Reliability improvement of a sound quality index for a vehicle HVAC system using a regression and neural network model

The reduction of vehicle interior noise has long been the main interest of noise and vibration harshness (NVH) engineers. A driver’s perception of vehicle noise is largely affected by psychoacoustic noise characteristics and SPL. Among the various types of vehicle interior noise, the sound of the heating, ventilation, and air conditioning (HVAC) systems is a source of distraction for drivers. HVAC noise is not as loud as the overall noise level; however, it affects a driver’s subjective perception and may lead to feelings of nervousness or annoyance. Therefore, vehicle engineers work not only to reduce noise, but also to improve sound quality. In this paper, HVAC noise samples were taken from many types of vehicles. Objective and subjective sound quality (SQ) evaluations were obtained, simple and multiple regression models were generated, and these were used with the Semantic Differential Method (SDM) to determine what characteristics trigger a “pleasant” response from listeners. The regression analysis produced diagnostic statistics and regression estimates. In addition, neural network (NN) models were created using three objective numerical inputs (loudness, sharpness, and roughness) of the SQ metrics and one subjective output (“pleasant”). The NN model was used primarily because human perceptions are very complex and often hard to estimate. The estimation models were compared via correlations between SQ output indices and hearing test results. Results demonstrated that the NN model is most highly correlated with SQ indices, which led to determination of suggested methods for SQ metrics prediction.     

Research on the procedure for analyzing the sound quality contribution of sound sources and its application

Transfer path analysis (TPA) plays an important role for identifying and quantifying the contribution by airborne and structure-borne in the automotive industry. The main bottleneck of the TPA method is the test time consumption and complex procedure. It becomes a key target in many applications to find out the source with dominant contribution to overall noise rather than to identify each source. In recent years the contribution pattern of sources to the vehicle overall interior noise has changed with the reduction of engine noise, which masks all other sources. The panel radiation noise of vehicle body could not be ignored. There is an increasing demand for analyzing the sound quality contribution of sound sources in simple ways. The procedure for analyzing sound quality contribution of panel radiation noise is suggested in this study, in which an operational path analysis (OPA) method combined with partial singular value decomposition (PSVD) analysis is applied and sound quality objective assessment is introduced. The experimental research for verifying the procedure is finished, from which the source with largest sound quality contribution is picked up from three sources. For engineering application, the sound quality contributions of panels to the interior noise of a micro commercial vehicle are analyzed by using the procedure. By investigating the contributions of sound sources to each sound quality attribute, the dominant sound source is determined.     

Development of a highway traffic noise prediction model that considers various road surface types

A highway traffic noise prediction model has been developed for environmental assessment in South Korea. The model is based on an outdoor sound propagation method and is fully compliant with ISO 9613 and the sound power level (PWL) estimation for a road segment, as suggested in the ASJ Model-1998 that is based on PWLs. Due to that model’s selection of two pavement types, such as asphalt or concrete pavement, an unacceptable traffic noise prediction is made in cases where the road surface is different from that on which the model is based. In order to address this problem, several road surface types are categorized, and the PWL of each surface type is determined and modeled by measuring the noise levels obtained from newly developed methods. An evaluation of the traffic noise prediction model using field measurements finds good agreement between predicted and measured noise levels.     

Prediction of noise inside tracked vehicles

In this paper, numerical simulation has been used to predict the noise inside tracked vehicles. To determine the interaction forces between running track system and the chassis hull of a tracked vehicle, a rigid multi-body tracked vehicle mode, which includes the track moving system, was constructed and simulated using ADAMS software. Finite element (FE) and boundary element (BE) models of the chassis hull of a tracked vehicle were created and adopted to perform the vibro-acoustic analysis. Correlation between the FE and BE models and physical measurements proved sufficiently good that the models could be used to predict the interior noise in a tracked vehicle. The structural frequency dynamic response was determined using the software MSC/NASTRAN. The interior noise was predicted using the software SYSNOISE. The predicted noise levels in a tracked vehicle have been found to be in good agreement with physical measurements.     

节气门全开工况下的车内轰鸣噪声预测*

采用内燃机多体动力学理论提取了某四缸汽油机的燃烧激励和惯性激励,将其加载到某车型的整车有限元模型上预测节气门全开工况下的车内噪声。通过仿真与实验的对比,发现仿真声压级曲线的变化趋势与实验基本一致,并且较好地捕捉到了3300～3700 r/min转速段内的轰鸣噪声,证明了仿真结果的可靠性,为整车开发阶段的节气门全开工况NVH性能预测提供技术参考。     

Index for vehicle acoustical comfort inside a passenger car

Sound quality is among the main factors that influence customers’ preference for choosing good automobile products. It all started more than 10 years ago and grows up so fast due to high competition in the automotive industries. A-weighted noise levels and sound power are usually utilised to measure the noise but they are not adequate to characterise the impact sound inside a car. The most popular approach to determine sound quality of a product is to define an annoyance or specific index, which involves both subjective and objective evaluations. Subjective and objective tests should be studied concurrently in order to determine the sound quality inside a passenger car. This approach is used in this paper to evaluate vehicle comfort index according to most frequently used sound quality metrics, namely; Zwicker loudness, sharpness, roughness and fluctuation strength. As a result researchers of different fields of automotive acoustics investigations can use this index according to the type of road (international road roughness) without any need to perform time-consuming jury tests. The metrics are correlated with jury test results that show which of them and how much has affected the acoustical comfort of the vehicle. The relation between road roughness and vehicle acoustical comfort index is another point of interest in this research.     

Dynamic noise modeling at roundabouts

Modeling spatial and temporal noise variations at roundabouts is a tedious task. Indeed, noise levels are strongly influenced by the complex vehicle interactions taking place at the entries. An accurate modeling of the merging process and its impact on vehicle kinematics, waiting time at the yield signs and queue length dynamics is therefore required. Analytical noise prediction models disregard those impacts since they are based on average flow demand patterns and pre-defined kinematic profiles. The only way to capture all traffic dynamics impacts on noise levels is to combine a traffic simulation tool with noise emission laws and a sound propagation model. Yet, such existing dynamic noise prediction packages fail in representing vehicle interactions when the roundabout is congested and are difficult to calibrate due to their numerous parameters. A new traffic simulation tool, specifically developed for roundabouts, is therefore proposed in this paper. It has few easy-to-calibrate parameters and can be readily combined with noise emission and propagation laws. The obtained noise package is able to produce relevant dynamic noise contour maps which can support noise emission assessment of local traffic management policies. Results are validated against empirical data collected on a French suburban roundabout on two different peak periods.     

Fast prediction of aerodynamic noise induced by the flow around a cylinder based on deep neural network

Accurate and fast prediction of aerodynamic noise has always been a research hotspot in fluid mechanics and aeroacoustics. The conventional prediction methods based on numerical simulation often demand huge computational resources, which are difficult to balance between accuracy and efficiency. Here, we present a data-driven deep neural network (DNN) method to realize fast aerodynamic noise prediction while maintaining accuracy. The proposed deep learning method can predict the spatial distributions of aerodynamic noise information under different working conditions. Based on the large eddy simulation turbulence model and the Ffowcs Williams-Hawkings acoustic analogy theory, a dataset composed of 1216 samples is established. With reference to the deep learning method, a DNN framework is proposed to map the relationship between spatial coordinates, inlet velocity and overall sound pressure level. The root-mean-square-errors of prediction are below 0.82 dB in the test dataset, and the directivity of aerodynamic noise predicted by the DNN framework are basically consistent with the numerical simulation. This work paves a novel way for fast prediction of aerodynamic noise with high accuracy and has application potential in acoustic field prediction.     

Modeling of offshore pile driving noise using a semi-analytical variational formulation

Underwater noise radiated from offshore pile driving got much attention in recent years due to its threat to the marine environment. This study develops a three-dimensional semi-analytical method, in which the pile is modeled as an elastic thin cylindrical shell, to predict vibration and underwater acoustic radiation caused by hammer impact. The cylindrical shell, subject to the Reissner–Naghdi’s thin shell theory, is decomposed uniformly into shell segments whose motion is governed by a variational equation. The sound pressures in both exterior and interior fluid fields are expanded as analytical functions in frequency domain. The soil is modeled as uncoupled springs and dashpots distributed in three directions. The sound propagation characteristics are investigated based on the dispersion curves. The case study of a model subject to a non-axisymmetric force demonstrates that the radiated sound pressure has dependence on circumferential angle. The case study including an anvil shows that the presence of the anvil tends to lower the frequencies and the amplitudes of the peaks of sound pressure spectrum. A comparison to the measured data shows that the model is capable of predicting the pile driving noise quantitatively. This mechanical model can be used to predict underwater noise of piling and explore potential noise reduction measures to protect marine animals.     

A road traffic noise pattern simulation model that includes distributions of vehicle sound power levels

Annoyance, sleep disturbance and other health effects of road traffic noise exposure may be related to both level and number of noise events caused by traffic, not just to energy equivalent measures of exposure. Dynamic traffic noise prediction models that include instantaneous vehicle noise emissions can be used to estimate either of these measures. However, current state-of-the-art vehicle noise emission models typically consider a single emission law for each vehicle category, whereas measurements show that the variation in noise emission between vehicles within the same category can be considerable. It is essential that the influence of vehicles that are producing significantly more (or less) noise than the average vehicle are taken into account in modeling in order to correctly predict the levels and frequency of occurrence of road traffic noise events, and in particular to calculate indicators that characterize these noise events. Here, an approach for predicting instantaneous sound levels caused by road traffic is presented, which takes into account measured distributions of sound power levels produced by individual vehicles. For the setting of a receiver adjacent to a dual-lane road carrying free flow traffic, the effect of this approach on estimated percentile levels and sound event indicators is investigated.