Method for Localisation of Sound Sources and Aggregation to an Acoustic Center
Time: 7:20 am
Author: Yannik Weber
Abstract ID: 1689
Preliminary work by the IPEK - Institute of Product Engineering at KIT has shown that the simulated pass-by measurement for exterior noise homologation of vehicles has relevant optimization potential: the measurement can be carried out in smaller halls and with a smaller measurement setup than required by the norm and thus with less construction cost and effort. A prerequisite for this however is the scaling of the entire setup. For the scaling in turn, the sound sources of the vehicle must be combined to a single point sound source - the acoustic centre. Previous approaches for conventional drives assume a static centre in the front part of the vehicle. For complex drive topologies, e.g. hybrid drives, and unsteady driving conditions, however, this assumption is not valid anymore. Therefore, with the help of an acoustic camera, a method for localizing the dominant sound sources of the vehicle and a software-based application for summarizing them to an acoustic centre were developed. The method is able to take into account stationary, unsteady and sudden events in the calculation of the acoustic centre, which is moved as a result. Using substitute sound sources and two vehicles, the method and the used measurement technology were examined and verified for their applicability.
NEMO project: acoustic detection of vehicle engine speed
Time: 6:00 am
Author: Truls Berge
Abstract ID: 1718
As part of the EU Horizon2020 project NEMO, SINTEF has developed an algorithm to detect the engine speed of passing vehicles. Some road vehicles can emit abnormal high noise levels or high levels of exhaust gases in urban conditions. The high noise level can be related to aggressive driving (high acceleration and high engine speed), to a modified or malfunctioning exhaust system, or to other vehicle defects. It is well-known that many motorcycles or mopeds often are equipped with non-original exhaust mufflers, giving high noise levels that can be a nuisance to the community. In the NEMO project, the detecting of so-called high emitters (HE) is essential to reduce the impact of such vehicles on the environment and public health. To enable to categorize HE vehicle based on the driving behaviour, it is necessary to detect both acceleration and corresponding engine speed. The paper describes the principle of the algorithm developed and results from testing on vehicles, including a motorcycle. This test shows that it is feasible to estimate the engine speed, also when the vehicle is accelerating, if the number of cylinders is available for the estimation. Further testing of the algorithm is planned within the NEMO project.
Advanced design of Close-Proximity (CPX) trailer enclosure acoustics on tyre/road noise measurement
Time: 8:00 am
Author: DONGFANG LI
Abstract ID: 1822
The PolyU Mark II Twin-wheeled CPX trailer was developed for the measurement of tyre/road noise in Hong Kong urban environment according to a standard methodology (ISO/CD 11819-2) - the Close-Proximity (CPX) method. Numerical simulations of the acoustics of PolyU Mark II CPX enclosure were conducted and a good agreement between numerical and experimental results was obtained. In order to extend the capacity of the Mark II CPX trailer and enhance the acoustic performance within the enclosure for future tyre/road noise studies, the validated numerical simulations were carried on to design the next generation of the PolyU CPX system. Through analyzing the acoustic performance within the enclosures of different dimensions and the distributions of sound pressure level (SPL) inside the anechoic chamber, the geometry of the PolyU Mark III CPX enclosure was finally determined. With newly designed enhanced interior wall absorption, the new PolyU Mark III CPX enclosure design was delivered into numerical simulations for acoustic analysis. Fewer room modes and high uniformity of SPL distributions were observed within the new enclosure design. The PolyU Mark III CPX enclosure was fabricated based on the corresponding dimensions and the specific absorption layers. Great consistency was achieved between the numerical and measured results of the Mark III CPX enclosure. In addition, the PolyU Mark III CPX enclosure shows an improved acoustic property with a lower background noise level during road tests than Mark II CPX enclosure. The outcome of this study firmly establishes the feasibility of designing advanced CPX enclosure with numerical simulations with results that can be realized in realistic CPX measurement.
Temperature influence on tire/road noise measurements: recently collected data and discussion of various issues related to standard testing procedures
Time: 8:20 am
Author: Erik Bühlmann
Abstract ID: 1830
Air, road, and tire temperatures substantially affect tire/road noise emission. For measuring purposes, one would like to normalize measurements to a reference temperature by means of a reliable correction procedure. Current studies show that temperature effects remain an important source of uncertainty in tire/road noise measurements and tire testing, even after applying the correction terms provided in the various standards. This seems to be the case for the measurement methods used in OBSI, CPX, SPB, and various regulations or directives based on ECE R117. This paper examines a new dataset consisting of 7.5 million temperature measurements aimed at contributing to a better understanding of temperature effects and the ways they relate to air, road, and tire temperatures. It is assumed that tire temperatures are the most relevant for noise corrections; therefore, special studies are made for how tire temperatures relate to air and road (test surface) temperatures. A profound analysis is provided on how these relationships vary over different day times, seasons, and climatic regions. Based on this analysis, the authors provide suggestions for improvement of temperature normalization in current tire/road noise and tire testing standards. Special considerations are devoted to measurements on test tracks having ISO 10844 reference surfaces.
Why do clogged porous asphalt pavements give better traffic noise reduction than a dense-graded asphalt pavement?
Time: 8:40 am
Author: Ulf Sandberg
Abstract ID: 2031
In Europe, porous asphalt concrete pavements (PAC) are commonly used to reduce traffic noise. Especially the double-layer type (DPAC) provides substantial traffic noise reduction. Unfortunately, PAC pavements compared to dense asphalt pavements have reduced acoustic longevity; the main reason being clogging of the pores and voids, sometimes also more ravelling. The dense-graded pavements considered here are stone mastic asphalts (SMA, in the US known as stone matrix asphalt) which often have surface macrotexture of the same size as the PAC. The main difference is that the PAC has accessible pores/voids providing sound absorption, while the SMA has practically no porosity. One would expect that when the pores in the PAC have become clogged while ravelling is not yet substantial, that the noise property of the PAC would approach that of the SMA. But experimental studies suggest that even when PAC:s are effectively clogged, they retain a certain noise reduction compared to SMA:s. This paper examines this feature of clogged PAC versus SMA and reasons for this unexpected property, for a few Swedish DPAC pavements compared to SMA pavements, with due consideration of possible difference in maximum aggregate size and macrotexture as represented by mean profile depth (MPD) and grading curves.
Monitoring trends in road surface impact on rolling noise emissions
Time: 9:00 am
Author: Dick Botteldooren
Abstract ID: 2365
Road surfaces degrade over time due to heavy traffic and weather conditions, which negatively influences both driving comfort and acoustic properties. In addition, the lifetime of a road surface can be increased by performing cost-effective incremental maintenance and this maintenance becomes more expensive when the damages are more severe (cracks, potholes). Current methods such as CPX are performed in a standardized way (using designated equipment and tightly controlled measurement conditions), however budget constraints limit frequent monitoring of surfaces. Therefore, continuous monitoring using ordinary passenger vehicles could be helpful to observe trends in rolling noise emissions and road evenness. Hence, we deployed designated sensor boxes in a number of vehicles that are on the road for other purposes. In addition, advances in calibration of different devices using de-noising autoencoders alleviate the effect of various measurement conditions such as driving speed, braking, accelerating, and temperature. As our innovative methodology has now been on the road for several years, trend analysis becomes possible.
Artificial Neural Network Model for Road Pavement Classification using Features of Tire-Pavement Noise and Road Surface Images
Time: 7:40 am
Author: Seo Il Chang
Abstract ID: 2964
Artificial neural network models were developed to classify road pavement types into the transverse-tined, the longitudinal-tined, NGCS(Next Generation Concrete Surface), Diamond Grinding, and Stone Mastic Asphalt by utilizing tire-pavement noise and road surface images. Tire-pavement noise data were collected by OBSI(On-Board Sound Intensity) method, and analyzed to obtain sound intensity level, sound pressure level, and sound quality indices. Road surface image data was analyzed through image feature extraction algorithms of Hough transformation and HOG(Histogram of gradient). The important features among the acoustic and image characteristics were selected by a random forest model. The acoustic features selected by the random forest algorithm are the overall sound intensity level of 400~5kHz 1/3-octave bands, the sound intensities (W/m2) of 800~2kHz 1/3-octave bands, loudness, fluctuation strength and tonality. The image features selected are the number of longitudinal lines extracted from Hough transform algorithm and HOG of the central cell. The two groups of the selected features were applied separately or together to an artificial neural network model to find classification performance. The classification accuracy rates of the models using acoustic features only, image features only and both acoustic and image features combined were 90.8%, 88.8%, and 97.3%, respectively.