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In the present paper, direct numerical simulation (DNS) and particle image velocimetry (PIV) have been applied complementarily in order to generate a database of near-wall turbulence properties of a highly turbulent jet impinging on a solid surface under different inclination angles. Thereby, the main focus is placed on an impingement angle of 45°, since it represents a good generic benchmark test case for a wide range of technical fluid flow applications. This specific configuration features very complex flow properties including the presence of a stagnation point, development of the shear boundary layer and strong streamline curvature. In particular, this database includes near-wall turbulence statistics along with mean and rms velocities, budget terms in the turbulent kinetic energy equation, anisotropy invariant maps, turbulent length/time scales and near-wall shear stresses. These properties are useful for the validation of near-wall modeling approaches in the context of Reynolds-averaged Navier–Stokes (RANS) and large-eddy simulations (LES). From this study, in which further impingement angles (0°, 90°) have been considered in the experiments only, it turns out that (1) the production of turbulent kinetic energy appears negative at the stagnation point for an impingement angle other than 0° and is balanced predominantly by pressure-related diffusion, (2) quasi-coherent thin streaks with large characteristic time scales appear at the stagnation region, while the organization of the flow is predominantly toroidal further downstream, and (3) near-wall shear stresses are low at the stagnation region and intense in regions where the direction of the flow changes suddenly.

[EN] A framework is described for predicting the airborne noise inside railway vehicles which is applied to rolling noise sources. Statistical energy analysis (SEA) is used to predict the interior noise by subdividing the train cabin into several subsystems. The dissipation loss factors are obtained from the measured reverberation time in the train cabin. The power input to the interior SEA model is obtained from the external noise sources by multiplying the incident sound power on the external surfaces with measured transmission coefficients of the train floor and sidewalls. The sound power incident on the train floor is calculated by using an equivalent source model for the wheels and track together with an SEA model of the region below the floor. The incident sound power on the sides is obtained by using a waveguide boundary element (2.5D BE) method. The procedure is applied to a Spanish metro train vehicle running in the open field for which rolling noise is the main external noise source. The procedure is verified by field measurements of sound pressure beneath the carriage, on the sidewalls and inside the vehicle. The sensitivity of the results to changes in interior absorption is also studied, including the effect of passengers.

[EN] An approach is presented for modelling the noise propagation beneath the train floor and this is applied to rolling noise sources. It is assumed that the sound incident on the train floor is made up of a direct and a reverberant component. A combination of two numerical modelling approaches is considered to deal with these: an equivalent source model, to represent the direct component, and statistical energy analysis (SEA) for the reverberant part. In the equivalent source model, the wheel is replaced by monopole and dipole sources, which represent its radial and axial radiation. The rail vertical vibration and the sleepers are replaced by arrays of monopole sources while the rail lateral vibration is replaced by an array of lateral dipoles. The sound power of the rolling noise is obtained by using the TWINS model. In the SEA model, the region beneath the train floor is divided into several volumes and the power input to these subsystems is assumed to be due to the first reflections from the train floor and the ground. The reverberant and direct sound have very similar contributions to the total sound power incident on the train floor although this depends on how the equipment is arranged beneath the train. The modelling approach is verified by comparing the predicted sound pressure levels with laboratory measurements and with field tests.

[EN] In order to be able to predict train interior noise, it is first important to calculate the external sound pressure distribution on the floor, sidewalls and roof. This can then be combined with the transmission loss of the train panels to determine the interior noise. Traditional techniques such as the finite element and boundary element (FE/BE) methods in three dimensions (3D) can achieve this result but are computationally very expensive. In this paper, a wavenumber-domain boundary element (2.5D BE) approach is instead adopted to predict the propagation of rolling noise from the wheels, rails and sleepers to the train external surfaces. In the 2.5D models, only the cross-section of the vehicle is represented by using boundary elements, while the third direction is considered in terms of a spectrum of wavenumbers. The rail is treated directly in the wavenumber domain but, to include the wheel, a method of representing point sources in a 2.5D approach is developed. An inverse Fourier transform is applied to obtain the spatial distribution of the sound pressure on the train surfaces. The validity of this approach has been verified by comparison with experimental data. The 2.5D BE method was first used to predict the sound distribution on a 1:5 scale train surfaces due to a point source below the vehicle, and later it was used to predict the sound pressure on a full-scale metro vehicle due to a loudspeaker. Comparisons of predictions with measurements on the scale model and on the metro vehicle showed good agreements. For a point source below the vehicle, the sound pressure levels on the train floor were found to be around 20 dB higher than on the sides, and the sound pressure on the train roof was negligible. The 2.5D BE method was also used to predict the sound pressure on the metro vehicle surfaces in running operation, in which the predicted sound pressure levels on the train external surfaces agreed with measurements to within 3 dB and similar trends were fo