-0.45 for unweighted results and 0.4-0.5 for A-weighted results, and are primarily governed by broadband noise sensitivity to disk loading and solidity. The rotor in propeller mode, with axial flow and reduced disk loading, showed less sensitivity to variation in disk loading and solidity than the rotor in hover, indicating weaker acoustic dependence in cruise conditions.
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.
This paper presents a study of gunshot acoustic signal detectability in the near field of propeller noise, with a focus on the isolation of external gunshot signatures masked by propeller-induced noise. Controlled measurements were conducted in a Recirculation Delayed Anechoic Chamber (RDAC), where acoustic data were collected across varying rotor speeds, source locations, and propagation distances. Propeller noise characteristics were verified using UCD-QuietFly. The recorded signals were analyzed for the acoustic pressure, sound pressure level, and overall sound pressure level directivity to quantify masking effects. Results show that RPM is the dominant factor governing signal detectability. At 3000 RPM, the gunshot signal remains clearly identifiable within the low frequency range of 200–2000 Hz. At 4000 RPM, the signal becomes partially masked, while at 5000 RPM, propeller noise fully dominates and the gunshot signal becomes undetectable. Detectability is further reduced with increasing propagation distance. In-plane microphone locations provide improved detectability. A machine learning-based spectral separation framework was developed to suppress propeller noise and enhance the visibility of impulsive gunshot signatures in multichannel spectrograms. Experimental results show that learning-based denoising is effective at lower RPMs where the signal-to-noise ratio remains favorable, but performance degrades as broadband masking intensifies at higher rotor speeds.
The Audio system is an important part of the design of a vehicle cabin. In the vehicle development process, the audio system needs to be tuned for optimal acoustic performance. Traditionally, this process is performed physically on vehicles. In this paper, a methodology is developed to numerically simulate the acoustic performance of the audio system across the full audible frequency range. To provide validation of the method, the p/v acoustic transfer functions (ie., the sound pressure p at the passengers’ ears divided by the voltage inputs v) are measured for different speakers in a production vehicle. As the sound perceived by the passengers depends on both the source and the path, the method development is split into two parts: (a) characterization of parameters that describe the loudspeaker as a source and (b) representation of the vehicle cabin as a path. The speaker parameters are characterized from sound radiation data measured in a 2pi chamber. To represent the vehicle cabin, a hybrid BEM-SEA model is utilized in which the cabin is fully deterministic below 1kHz and is statistical between 1 kHz and 20 kHz. The speaker model is then integrated into the cabin model in order to predict the acoustic transfer functions. This model accounts for two-way coupling between the speakers and the cabin. The results show that the predicted transfer functions are in very good agreement with the data from acoustic measurements. Therefore, performing the audio tuning virtually by numerical simulation is a feasible solution for the industry.
Modern aeroacoustic wind tunnels are required to have flat axial static pressure distribution, very low background noise levels, and minimal low-frequency pressure fluctuations. These characteristics enable accurate measurement of aerodynamic forces acting on a vehicle as well as identification of noise sources. The collector of an open-jet or ¾ open-jet wind tunnel plays a critical role in achieving these goals. Collector self-generated noise contributes to the overall background noise level in the test section, and this contribution has become more significant as other noise sources, such as the main fan, have been addressed through improvements to acoustic treatment. Ever-increasing attention to detail is required to manage noise signatures as the overall facility noise floor is lowered. Furthermore, aspects of collector design that may be beneficial to aerodynamics or pressure fluctuation tend to be some of the worst offenders for noise generation. A new collector configuration was designed during construction of the Honda Automotive Laboratories of Ohio (HALO) Wind Tunnel. The collector design balances functional requirements for aerodynamics and acoustics, with development work making use of modern computational fluid dynamics techniques and sub-scale laboratory testing. The resulting collector design enabled a flat axial static pressure distribution, low background noise levels and helped minimize low-frequency pressure fluctuations. A previous paper describes the HALO wind tunnel’s overall features and commissioning results. This paper focuses specifically on the challenges, engineering approach, and trade-offs that went into the collector design.
This study examines the capability of medium-fidelity comprehensive analysis models to predict the acoustics for manned and unmanned rotorcraft configurations. Using the automated tool NDARC2RCAS developed at DEVCOM Army Research Laboratory, multiple configurations including a single main rotor, tilt rotor, coaxial and pusher, quadcopter, and hexacopter are evaluated at various mission segments including hover, advancing climb, and forward flight. Each configuration and condition is evaluated using a range of aerodynamic models from lower to higher fidelity, including uniform inflow, dynamic inflow, prescribed wake, free wake, and viscous vortex particle method (VVPM). These evaluations are then used with another automated tool, RCAS Acoustics, to predict noise on a Voronoi observer sphere. A comparison of the results for the single main showed good agreement between all of the aerodynamic models except VVPM. For the tilt rotor in forward flight, the higher-fidelity models produced changes in rotor loads due to the interaction with the wing. With prescribed and free wake models, this change in load is sharp and causes noise increases of up to 40 dB in front of and behind the vehicle, while the VVPM model produced a smoother change that results in a smaller, 20 dB increase in noise. The quadcopter and hexacopter show similar in-plane noise levels for all models, with alternating cancellation and amplification patterns due to rotor phasing, while out-of-plane noise is increased on the hexacopter when using the higher fidelity models.
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.
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.
Tire noise reduction is important for improving ride comfort, especially in electric vehicle due to lack of engine noise and majority of the noise generated in-cabin is from tire-road interaction. Therefore, the tire tread pattern contribution is one of the important criteria for NVH performance apart from other structurally generated noise and vibration. In this work a GUI-based pitch sequence optimization tool is developed to support tire design engineers in generating acoustically optimized tread sequences. The tool operates in two modes: without constraints, where the pitch sequence is optimized freely to reduce tonal noise levels; and with constraints, where specific design rules are applied to preserve pattern consistency and manufacturability. The key point to be considered in this pitch sequence is that it should be reducing the tonal sound and equally spread i.e., the same pitch cannot be concentrated on one side which may lead to non-uniformity. So, the restriction is that the highest and lowest pitch types cannot occur adjacent to one another. This design rule helps in reducing undesirable pattern non-uniformity and improves both acoustic and structural performance. This tool helps in faster design iteration and integration with downstream development processes. This tool is also validated in current OE projects showing promising improvements in tire noise behavior while maintaining realistic design feasibility.
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 activation of the fuel injector affects both engine performance and pollutant emissions. However, the automotive industry restricts access to information regarding the circuits and control strategies used in its vehicles. One way to optimize fuel injections is using piezoelectric injectors. These injectors utilize crystals that expand or contract when subjected to an electric current, moving the injector needle. They offer a response time up to four times faster than solenoid-type injectors and allow for multiple injections per combustion cycle. These characteristics result in higher combustion efficiency, reduced emissions, and lower noise levels, making piezoelectric injectors widely used in next-generation engines, where stricter emission and efficiency standards are required. This study aims to design a drive circuit for piezoelectric injectors in a common rail system, intended for use in a diesel injector test bench. Experimental measurement of voltage was obtained from an injector coupled to a running diesel engine. The developed equivalent circuit demonstrated the capability to drive piezoelectric injectors with voltage values close to those observed in a commercial injector installed in a diesel engine, validating its suitability for research and experimental applications. Additionally, injector operating curves were generated, evaluating the injected diesel mass flow rate for different energization times and injection pressure. The designed equivalent circuit successfully enabled the correct operation of piezoelectric injectors on the test bench, reproducing the expected charge and discharge behavior required for precise actuation.
We present a novel processing approach to extract a ship traffic flow framework in order to cope with problems such as large volume, high noise levels and complexity spatio-temporal nature of AIS data. We preprocess AIS data using covariance matrix-based abnormal data filtering, develop improved Douglas-Peucker (DP) algorithm for multi-granularity trajectory compression, identify navigation hotspots and intersections using density-based spatial clustering and visualize chart overlays using Mercator projection. In experiments with AIS data from the Laotieshan waters in the Bohai Bay, we achieve compression rate up to 97% while maintaining a key trajectory feature retention error less than 0.15 nautical miles. We identify critical areas such as waterway intersections and generate traffic flow heatmap for maritime management, route planning, etc.
The operator station or “cab” in off Highway equipment plays a critical role to provide a comfortable workspace for the operator. The cab interfaces with several elements of the off-highway equipment which can create gaps and openings. These openings have the potential for acoustic energy leakage, ultimately increasing sound within the cab. During machine operation, noise generated around the cab conducts inside through these leakages resulting in increased sound levels. Acoustic leakages are among the key noise transfer paths responsible for noise inside the cab. Therefore, before considering noise control treatments it is best to first identify and minimize any leakages from joints, corners, and pass-throughs to achieve the required cab noise reduction. In this effort the sound intensity technique is used to detect the acoustic leakages in cab. The commercial test system is used for measuring the sound intensity field over objects. For the cab, an acoustic source is used inside the cab as a known energy source and the intensity levels are measured outside the cab. The test is conducted in a semi-anechoic chamber. Each external surface of the cab was scanned with a Sound-intensity probe and the leakages on each surface are detected individually. The results from this test will be used to reduce the leakages, eventually reducing the noise level inside the machine operator station.
To address the growing concern of increasing noise levels in urban areas, modern automotive vehicles need improved engineering solutions. The need for automotive vehicles to have a low acoustic signature is further emphasized by local regulatory requirements, such as the EU's regulation 540/2014, which sets sound level limits for commercial vehicles at 82 dB(A). Moreover, external noise can propagate inside the cabin, reducing the overall comfort of the driver, which can have adverse impact on the driving behavior, making it imperative to mitigate the high noise levels. This study explores the phenomenon of change in acoustic behavior of external tonal noise with minor geometrical changes to the A-pillar turning vane (APTV), identified as the source for the tonal noise generation. An incompressible transient approach with one way coupled Acoustics Wave solver was evaluated, for both the baseline and variant geometries. Comparison of CFD results between baseline and variant showed spectral broadening of critical tone in variant case. Impact of various other simulation parameters like turbulence intensity, turbulence length scale, time-step size and sampling time, on the critical tonal frequency, was also evaluated. Reduction in time step had a significant impact on the acoustic behavior of the APTVs due to spectral broadening & reduction of tonality. Whereas turbulence intensity is observed to have a significant effect on the frequency of the critical tone, the effect of other simulation parameters was not significant. Coherent vortex shedding from the APTV is identified to be the underlying source of the noise, exhibiting a dipole acoustic behavior. Geometric modification to the leading edge of the APTV is observed to reduce the tonal amplitude due to reduced coherence of vortex shedding and weak vortex core. The current method is able to predict the change in acoustic behavior due to geometric modifications for a particular yaw angle, further studies are ongoing to improve accuracy for full yaw sweep.
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.
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