The Vehicle Noise Control Engineering Academy covers a variety of vehicle noise control engineering principles and practices. There are two concurrent, specialty tracks (with some common sessions): Vehicle Interior Noise and Powertrain Noise. Participants should choose and register for the appropriate track they wish to attend. The Vehicle Interior Noise track focuses on understanding the characteristics of noise produced by different propulsion systems, including internal combustion, hybrid and electric powered vehicles and how these noises affect the sound quality of a vehicle’s interior.
The Vehicle Noise Control Engineering Academy covers a variety of vehicle noise control engineering principles and practices. There are two concurrent, specialty tracks (with some common sessions): Powertrain Noise and Vehicle Interior Noise. Participants should choose and register for the appropriate Academy they wish to attend. The Powertrain Noise track focuses on noise and vibration control issues associated with internal combustion, hybrid and electric powered vehicles. The vehicle in this case includes passenger cars, SUVs, light trucks, off-highway vehicles, and heavy trucks.
Due to their remarkable efficiency and efficacy, chevrons have emerged as a prominent subject of investigation within the Aviation Industry, primarily aimed at mitigating aircraft noise levels and achieving a quieter airborne experience. Extensive research has identified the engine as the primary source of noise in aircraft, prompting the implementation of chevrons within the engine nozzle. These chevrons function by inducing streamwise vortices into the shear layer, thereby augmenting the mixing process and resulting in a noteworthy reduction of low-frequency noise emissions. Our paper aims to conduct a comparative computational analysis encompassing seven distinct chevron designs and a design without chevrons. The size and configuration of the chevrons with the jet engine nacelle were designed to match the nozzle diameter of 100.48mm and 56.76mm, utilizing the advanced SolidWorks CAD modeling software.
As a key component of in-vehicle intelligent voice technology, speech enhancement can extract clean speech signals contaminated by environmental noise to improve the quality and intelligibility of speech perception. It has broad applications in human-vehicle interaction, in-car communication, and in-car cinema. Although some end-to-end time-domain-based speech enhancement methods have been proposed, few models are designed based on the characteristics of speech signals. In this paper, we propose a new U-Net based speech enhancement method that utilizes the temporal correlation of speech signals to reconstruct higher-quality and more intelligible clean speech.
As the mobility becomes complex, the coopertion of test and simulation is more and more important. For several years, various technologies for hybrid methods of test and simulation, such as VPT, FBS decoupling, modal model, SEMM and auralization have been studied and developed to help virtual development activities. As a result, the model based roadnoise development process and dedicated softwares have been developed. In this study, the system models such as tire blocked force, suspension FRFs and body FRFs were made by experiment and simulation and assembled to predict vehicle’s roadnoise. As a objective evaluation, roadnoise was analysed and the systematic TPA and sensitivity analysis process and program were development to set reasonable system targets to meet vehicle target. Then, as a subjective evaluation, roadnoise were auralized for various driving conditions such as speeds and road types according to driver’s input.
In vehicle development, reducing noise is a major concern to ensure passenger comfort. As electric vehicles become more common and engine and vibration noises improve, the aerodynamic noise generated around the vehicle becomes relatively more noticeable. In particular, the fluctuating wind noise, which is affected by turbulence in the atmosphere, gusts of wind, and wake caused by the vehicle in front, can make passengers feel uncomfortable. However, the cause of the fluctuating wind noise has not been fully understood, and a solution has not yet been found. The reason for this is that fluctuating wind noise cannot be quantitatively evaluated using common noise evaluation methods such as FFT and STFT. In addition, previous studies have relied on road tests, which do not provide reproducible conditions due to changing atmospheric conditions. To address this issue, automobile manufacturers are developing devices to generate turbulence in wind tunnels.
In recent years, with the development of computing infrastructure and methods, the potential of numerical methods to reasonably predict aerodynamic noise in compressors has increased. However, aerodynamic acoustic modeling of complex geometries and flow systems is currently immature, mainly due to the greater challenges in accurately characterizing turbulent viscous flows. Therefore, recent advances in aerodynamic noise calculations for automotive turbocharger compressors were reviewed and a quantitative study of the effects for turbulence modeling (Shear-Stress Transport (SST) and Detached Eddy Simulation (DES)) and time-steps (2°and 4°) in numerical simulations on the performance and acoustic prediction of a compressor under full operating conditions was investigated. The results showed that for the compressor performance, the turbulence models and time-step parameters selection were within 1.5% error of the simulated and measured values for pressure ratio and efficiency.
In the process of automobile industrialization, integrated electric drive systems turn to be the mainstream transmission system of electric vehicles gradually. The main sources of noise and vibration in the chassis are from the gear reducer and motor system, as a replacement of engine. For improving the electric vehicles NVH performance, effective identification and quantitative analysis of the main noise sources are a significant basis. Based on the rotating hub test platform in the semi-anechoic chamber, in this experiment, an electric vehicle equipped with a three-in-one electric drive system is taken as the research object. As well the noise and vibration signals in the interior vehicle and the near field of the electric drive system are collected under the operating conditions of uniform speed, acceleration speed, and coasting with gears under different loads, and the test results are processed and analyzed by using the spectral analysis and order analysis theories.
This paper analyzes the mechanism of vibrational energy propagation and panel vibration generation at the point joints between frame and panel which can be applied to reduce the vehicle interior noise. In this study, we focused on the traveling wave in the early stage of propagation before the mode is formed, and investigated the mechanism of panel vibration generation due to wave energy propagation and its reduction method. First, we show theoretically that the out-of-plane component of the transmitted power at the point junction between frame and panel that contributes to panel vibration is associated with frame deformation. Then, we show through numerical verification that panel vibration can be reduced by reducing the transferred power of the out-of-plane component, and explain the effectiveness of the frame-to-panel joint design guidelines based on energy propagation analysis. Next, This analysis technique is applied to the vehicle body model.
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 valve, 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.
The noise tests of electric motor systems serve as the foundation for studying their acoustic issues. However, there is currently a lack of corresponding method for identifying key noise characteristics, such as the noise frequency range that needs to be considered, the significant noise orders, and the resonance bands worth paying attention to, based on experimental test data under actual operating conditions. This article proposes a method for identifying the noise characteristics of an electric motor system based on tests conducted under distinct operating conditions, which consists of three parts: identifying the primary frequency range, the significant orders, and the important resonance bands. Firstly, in order to extract noise with the primary energy, the concept of noise contribution is introduced. The primary frequency range and the significant orders under a specific operating condition can be obtained after extraction.
LiDAR sensors play an important role in the perception stack of modern autonomous driving systems. Adverse weather conditions such as rain, fog and dust, as well as some (occasional) LiDAR hardware fault may cause the LiDAR to produce pointcloud with abnormal patterns such as scattered noise points and uncommon intensity values. In this paper, we propose a novel approach to detect whether a LiDAR is generating anomalous pointcloud by analyzing the pointcloud characteristics. Specifically, we develop a pointcloud quality metric based on the LiDAR points' spatial and intensity distribution to characterize the noise level of the pointcloud, which relies on pure mathematical analysis and does not require any labeling or training as learning-based methods do. Therefore, the method is scalable and can be quickly deployed either online to improve the autonomy safety by monitoring anomalies in the LiDAR data or offline to perform in-depth study of the LiDAR behavior over large amount of data.