Railway tunnels avoid direct acoustic annoyance by railway traffic. However, vibrations from tunnels propagate through the soil and lead to disturbances by percieved low frequency vibrations.
The objective of this project is to develop and implement a mathematical model that takes a moving vibrating load into account. Furthermore, the surrounding soil is modeled as an anisotropic material, consisting of layers with arbitrary orientation.
The propagation of the vibrations inside the tunnel are modelled by the finite element method (FEM), where the superstructure of the tunnel and the railway are considered. Vibrations outside the tunnel, propagating through the (infiinite) soil are modelled by the boundary element method (BEM). For a detailed model of the whole system, both methods have to be coupled.
Railway platforms are located very close to the track and thus are assumed to alter the sound propagation. The degree of this effect, however, has not yet been investigated in detail
The aim of the project WiaBahn was to investigate the shielding effect of railway platforms. One of the main questions was how to properly deal with the vicinity to the track, the platform’s large reflecting horizontal surface, and the often present canopy. It is unclear whether standard noise propagation prediction methods can be applied without modifications.
Based on measurements directly at the platform as well as in the distance the acoustic effect of low railway platforms was investigated and suitable source models for the 2.5D boundary element method (BEM) as well as for standardized prediction methods were derived. The advantage of the 2.5D method which was also used in the project PASS is, that a constant cross-section can be combined with point sources or incoherent line sources which is not possible with pure 2D methods. 3D BEM is not feasible for such large structures.
WiaBahn was funded by the FFG (project 845678) and the ÖBB. The project was done in cooperation with the Austrian Institute of Technology (AIT, project leader) and Kirisits Engineering Consultants.
Rumble strips are (typically periodic) grooves place at the side of the road. When a vehicle passes over a rumble strip the noise and vibration in the car should alert the driver of the imminent danger of running off the road. Thus, rumble strips have been shown to have a positive effect on traffic safety. Unfortunately, the use of rumble strips in the close vicinity of populated areas is problematic due to the increased noise burden.
The aim of the project LARS (LärmArme RumpelStreifen or low noise rumble strips) was to find rumble strip designs that cause less noise in the environment without significantly affecting the alerting effect inside the vehicle. For this purpose, a number of conventional designs as well as three alternative concepts were investigated: conical grooves to guide the noise under the car, pseudo-random groove spacing to reduce tonality and thus annoyance, as well as sinusoidal depth profiles which should produce mostly vibration and only little noise and which are already used in practice.
Two test tracks were established covering a range of different milling patterns in order to measure the effects of rumble strips for a car and a commercial vehicle running over them. Acoustic measurements using microphones and a head-and-torso-simulator were done inside the vehicle as well as in the surroundings of the track. Furthermore, the vibration of the steering wheel and the driver seat were measured. Using the acoustics measurements, synthetic rumble strip noises were produced, in order to get a wider range of possible rumble strip designs than by pure measurements.
Perception tests with 16 listeners were performed where the annoyance of the immissions as well as the urgency and reaction times for the sounds generated in the interior were determined also using the synthetic stimuli.
LARS was funded by the FFG (project 840515) and the ASFINAG. The project was done in cooperation with the Research Center of Railway Engineering, Traffic Economics and Ropeways, Institute of Transportation, Vienna University of Technology, and ABF Strassensanierungs GmbH.
Railway vehicles passing through tight curves can produce a high pitched noise called curve squeal. Curve squeal is a very salient type of noise located in the high frequency range that can range between a tonal narrow band and a wide band noise. The reason for the tonal noise is lateral creepage on the top of the rail, which excites wheel vibration at frequencies corresponding to their modes. Wide band noise, however, is caused by wheel flanges touching the rail.
The project PAAB aims at investigating the effect on the perceived annoyance of such noises using in a perception test. Using the resulting perceptual characterization of curve squeal should aid in more adequately considering this type of noise in noise mapping.
Based on previous conventional large-scale emission measurements as well as new measurements at immission distances using a head-and-torso-simulator representative samples for curve squeal will be derived and used in a perception test. This will also be aided by using synthetic well defined curve squeal noise.
PAAB is funded by the FFG (project 860523) and the Austrian Federal Railways (ÖBB). The project is done in cooperation with the Research Center of Railway Engineering, Traffic Economics and Ropeways, Institute of Transportation, Vienna University of Technololgy (project leader), Kirisits Engineering Consultants, and psiacoustic Umweltforschung und Engineering GmbH.
The rapid increase in available computing power and the fast evolution of audio interfacing and transmission technologies have led to a new age of immersive audio systems to reproduce spatial sound with surrounding loudspeakers. Many of these approaches require a precise and robust space-time-frequency analysis of sound fields. The joint project of ARI and IRCAM combines the mathematical concepts provided by the ARI with the profound knowledge in real-time signal processing and acoustics of IRCAM. It addresses fundamental research questions in both fields and aims at developing improved methods for the target applications mentioned above.
The main questions that his project aims at are:
Computational models for speech production and analysis have been of research interest since the 1960s. Most models assume the vocal tract (VT) to be a segmented straight tube, but when pronouncing nasals like /m/ and /n/ or nasalized vowels the nasal part of the vocal tract plays an important part and a single tube model is not feasible anymore. Thus, it is necessary to consider a branched tube model that includes an additional tube model for the nasal tract. For these branched models, the estimation of the cross section area of each segments from a given signal is highly non trivial and in general requires the solution of a non-linear system of equations.
The problem is overdetermined, and we have to add additional restrictions to our solution, for example restrictions on upper and lower bounds of the area functions or smoothness assumption about the vocal tract. To that end we introduced e.g. probabilistic methods (variational Bayes) into our model estimation.
The beam forming method focuses an arbitrary receiver coil using time delay and amplitude manipulation, and adds to the temporal signal of the microphones or the short time Fourier transform.
64 microphones are collected by a microphone array with arbitrary shape. For compatibility with acoustic holography, equal spacing and a grid with 8 x 8 microphones is used.
Localization of sound sources on high speed trains is a typical application. The method is used to separate locations along the train and especially the height of different sound sources. Typical sound sources on high speed trains are rail-wheel contact sites and aerodynamic areas. The aerodynamic conditions occur at all heights, especially at the pantograph.
In the past a FWF project dealing with the basics of Stochastic Transformation Methods was executed at the ARI. Explicitly the Karhunen Loeve Expansion and the Transformation of a polynomial Chaos were applied in the wave number domain. The procedure is based on the assumption of Gaussian distributed variables. This assumption shall be generalized to arbitrary random variables.
The assumption of a wave number domain limits the model to a horizontally layered half space. This limitation shall be overcome by Wavelets kernels in the transformation instead of Fourier kernels. The aim is the possibility to calculated one sided statistical distributions for the physical parameters and arbitrary boundaries with the new method.
Up to now a boundary element method based on the collocation method was combined with the Fast Multipole Method to speed up the numerical calculation.
This approach was chosen, because the collocation is a fast method to build up the matrix and the Fast Multipole Method (FMM)is a fast method to solve large matrices. For compatibility with the software HAMS the FMM has to be ported to a Galerkin based Boundary Element (BE) approach.
One of the biggest problems encountered when building numerical models for layers is the lack of exact deterministic material parameters. Therefore, stochastic models should be use. However, these models have the general drawback of overusing computer resources. This project developed a stochastic model with the ability to use a shear modulus in conjunction with a special iteration scheme allowing efficient implementation.
With the Karhunen Loeve Expansion (KLE), it is possible to split the stochastic shear modulus, and therefore the whole system, into a deterministic and a stochastic part. These parts can then be transformed into a linear system of equations using finite elements and Chaos Polynomial Decomposition. Combining the KLE and the Fourier Transformation in combination with Plancherel's theorem enables decoupling of the deterministic part into smaller subsystems. An iteration scheme was developed which narrows the application of "costly" routines to only these smaller deterministic subsystems, instead of the whole higher dimensional (up to a dimension of 10,000) system matrix.
As concerns about vibrations produced by machinery and traffic have increased in past years, models that can predict vibrations in soil became more important. However, since material parameters for soil layers cannot be measured exactly in practice, it is reasonable to use stochastic models.
Methods to predict the propagation of vibrations in soil are relatively undeveloped. Reasons for this include the complexity of the wave propagation in soil and the insufficient knowledge of material parameters. During this project a method was developed to simulate the propagation of vibrations that are caused by a load at the base of a tunnel.
When dealing with the model of a tunnel in a semi-infinite domain like soil, the boundary element method (BEM) seems to be an appropriate tool. Unfortunately it cannot be applied directly to layered orthotropic media, because of the lack of a closed form of the Greens function, which is essential for BEM. But by transforming the whole system into the Fourier domain with respect to space and time, it is possible to numerically construct an approximation for this function on a predefined grid. With this approximation the boundary integral equation, that describes the propagation of waves caused by a vibrating load at the base of a tunnel can be solved.
Models that can help to predict the propagation of vibrations inside soil layers are of great interest in earthquake sciences or when constructing railway lines and tunnels.