Contract No. 881771 TRAIN PASS-BY NOISE SOURCE ...

Contract No. 881771

TRAIN PASS-BY NOISE SOURCE CHARACTERIZATION AND SEPARATION TOOLS FOR COST-EFFECTIVE VEHICLE CERTIFICATION

Deliverable D2.1

Report on State of the art, strategy and requirements for new separation techniques

Due date of deliverable: 30/08/2020

Actual submission date: 13/09/2020

Leader/Responsible of this Deliverable: Vibratec/MicrodB

Reviewed: Y

Document status Revision Date Description

1 First issue

The information in this document is provided “as is”, and no guarantee or warranty is given that the information is fit for any particular purpose. The content of this document reflects only the author`s view – the Joint Undertaking is not responsible for any use that may be made of the information it contains. The users use the information at their sole risk and liability. This project has received funding from Shift2Rail Joint Undertaking under the European Union’s Horizon 2020 research and innovation programme under grant agreement No 881771.

Dissemination Level PU Public X CO Confidential, restricted under conditions set out in Model Grant Agreement CI Classified, information as referred to in Commission Decision 2001/844/EC

Start date of project: 01/12/2019 Duration: 36 months

Ref. Ares(2020)4781762 - 13/09/2020

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REPORT CONTRIBUTORS Name Company Details of Contribution LAMOTTE MicrodB Applications, requirements LE MAGUERESSE MicrodB Time domain inverse method JANSEN/DITTRICH TNO PBA based method SARRADJ/KUJAWSKI TUB Microphone antenna method Thompson ISVR Review Dittrich TNO Review 26/8/2020: Exec summary,

Other corrections and remarks thoughout LAMOTTE MicrodB Modification acceptance for final release

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EXECUTIVE SUMMARY The objective of this work package WP2 is to develop novel and innovative techniques to obtain the sound power level and directivity of the different types of separate noise sources during a train pass-by at constant speed. These are required as input for simulation tools and environmental prediction models. The development will focus on microphone array techniques and PBA-based techniques to obtain such separated source data. To date, despite many past field measurements, relatively limited progress has been made in this respect. Therefore the methods to be developed in TRANSIT are expected to provide a new basis for railway noise source measurement and separation, guided by the specific requirements of the rail sector. Many references are known for microphone array measurements and processing for pass-by noise analysis, applied for several decades especially in both the railway and aeronautic fields. The first experiments and subsequent improvements were mostly on the identification and visualisation of the main noise sources. They are visualised by 2D acoustic maps obtained from the usual beamforming or delay-and-sum methods. The Doppler effect associated with source speed and source/array distance requires selection of the best processing in frequency or time domain. In the case of a high speed train, with a short source/array distance (10 m envisaged for TRANSIT) and short pass-by time, the recent time domain CLEANT deconvolution which has been successfully applied to train pass-by will be used for TRANSIT. The selected array geometry for TRANSIT experiments is Vogel’s spiral which achieves high performance. Past measurements have demonstrated that the strength of aerodynamic sources increases at a greater rate with the train speed than the rolling noise. This property is put to use for the PBA-based method, requiring measurements at medium speed to characterize the rolling noise and at high speed for the aerodynamic noise. The strongest aerodynamic sources are located on the first coach and bogies, emitted in the frequency range below 2 kHz. At higher frequencies up to 4 kHz, the higher train part with pantographs is dominant. It permits to limit the upper limit frequency range at 4 kHz. After source localization from beamforming, past research efforts were concentrated on applying deconvolution methods on those results to extract the sound power of sources by spatial integration. All methods rely on the assumption of monopole distribution and do not permit to extract source directivity. So far, publications aimed at extracting the source directivity have used the movement of the sources to identify their sound power with different view angle from the array. This first approach could be used for TRANSIT. The second approach, defining the radiation pattern of the sources, aims at replacing source areas by an equivalent sound source, simulating the pass-by with those equivalent sound sources and in accordance with the measured SPL, minimizing the difference by adjusting radiation characteristics or by choosing an appropriate source model.

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The PBA (Pass-by analysis) method is more recent than microphone array technology but has been successfully applied in different railway projects since 2005. The in situ measurements require one or more trackside microphone and one or more accelerometer under the rail. This method primarily characterizes the rolling noise spectrum in terms of combined roughness and transfer function, from which rolling noise at any other speed can be derived. This in turn allows to separate other sources such as traction noise or aerodynamic noise at other speeds In the TRANSIT project, PBA-based methods will be developed for separation of the physical sources, and extending this with sound power and directivity. Methods for sound power will be in line with the new CEN draft standard on source terms, whilst directivity will be evaluated from low speed measurements or spectrogram techniques. This will always be limited by the configuration and complexity of sources, but should in any case be assessed by comparison with basic source directivities (e.g. monopole/dipole) pressure level. While results can be expressed in a linear spectrum for microphone array methods, the results from PBA are in third octave bands as as commonly used in measurement standards. For the validation of both methods, different campaigns are scheduled and provided by the complementary project FINE-2. Microphone array methods will be evaluated on an Alstom hybrid-train at Velim with pass-bys at medium speeds, and on a Talgo train on a high-speed line with high-speed pass-bys with predominantly aerodynamic noise. The PBA based methods will in addition to those campaigns, will also be evaluated at Madrid metro with low speeds and with predominant traction noise. In addition, those preliminary tests will provide an opportunity to resolve any unforeseen issues.

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ABBREVIATIONS AND ACRONYMS

Abbreviation / Acronyms Description BW Beamwidth; width of the main-lobe corresponding to the

PSF BF Beamforming CEN European Committee for Standardization CMF Cross-Spectral Matrix Fitting method CSM Cross-spectral matrix; frequency representation of array

sensor signals CLEAN-T Time domain CLEAN deconvoltuion method CNOSSOS Common Noise Assessment method in EU DAMAS Deconvolution Approach for the Mapping of Acoustic

Sources EMU Electric Multiple Unit ESM Equivalent Source Method DMU Diesel Multiple Unit MSL Maximum side-lobe level of PSF NNLS Non Negative Least Square PBA Pass-by-analysis PBN Pass-by Noise PSF Point-spread function; spatial filter response of microphone

array to a monopole source SNR Signal-to-noise ratio SODIX Source Directivity Modelling on Cross-Spectral Matrix

method SPI Source power integration method SPL Sound pressure level (dB) TDH Time Domain Holography TF Transfer Function TSI Technical Specifiction for Interoperability

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TABLE OF CONTENTS REPORT CONTRIBUTORS ...................................................................................................................................... 2 EXECUTIVE SUMMARY ............................................................................................................................................ 4 ABBREVIATIONS AND ACRONYMS ..................................................................................................................... 6 TABLE OF CONTENTS .............................................................................................................................................. 7 LIST OF FIGURES ..................................................................................................................................................... 10 LIST OF TABLES ...................................................................................................................................................... 12 1 INTRODUCTION ............................................................................................................................................... 13 2 STATE OF THE ART: MICROPHONE ANTENNA METHOD ................................................................... 14

INTRODUCTION .................................................................................................................................................. 14 DOPPLER EFFECT AND TIME / FREQUENCY DOMAIN APPROACHES .............................................................. 15 BEAMFORMING AND DECONVOLUTION IN FREQUENCY DOMAIN .................................................................... 18

2.3.1 BEAMFORMING-MS ....................................................................................................................................................... 18 2.3.2 HYBRID BEAMFORMING ................................................................................................................................................ 18 2.3.3 DECONVOLUTION METHODS ......................................................................................................................................... 21 2.3.4 SOURCE POWER INTEGRATION (SPI) ........................................................................................................................ 21

BEAMFORMING AND DECONVOLUTION IN TIME DOMAIN ............................................................................... 24 2.4.1 DELAY-AND-SUM BEAMFORMING ............................................................................................................................... 24 2.4.2 DECONVOLUTION IN FREQUENCY DOMAIN FROM DELAY-AND-SUM BEAMFORMING ......................................... 25 2.4.3 DIRECTIVITY ESTIMATION ............................................................................................................................................ 29

INVERSE METHODS IN FREQUENCY DOMAIN .................................................................................................... 30 2.5.1 CROSS SPECTRAL MATRIX FITTING (CMF) ............................................................................................................... 30 2.5.2 SOURCE DIRECTIVITY MODELLING ON CROSS-SPECTRAL MATRIX METHOD (SODIX) .................................... 31

INVERSE METHOD IN TIME DOMAIN ................................................................................................................. 32 OTHER COMPLEMENTARY METHODS ............................................................................................................... 34

2.7.1 COUPLING COHERENCE METHODS ............................................................................................................................... 34 2.7.2 SWEAM OR WSE METHODS ....................................................................................................................................... 34

ARRAY DESIGN ................................................................................................................................................... 35

3 INDUSTRIAL APPLICATIONS FOR ARRAY METHODS ......................................................................... 38

OVERVIEW OUTSIDE RAILWAY APPLICATION ................................................................................................. 38 OVERVIEW OF RAILWAY APPLICATIONS .......................................................................................................... 43

4 STATE OF THE ART: PBA-BASED METHOD ............................................................................................ 50

INTRODUCTION ................................................................................................................................................. 50 PBA-BASED METHOD FOR PASS-BY NOISE SEPARATION ............................................................................... 50

4.2.1 DEFINITIONS ................................................................................................................................................................... 50

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4.2.2 PASS-BY NOISE SEPARATION ........................................................................................................................................ 52 4.2.3 ILLUSTRATION OF METHOD .......................................................................................................................................... 53

SOUND POWER LEVELS AND DIRECTIVITY ....................................................................................................... 57 EXPERIMENTAL MEANS AND PROCEDURES...................................................................................................... 61

4.4.1 EXPERIMENTAL MEANS ................................................................................................................................................. 61 4.4.2 PROCEDURES .................................................................................................................................................................. 63

5 REQUIREMENTS AND STRATEGY ............................................................................................................... 66

REQUIREMENTS ................................................................................................................................................. 66 STRATEGY .......................................................................................................................................................... 68

5.2.1 METHOD SYNTHESIS ...................................................................................................................................................... 68 5.2.2 IMPROVEMENT PROPOSAL FOR ARRAY METHODS .................................................................................................... 70 5.2.3 STRATEGY FOR PBA-BASED SOURCE SEPARATION ..................................................................................... 72

REFERENCES ............................................................................................................................................................ 75 APPENDICES ............................................................................................................................................................. 83 ANNEX A: REQUIREMENTS .................................................................................................................................. 84

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LIST OF FIGURES Figure 1: Schematic representation of the speed dependency of the contributions from different

physical noise sources such as traction noise and aerodynamic noise [Metarail]. ................ 13

Figure 2: parametric analysis of Doppler Effect depending of block size, speed for source/array distance of 10 m in underwater pass-by experiment [Oudompheng 2015] ........................... 16

Figure 3: Effect of convective amplification in dB (left) and frequency shift (right) for 2 speeds (50 and 100 km/h) and 2 source/array distances (3 and 10 meters) with trajectory position [Cousson 2019] ..................................................................................................................... 17

Figure 4: comparison of localization results in third octave 1250 Hz for high speed train pass-by measurement acquired at 65 kHz frequency sampling [Le Courtois 2012] ........................... 17

Figure 5: Single source maps obtained with time domain delay-and-sum Beamforming (left) and Hybrid Beamforming (right) from [Zhang 2018b]. .................................................................. 20

Figure 6: comparison of reference spectra (thin lines) with quantification from SPI procedure (thick lines) for 3 speeds ((a): 50 km/h, (b): 180 km/h, (c) : 300 km/h [Zhang 2019]. ..................... 23

Figure 7: strength of a dipole and a quadrupole: spectrum for reference microphone and SPI procedure integration with and without compensation factor [Zhang 2019]. ......................... 24

Figure 8: Illustration of moving focus for de-dopplerisation [Barsikow 1988] ................................ 25

Figure 9: Effect of speed on the localization of a moving source [Cousson 2019] ........................ 27

Figure 10: Effect of speed on the quantification of a moving source [Cousson 2019] .................. 27

Figure 11: Deconvolved source maps examined with CLEANT method [Kujawski 2020] ............ 28

Figure 12: Sound power estimates for each source region via spatial integration [Kujawski 2020] .............................................................................................................................................. 29

Figure 13: Diagrammatic representation of the method [Poisson 1996]. ...................................... 30

Figure 14: Example of source amplitudes as a function of position and direction (right) obtained from turbo-fan measurements (left) [Funke 2012]. ................................................................ 31

Figure 15: MSL and BW for different arrangements with M=64, He = 10 and r = 0.5D [Sarradj 2016] .............................................................................................................................................. 36

Figure 16 : Examples for different parameter values with M= 64 with Voronoi diagrams showing approximately the area per microphone [Sarradj 2016] ........................................................ 37

Figure 17: BW (a) and MSL (b) over frequency from [Zhang 2018b]. The black line indicates the genetic optimized array ......................................................................................................... 38

Figure 18: Directivity measurement for flyover: microphone array and results [Siller 2002] ......... 39

Figure 19 : Source contributions and overall level: Results from [Siller 2018] .............................. 40

Figure 20: Investigation of engine noise through amplitude method on A340-300 [Lamotte 2009] .............................................................................................................................................. 40

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Figure 21: Large microphone arrays from [Humphreys 2016] ....................................................... 41

Figure 22 : Car pass-by noise measurement with small microphone array [Ballesteros 2015] ..... 42

Figure 23: CLEANT results from car pass-by noise measurement [Cousson 2018] ..................... 43

Figure 24 : METARAIL results using a T-array with 48 microphones positioned at a distance of 2.7 m from the track. .................................................................................................................... 45

Figure 25: Microphone arrays for rail pass-by measurement [Thompson 2018] ........................... 46

Figure 26 : Measurement setup from [Zang 2018] and [He 2014] for a Chinese high speed train 48

Figure 27: Localisation result from [He 2014] for a Chinese high speed train ............................... 49

Figure 28: Illustration of the summation of a speed-dependant roughness level spectrum (left) and a speed independent transfer function level spectrum to obtain the pass-by level spectrum. .............................................................................................................................................. 51

Figure 29: Indicative graph of deviation from the rolling noise transfer function due to other sources in different frequency regions. ............................................................................................... 52

Figure 30: Indicative contributions of other sources in different frequency regions to the overall pass-by level.......................................................................................................................... 53

Figure 31: Total transfer function levels near the Rheda track and a ballast track, measured with various train types, as a function of frequency in third-octave bands (left). Measured and calculated pass-by level of the Thalys on a Rheda high speed track at 300 km/h as a function of frequency in third-octave bands (right). ............................................................................. 55

Figure 32: Photograph of the Dutch MAT’64 rolling stock (top), spectrogram of the sound pressure (centre) and vertical rail acceleration (bottom) of a pass-by at 113 km/h. .............. 56

Figure 33: Measured combined transfer function for MAT64 and VIRM as a function of frequency in third-octave bands (left). Measured (rolling+traction) and calculated (rolling) pass-by levels of MAT64. .............................................................................................................................. 57

Figure 34: Three sound paths for pass-by noise ........................................................................... 58

Figure 35: Screen shot of a calculation model for the transfer functions Hi. .................................. 60

Figure 36: Overview measurement set-up for PBA-based pass-by source separation. ................ 62

Figure 37: Flow chart of pass-by source separation. .................................................................... 63

Figure 38: Illustration of different selections of the pass-by around source locations on a train. 65

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LIST OF TABLES Table 1: Wheel contributions estimated from microphone array measurements and track

contributions estimated from rail vibration together with a radiation model for different test speeds [Roll2Rail 2017]. ........................................................................................................ 47

Table 2: Target number of pass-bys for each indicated speed for high-speed and conventional trains ...................................................................................................................................... 63

Table 3 : state of the art summary ................................................................................................. 69

Table 4 Overview of strategy for microphone array method development .................................... 70

Table 5: Overview of strategy for PBA-based source separation .................................................. 73

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1 INTRODUCTION The objective of this work stream is to develop novel and innovative techniques to obtain the sound power level and directivity of the different types of noise source during train pass-bys at constant speed. It may be used as input for simulation tools and environmental prediction models. Several physical noise sources can contribute to the total pass-by sound level of a train. Besides the rolling noise generated in the wheel/rail contact, aerodynamic noise (bogies, inter-coach cavities, pantographs and front end) and traction noise (motors, gear transmissions, electrical converters and transformers, cooling and HVAC systems and other auxiliaries) can contribute considerably depending on the operating conditions. The extent to which these non-rolling noise sources are significant depends on train speed and combined roughness levels, which govern the rolling noise. This is illustrated in Figure 1. This example shows that the source levels are train speed dependent to different degrees. The balance between rolling noise and other noise sources depends not only on speed but also their strength, in particular that of the rolling noise. A noisy traction source in combination with a low combined roughness could even result in a dominant contribution of the traction source to the pass-by level at higher train speeds.

Figure 1: Schematic representation of the speed dependency of the contributions from different physical noise sources such as traction noise and aerodynamic noise [Metarail].

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Regarding the TSI procedure integration and experiments in the TRANSIT project, there are 3 challenges for the separation of noise sources: • an industrial challenge to propose acoustic measurement equipment in accordance with

financial means of train manufacturers who would use this type of diagnosis, the most obvious solution is then the reduction of cost, in agreement with easy implementation of the measurements on existing TSI measurement campaigns,

• scientific challenges to improve methods for quantifying sources of interest adapted to high speed trains and traction systems, the expectations being accuracy in sound power quantification, directivity for new benefits in propagation model,

• Technical challenges to propose two vehicle scenarios and measurement configurations which will permit to qualify the proposed improvements.

Building on this, at least two different methods capable of separating aerodynamic noise sources, traction and equipment noise sources, and rolling noise will be developed and implemented. The first category uses a microphone antenna to reconstruct the sound source distribution from pass-by measurement, whilst the second extends PBA with static and operational pass-by measurements. The review of their industrial applications in railway and other domains will demonstrate their current efficiency. The work programme is based on a state-of-the-art review comparing methods used in the past and new methods along with a requirements definition leading to the strategy choice. This report starts with the first chapters summarizing the state of the art for each of the methods and their applications in industrial context. It focuses on their capabilities to provide sound power in third-octave bands, information on the directivity of sound radiation and the uncertainty of the results, their usability and correspondence to standardization needs. The last chapter details the requirements which will guide the technical choice for the improvement to develop and the scenarios to validate them.

2 STATE OF THE ART: MICROPHONE ANTENNA METHOD

INTRODUCTION The techniques based on array measurements identify the contributions from different noise sources from their spatial localization. They attempt to find the best source distribution fitting the sound pressure measurements on the microphone array. The propagation models can be very simple by assuming free field propagation and omnidirectional radiation or more complex with advanced transfer functions and source models. Generally, the methods are categorized into beamforming (BF) with associated deconvolution processing corresponding to a point-by-point calculation, or inverse methods working with the complete source distribution. It can be applied in time or frequency domain. Those categories are reviewed in the next chapters with the adaptation

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to the case of moving sources. The movement implies an amplitude and frequency modulation, respectively named convection and Doppler Effect. A first section will detail the constraints to consider this effect in processing.

DOPPLER EFFECT AND TIME / FREQUENCY DOMAIN APPROACHES

The frequency shift between the one emitted by the source (fs) and the measured one on microphones (fm) is defined by:

𝛥𝛥𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑 = 𝑓𝑓𝑓𝑓 − 𝑓𝑓𝑓𝑓 = 𝑓𝑓𝑓𝑓 � 11−𝑣𝑣 𝑐𝑐 cos𝜃𝜃�

− 1� (1)

It depends on the frequency fs, D array/source distance, and block size due to θ angle (maximum view angle between microphone and calculation grid). The ratio of the source velocity and the speed of sound is the Mach number. The radial Mach number is obtained when the velocity in the direction to the receiver is used. The operation to remove the Doppler Effect by resampling the received/recorded signal is called dedopplerisation. 2 cases have to be considered: Doppler effect per analysed time data block/snapshot size is:

• within the frequency resolution: all frequency domain methods without dedopplerisation can be applied,

• outside the frequency resolution: time data dedopplerisation over small windows is obliged, with averaging/overlap snapshot

The maximum Doppler Effect is calculated within the snapshot for array microphone and source extremities. The time window should also respect:

• a displacement of sources within the map resolution to avoid source spread over several calculation points.

• a constant array directivity or point-spread function (avoiding incident angle)

A parametric study is required to determine the Doppler Effect per measurement configuration depending on source/array distance, source speed, upper limit of the frequency range, snapshot size. Some examples of such analysis are reviewed below, with different cases/application domains where the applied methodology is very different. A first analysis done for PhD work [Oudompheng 2015] about pass-by noise for vessel at low speed in underwater environment is presented Figure 2. The Doppler effect is small in those conditions as demonstrated by the maximum frequency not being affected by the Doppler effect. The

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frequency shift is within the frequency resolution in spectral domain. It also proves the small effects with increasing analysis angle (trajectory size). In this case the acoustic source picture can be calculated from a single shot in the frequency domain.

Figure 2: parametric analysis of Doppler Effect depending of block size, speed for source/array distance of 10 m in underwater pass-by experiment [Oudompheng 2015]

A similar investigation has been carried out by [Cousson 2019] for road vehicle pass-by analysis. The effects of convective amplification and frequency shift were calculated regarding vehicle speed/distance from microphone antenna centre. The Doppler Effect varies very quickly between 0.95-1.05 at high speed for the vehicle size (+/- 3 m) in front of the array (Xs=0). The knowledge of the frequency shift is important to accurately apply frequency domain methods, a dedopplerisation process is required but the instantaneous acoustic source map is possible.

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Figure 3: Effect of convective amplification in dB (left) and frequency shift (right) for 2 speeds (50 and 100 km/h) and 2 source/array distances (3 and 10 meters) with trajectory

position [Cousson 2019] In the aeronautic domain, in the experiment by [Siller 2002], the Doppler Effect was analysed in terms of angle/aircraft position to apply frequency domain methods where speed is high but array/source distance is large. A time series of 12288 samples was then used for the further frequency analysis using a Fast Fourier Transform for 11 time segments with 2048 samples with an overlap of 0.5, windowed with a Hanning function. The length of the time series corresponds to an averaging time of 0.3 s. For a typical fly-over with a ground speed of 80 m/s and an altitude of 200 m, the aircraft moved about 25 m in 0.3 s. The maximum change of the emission angle θ for a certain time segment occurs when the aircraft is directly over the array and is in the order of +/- 3,7°. In those conditions with small Doppler Effect, the acoustic source picture technique is possible. In railway applications, the distance is short, the speed can be high, the source distribution is over several wagons, and the Doppler Effect is important. A single acoustic source picture is not possible, each train part is processed on a given position, preferably in front of the array and the obtained pictures are mapped into a single result. An analysis is done in the PhD work [Le Courtois 2012] about pass-by noise for high speed train (320 km/h) applying both time and frequency methods. The Figure 4 compares the acoustic source maps. The time domain analysis results in better localization than in frequency domain with improved signal to noise ratio.

Figure 4: comparison of localization results in third octave 1250 Hz for high speed train pass-by measurement acquired at 65 kHz frequency sampling [Le Courtois 2012]

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A preliminary Doppler effect analysis is necessary in order to decide if processing in frequency or time domain can be applied with low uncertainties.

BEAMFORMING AND DECONVOLUTION IN FREQUENCY DOMAIN Beamforming in frequency domain (Conventional Beamforming) is a commonly used spatial filtering technique that has been used for decades to investigate stationary sources [Merino-Martìnez 2019]. The method uses the Fourier transforms of the individual sound pressure signals recorded with a microphone array. Uncorrelated monopoles are assumed and their sound propagation from the scan-plane to each sensor is modelled by the so-called steering vector. In the past, some extensions of the algorithm have been developed to make it suitable for the analysis of moving sources that are mentioned in the following.

2.3.1 Beamforming-MS

Conventional Beamforming has been extended to moving sources by Fleury and Bultè, introduced as Beamforming-MS [Fleury 2011]. The extension consists of a reformulation of the steering vector including a Doppler approximation of monopole Green’s function. This accounts for the frequency and amplitude shift at the observer position related to emission time frame that is evaluated. The beamforming results must be calculated for each evaluated time frame individually since the values of the steering vector change over time. An averaging over the calculated source maps is performed as the final step of the method.

To ensure the validity of the Doppler approximation, some conditions related to the evaluated emission time interval have to be fulfilled:

● the monopole sources are moving linearly with constant speed

● it is assumed that the displacement of the sources compared to the array-source-distance is very small to achieve a constant Doppler frequency shift over the evaluated time frame

The latter criterion is associated with low radial Mach number scenarios. As explained in section 2.2, low Mach numbers occur when the source velocity is low or the distance of the array from the observation plane is large. In the case of railway pass-by measurements, often this cannot be satisfied with the array placed close to the track. Thus, the use of this method is preferable for the analysis of distant sources like wind turbines and aircrafts [Fleury 2013] or slow vehicles like ships [Lamotte 2016].

2.3.2 Hybrid Beamforming

Another Beamforming method in the frequency domain was introduced in [Zhang 2017] and communicated as Hybrid Beamforming in [Zhang 2018b]. The method differs from the

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Beamforming-MS algorithm with regard to the formulation of the steering vector and the de-dopplerisation method. The de-dopplerisation procedure is performed in time domain and consists of resampling the signals collected by the microphone array according to a certain location in the scan-plane. The subsequent weighting and summation of the transformed signals is carried out in the frequency domain.

In [Zhang 2018b], a formulation of the steering vector is derived that is based on a linear approximation of the source motion. As a result, it is not necessary to de-dopplerise the signals according to every grid point. Moreover, several beamforming results for the same time frame can be averaged when using different de-dopplerisation points in the scan-plane. Zhang showed that the resulting source maps have an improved signal-to-noise ratio compared to the source maps obtained with beamforming in the time domain (see Figure 5). Similar to Beamforming-MS, an averaging of individual source maps from each evaluated time frame is performed as the last step of the method.

The algorithm has been used in multiple publications to evaluate data from train pass-by measurements [Zhang 2017], [Zhang 2018b], [Thompson 2018].

Source maps obtained with Beamforming-MS or Hybrid Beamforming do not allow statements about the individual strengths of different sound sources since these are correlated with the spatial array response. In addition, Beamforming-MS and Hybrid Beamforming assume uncorrelated sources and are not capable of determining their directivity. Nevertheless, the influence of the PSF can be removed from the source maps by the application of deconvolution methods discussed in the next section. Alternatively, an extended version of the Source Power Integration (SPI) method

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proposed in [Zhang 2018b] and [Zhang 2019] can be used for appropriate weighting of the source maps.

Figure 5: Single source maps obtained with time domain delay-and-sum Beamforming (left) and Hybrid Beamforming (right) from [Zhang 2018b].

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2.3.3 Deconvolution methods

Fleury and Bultè [Fleury 2011] extended existing deconvolution methods (CLEAN [Högbom 1974], CLEAN-SC [Sijtsma 2007], DAMAS [Brooks 2006], DAMAS2 [Dougherty 2005]) to the application of moving source scenarios.

Of particularly interest is the deconvolution approach for the mapping of acoustic sources (DAMAS) [Brooks 2006], extended and referred to as DAMAS-MS in [Fleury 2011]. The DAMAS method developed for stationary sources solves a linear system of equations with a modified Gauss-Seidel method to recover the initial source constellation from the beamforming result. A matrix containing the PSFs for each considered source position is calculated beforehand which can be time consuming when many source positions are taken into account. The DAMAS-MS method uses an averaged version of the individual PSFs over multiple time frames to build the system of equations.

Another approach is to treat the DAMAS-MS problem as a minimization problem with additional regularization parameters related to physical assumptions. In [Pham 2017], the smoothed L1/L2 ratio was used as a regularization function to ensure sparsity of the solution. In addition, a prior knowledge term was added to the objective function which provides a non-negative solution and a maximum value that corresponds to that of the beamforming result. Pham et al. [Pham 2017] solved this problem by using the variable metric forward-backward algorithm (VMF-B) [Chouzenoux 2014].

In [Lamotte 2016], the VMF-B solution strategy showed superior performance over BF-MS and DAMAS-MS regarding sparsity and robustness against noise on simulated and experimental pass-by data of a ship.

A computationally efficient method to solve the deconvolution problem has been proposed by Dougherty, known as DAMAS2 [Dougherty 2005]. The problem is simplified by assuming a shift-invariant PSF. Extended versions of this algorithm to be applied to moving sources have been developed in [Fleury 2011] as DAMAS2-MS and in [Zhang 2018b] as embedded DAMAS2. However, often the underlying requirement of a shift-invariant PSF cannot be fulfilled for train pass-by experiments where the array-source distance is small. The method is primarily used in fly-over measurements.

2.3.4 Source Power Integration (SPI)

Another approach to determine the strength of certain source regions based on beamformed source maps is the SPI method, originally developed by Brooks and Humphreys [Brooks 1999]. The method consists of normalising the beamformer output at each position in the integration area

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by a sum of the PSF associated with neighbouring sources. The normalized beamforming results are summed to obtain an estimate of the strength that belongs to the source region.

Zhang et al. developed an extended approach for the quantification of moving sources that is based on the SPI method and takes the unknown directivity into account [Zhang 2019]. According to Zhang et al, the procedure can be divided into the following steps:

● A frequency-dependent compensation factor is first obtained numerically by simulating the pass-by of a monopole over the length of a single car to account for directivity biased source strength estimates. This will be used to relate the source spectrum obtained from the beamforming output to the SPL contribution at a reference microphone, set at the centre of the array.

● The various sources on the map are identified and enclosed in a certain predefined area centred at the peak of the main lobe of that source

● The acoustic map is integrated over these areas to give a frequency dependent result for each of the sources identified on the map.

● The compensation factor is applied to the integrated beamforming result to give the spectrum of each source. The effect of the compensation factor is to rescale the noise spectra estimated from the beamforming output so that they represent noise spectra at the array centre averaged over the length of a single car.

A level deviation between the averaged reference and source spectra was noticed over 50 numerical simulations of moving monopole sources at three different speeds (50, 180, 300 km/h) (see: Figure 6 from [Zhang 2019]). The reason for the deviation is attributed to the fact that the spectra of the reference microphone are obtained without and the source spectra with de-dopplerisation. According to Zhang, the deviation of the two averaged spectra is within 0.5 dB for all three speeds [Zhang 2019].

In addition, the SPI procedure has been used to calculate the strength of a dipole and a quadrupole from pass-by. This numerical simulation revealed that the extended SPI method can be successfully used to predict the noise of a directional source at the reference position from beamforming results (see: Figure 7 from [Zhang 2019]). The use of the compensation factor decreased the mean deviation of the estimated source spectra from the spectrum at the reference microphone from 2.0 dB to -0.1 dB in the dipole case and from 2.9 dB to -0.6 dB in the quadrupole case.

Nevertheless, it is an open question if it is possible to obtain the correct compensation factor when multiple sources are outside of the integration area or multiple areas with different directivity

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patterns exist. In this case, the reference microphone spectrum is a mixture of contributions from all source regions which makes it difficult to compare it to the spectrum of a single source area.

Figure 6: comparison of reference spectra (thin lines) with quantification from SPI

procedure (thick lines) for 3 speeds ((a): 50 km/h, (b): 180 km/h, (c) : 300 km/h [Zhang 2019].

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Figure 7: strength of a dipole and a quadrupole: spectrum for reference microphone and SPI procedure integration with and without compensation factor [Zhang 2019].

BEAMFORMING AND DECONVOLUTION IN TIME DOMAIN

2.4.1 Delay-and-sum Beamforming Beamforming in the time domain consists of summing the individually delayed sound pressure signals collected by the array microphones with respect to a certain focus direction or location. The principle giving the method its name is used for decades in the context of vehicle pass-by measurements (e.g. [Barsikow 1996], [Noh 2014]). For convenience, moving vehicles are evaluated in a moving frame over a predefined tracking length. This is done by constantly adapting the focusing direction of the array according to the sources trajectory and by resampling the collected sound pressure values, known as de-dopplerisation [Barsikow 1988]. This procedure enables to remove the Doppler frequency shift and to take convective amplification effects into account that are caused by the source movement relative to the observer. The principle of moving focus for de-dopplerisation is illustrated in Figure 8 from [Barsikow 1988].

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Figure 8: Illustration of moving focus for de-dopplerisation [Barsikow 1988] Uncorrelated self-noise at the sensors often degrades the beamforming result. One way to improve the SNR of the beamformed source maps is to remove the auto-power of the microphone signals from the squared beamforming result [Dougherty 2004]. This technique has been used in the context of train pass-by measurements for example in [Kujawski 2020]. Similar to source maps obtained with beamforming in the frequency domain, the spatial resolution of the source maps obtained via time domain beamforming are degraded by the influence of the microphone array response (PSF). This is especially the case at low frequencies where the main lobe is larger than at higher frequencies. Further, the superposition of the side lobes belonging to adjacent sources leads to an overestimation at the considered source position. This prohibits a spatial integration of the beamformer output to estimate the sound power levels of individual source regions. Thus, it is appropriate to use delay-and-sum beamforming for relative comparison of mitigation measures (e.g. in [Carlsson 2011]), but the method does not yield absolute source strength estimates for each considered source position.

2.4.2 Deconvolution in frequency domain from delay-and-sum Beamforming Early approaches to recover the initial moving source distribution by removing the PSF from delay-and-sum beamforming results made use of a fast Fourier transform to solve the inverse problem in the frequency domain [Brühl 2000]. The Source Density Modellization approach (SDM) developed by Brühl and Röder [Brühl 2000] aims to find a solution for the full-scale deconvolution problem, which requires to include all frequencies and source positions during optimization. The algorithm was originally developed and validated in the context of railway pass-by measurements. From today's perspective, this procedure can be considered as outdated due to its computational demanding nature.

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Guérin and Weckmüller [Guérin 2006], [Guérin 2008b] simplified the problem by considering only a limited number of frequency components in the problem formulation. However, an integration over multiple frequencies that depend on the specific measurement situation is still necessary, making the method unsuitable for the analysis of narrow band source spectra. The method was exclusively used in the context of fly-over measurements (e.g. [Guèrin 2008]).Deconvolution in time domain A recent deconvolution method that has already been applied to high-speed train measurements ([Kujawski 2020]) is the CLEANT method proposed by Cousson et al. [Cousson 2019]. This iterative method is exclusively performed in time domain which allows an accurate de-dopplerisation of the microphone signals. The steps performed by the method in each iteration are to find the source signal containing the highest energy over a certain time interval emitted by one of the positions in the scan-grid. This signal is simultaneously written to a new (“clean”) source map. In the following step, the propagation of the source signal from the location to each sensor is modelled and subtracted from the actual microphone signals yielding new microphone signals. The final step of one iteration consists of applying the beamforming method again by using the new microphone signals. The CLEANT method has been evaluated and compared in [Cousson 2019] regarding its performance to the frequency domain deconvolution method DAMAS-MS proposed by Fleury and Bultè [Fleury 2011]. As illustrated in Figure 9 and Figure 10 taken from [Cousson 2019], the CLEANT method showed a lower position and quantification error especially at higher speeds on simulated pass-by data of a single source. In addition, the robustness of the method for a wrong vehicle speed input was proven to be higher. However, the quantification error obtained with the CLEANT method increases with the presence of uncorrelated noise and showed slightly worse results than the DAMAS-MS method at a value of SNR=0 dB. In addition, it became apparent that the quantification of the source strength is more sensitive to an incorrect specification of the array source distance.

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Figure 9: Effect of speed on the localization of a moving source [Cousson 2019]

Figure 10: Effect of speed on the quantification of a moving source [Cousson 2019]

Cousson et al. [Cousson 2019] also validated the method in an experiment with two sources mounted at a moving pendulum. The experiment showed the superior source separation performance of the CLEANT method over frequency domain beamforming and the DAMAS-MS deconvolution method. Kujawski and Sarradj [Kujawski 2020] further applied the CLEANT method to data from high-speed train measurements with operating speeds of 275 km/h and 278 km/h (see Figure 11 [Kujawski 2020]). Individual source regions (pantograph, bogies) of two trains were investigated regarding their individual strength (see Figure 12, [Kujawski 2020]). It was shown that the CLEANT method provides a highly improved spatial resolution compared to the standard delay-and-sum method. Further, the removal of the PSF allowed to obtain sound power estimates for each source region via spatial integration.

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Figure 11: Deconvolved source maps examined with CLEANT method [Kujawski 2020]

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Figure 12: Sound power estimates for each source region via spatial integration [Kujawski 2020]

2.4.3 Directivity estimation Attention must be given to the fact that the previously mentioned time domain methods are assuming an omnidirectional radiation which is rarely the case for aerodynamic sources. For example, the noise emitted by the pantograph area has been examined in different experimental studies, finding that the radiation pattern is rather similar to a dipole [Bosquet 2019]. Spatially varying radiation was also found in [Carlsson 2011] for a high-speed train, where beamformed source maps from five different calculation windows in the range of -45° to 45° were compared. Therefore, inaccuracies in the calculation of the sources sound power must be expected if the beamforming results from a small number of observation angles are considered. In [Zhang 2019] it was shown based on simulated data from a dipole and a quadrupole that a significant discrepancy between the source spectra obtained from a single microphone and from beamforming can appear. A method for estimating the horizontal directivity pattern of different sources at pass-by has been introduced in [Poisson 1996]. As illustrated in Figure 13 taken from [Poisson 1996], a time-frequency representation of the beamformer output is created for each source under consideration, whereas each time instant is related to its radiation characteristic. In order to cope with the low time-frequency resolution, the smoothed pseudo wigner ville transformation [Shin 1993] was used. The method showed reasonable performance in simulated and experimental conditions.

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Figure 13: Diagrammatic representation of the method [Poisson 1996].

INVERSE METHODS IN FREQUENCY DOMAIN Few inverse methods exist in the literature that are capable of determining source directivities in addition to the spatial source distribution and strength. All of these methods were originally developed for the use in stationary measurement scenarios and are explained in the following.

2.5.1 Cross Spectral Matrix fitting (CMF) The CMF method [Yardibi 2008] aims to find the unknown source strengths by solving an inverse problem by minimizing the difference between the measured and a modelled cross-spectral matrix (CSM). The modelled CSM is described by a transfer matrix specifying the sound propagation from the source to the receiver and the cross-spectrum of the sources pressure amplitudes. For simplicity, the pressure amplitude matrix is reduced to a diagonal matrix which is associated with

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the assumption of uncorrelated sources. A solution strategy to solve the full problem for correlated sources was already exploited in [Yardibi 2008], known as CMF-C. Although the method was not originally developed for taking different radiation patterns into account, it can be solved for different source models and transfer functions. A possible implementation is currently under investigation in the context of rotating machinery [DFG , ref. SA 1502/9-1]. The CMF method has been primarily used in stationary aeroacoustic applications like measurements of airfoils in wind tunnels [Herold 2013].

2.5.2 Source Directivity Modelling on Cross-Spectral Matrix Method (SODIX) The source directivity modelling on cross spectral matrix method (SODIX) [Michel 2008], [Funke 2012] is an extension of the CMF method. Similarly to CMF, the source distribution is recovered by iteratively minimizing the difference between the modelled and measured CSM. The SODIX method extends the modelling of the CSM by including discrete directivities. Those are defined between the individual sensors and each assumed source position in the observation plane. The problem can be solved by the use of a conjugative gradient optimization method and a side condition that enforces positive source strength. As it can be seen from the measurements of a turbofan engine illustrated in Figure 14 [Funke 2012], the SODIX method provides source amplitudes as a function of position and direction. The principle of modelling discrete directivities can also be extended to arbitrary source models, which is currently under investigation in [DFG, ref. SA 1502/9-1]. The SODIX method was initially developed to separate broadband sound sources of turbofan engines, which are known to have sizable directivities, on a test-bench [Funke 2012] and later applied to jet engines in wind-tunnels [Siller 2017].

Figure 14: Example of source amplitudes as a function of position and direction (right) obtained from turbo-fan measurements (left) [Funke 2012].

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The CMF and SODIX methods were originally developed for the characterization of stationary sources and have not yet been used in railway pass-by context. One possibility to extend the methods to moving sources is to utilize procedures derived in previous publications. As explained in section 2.1.3., [Fleury 2011] and [Zhang 2018b] showed that the array signals can be de-dopplerised in time and in frequency domain and derived formulations of the corresponding transfer functions. Those de-dopplerisation procedures and transfer functions could also be used in the CMF and SODIX method to calculate and model the CSM. Different concepts are conceivable to take the source directivity into account. One possibility is to enhance the methods with prior information about the source models and transfer functions developed in WP1 and to solve the minimization problem accordingly. Otherwise, the SODIX method may be solved without previous knowledge to obtain the source strengths and discrete directivity information. Regarding the latter approach, some potential issues can be considered beforehand that might limit the success of the method applied to moving sources. First, the inverse problem has to be solved for each evaluated time-frame since the transfer functions vary over time. Thus, the approach can be computationally challenging depending on the length of a vehicle and the number of scan-grid points and time frames to be considered. Second, the method will suffer from statistically weak results since the source-receiver path for which the discrete directivities are specified are varying for each time-frame. An averaging can only be performed across results from multiple pass-by runs.

INVERSE METHOD IN TIME DOMAIN This section is dedicated to the inverse methods applied in time domain. Three types of method will be studied: 1. Methods based on the reconstruction of temporal signal per snapshot by passing though the frequency domain 2. Methods based on the convolution with an inverse filter pre-calculated in the frequency domain 3. Pure time domain methods The first family is basically the use of frequency-domain methods applied on each temporal snapshot. For each snapshot, a short term Fourier transform is performed and an inverse method is resolved on each frequency line. Once the calculation is finished, an inverse Fourier transform is applied on the full reconstructed spectrum in order to recover temporal signals for each discrete node of the object. This process has been published under the name of “Time Domain Holography” (TDH) in 2001 [Hald 2001]. Actually, one could use another method than Holography to recover sources in the frequency domain (Equivalent source method ESM or Bayesian focusing for instance). Since the snapshots are windowed, an overlap-and-add algorithm has to be set up in

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order to avoid losing temporal samples. A very similar process has been published by Deblauwe et al. [Deblauwe 1999]. The process could be applied only on one frequency line to study the time-evolution of one component. Finally, a method called Moving Frame Acoustic Holography (MFAH) has been developed for the application of acoustic holography based on pass-by measurement. It follows the same approach as TDH without taking into account evanescent waves and thus may be applied in far field. A particular pass-by noise application is provided by [Park 2001]. A real experimental validation of the approach is given by the reconstruction of a field generated by a speaker embedded on the car. The second type of method is based on convolving an inverse multi-channel filter by the measured temporal signals [Bai 2007]. This method is based on the equivalent source concept. The inverse filter is design in the frequency domain using the least-squares optimization with the aid of Tikhonov regularization. The regularization parameter is empirically determined. Then an inverse FFT is called for converting the frequency-inverse filter into finite impulse response filters in the time domain. For each focal point, the source strengths on the source grid are the result of a convolutional product between the measured sound pressure and the impulse response function. A last family is based on recovering sources in the time domain, sample by sample. The equivalent source concept is again used. This method is called “Time Domain Equivalent Source Method” [Bi 2013], [Zhang 2015]. In that case, an impulse response function is described by a distribution of Dirac functions weighted by the inverse of the propagation distance. The convolution product appearing in the forward problem is replaced by a matrix product. A classical system of the form y=Ax+b is then deduced, for each temporal sample. The sources are obtained by the Tikhonov-form solution. All appropriate precautions shall be taken concerning the interpolation of temporal samples reconstructed on the discrete temporal vector. This method is highly unstable because each reconstructed sample depends on the previous one: errors are cumulative over time. But theoretically, the method could be used for pass-by analysis. The reader should note that these methods have never been used in a railway context (except the MFAH method). Indeed, these methods suffer from a problem linked with the high number of data to compute. The non-stationarity property implies to work sample-by-sample (third family) or to resolve one inverse problem per frequency line and per snapshot (first family). The second family is quite efficient but can lead to instabilities (problem of a constant regularization) and the need to store a lot of inverse filters inside the computer. However, a method like TDH could be helpful if we want to track the time evolution of tonal components. In that case, we do not have to repeat the reconstruction for the whole frequency spectrum and the method could be fast enough. A recent publication [Meng 2019] applies the Compressive Beamforming to reconstruct a fast moving source. Although the name of the method is Compressive beamforming, it is an inverse time domain procedure that recovers the source signals.

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OTHER COMPLEMENTARY METHODS

2.7.1 Coupling coherence methods In case of several sources located in the same small area, their contribution cannot be calculated from spatial integration over the localization map as explained before for SPI and deconvolution methods. The principle is to apply pre-processing where sources are separated in the signal into different components. There are two approaches:

• Blind separation with Principal component analysis for “virtual” sources • Conditioned Spectral Analysis with physical sources from reference sensors

It has been applied in the pass-by domain to road vehicle in the PhD work from Cousson [Cousson 2018]. The interest is to separate in the wheel area the contribution of rolling noise and propulsion sources. In railway vehicle application, it could be of interest for the separation of sources emitted in close area (for example to separate aerodynamic sources on the bogies and the rolling noise). But the possibility to have a pure source reference signal is a difficulty. Moreover, the synchronization of on-board sensors and exterior microphone array is also a difficult task. At least, the short signal duration with high speed will compromise the quality of the results because coherence analysis needs long time window. Microphone array used in attempt to identify the wheel contribution by suppressing the rail contribution measured with an accelerometer on the track could be tested. Nevertheless, the rail can be considered as a coherent line source that radiates at a preferred angle relative to the track. Moreover, it is excited by the same roughness as the wheel and is therefore correlated with the wheel source.

2.7.2 SWEAM or WSE methods A recent alternative to beamforming are the so-called SWEAM (Structural Wavenumbers Estimation with an Array of Microphones) [Faure 2015] or WSE (Wave Signature Extraction) [Zea 2017] methods, which calculates an inverse estimation of the structural wavenumbers and decay rates of the waves in the rail. The main principle of the SWEAM method is to separate the rail noise by means of filtering the wavenumber spectra obtained from microphone array measurements of the train pass-by. The key point is the design and application of the wavenumber filters according to the rail signature measured with the microphone array. As mentioned above, the rail signature consists of plane waves that typically can be represented as narrow–band signals in the wavenumber domain, hence

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justifying the application of band-pass wavenumber filters that only accept the narrow–band content and reject the rest of the spectrum.

ARRAY DESIGN Array design approaches aim to find sensor arrangements offering optimal spatial filter properties in terms of separating closely adjacent noise sources. The physical size of the array and the distance between the individual microphones defines the frequency bandwidth for which a sufficient separation performance can be provided. At low frequencies, this depends on the extent of the planar arrangement and the distance from the observation area. In contrast, the space between the sensors determines the upper frequency limit until the Shannon criterion is violated and spatial aliasing degrades the array output. Thus, the definition of an optimum geometry depends on the individual measurement situation that is determined by the source constellation and the desired frequency range to be evaluated. Several qualitative and quantitative measures can be found in the literature that have been used in the past to assess the performance of an arrangement. One approach is to draw its co-array pattern [Johnson 1992]. Each position of the co-array belongs to the distance vector between a pair of microphones. The number of unique points in the pattern inversely coincides with the redundancy of the sensor positions, whereas a higher number corresponds to an improved array performance. The co-array pattern also provides information about the frequency dependence and spatial aliasing characteristics of a geometry. Further measures are directly evaluated on the spatial impulse response of the arrangement known as the point-spread function (PSF). This includes the beamwidth (BW), which is the size of the area surrounding the maximum of the main lobe that is above the amplitude of -3 dB. The BW acts as a measure of spatial separability performance between adjacent sources. In addition, the level difference between the main lobe and the maximum sidelobe is often evaluated, known as the maximum sidelobe level (MSL). This property indicates the spatial filter performance of rejecting sources from other directions which is equivalent to the ability of resolving sources of lower strength in a source map. BW and MSL are frequently used for performance comparison across various studies ([Prime 2013], [Sarradj 2016]). However, the spatial impulse response of a given array varies with the position and frequency. [Le Courtois 2016] considered the variance of BW and MSL for different sound incidence angles to develop an appropriate geometry for train pass-by applications via genetic optimization. Early approaches in array design for pass-by applications made use of simple geometries, e.g. one-dimensional line- or two-dimensional cross-arrays ([Barsikow 1996]). The redundant positioning of the sensors led to unfavourable properties of the array response including a limited bandwidth. The regular spacing between the sensors made it necessary to adjust the distances according to the frequency under consideration.

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Irregular designs were used to overcome this drawback in subsequent studies, which includes the Star- [Mellet 2006], T- [Dittrich 2000] and single branch spiral-shape [Dougherty 1998]. Especially the spiral design gained widespread attention and was used in many of the subsequent studies involving train pass-by measurements (e.g. [Wakabayashi 2008]). The superior performance of the logarithmic spiral compared to classical shapes particularly reflects in a lower sidelobe level and main lobe width [Dougherty 1998] which has been additionally proved in numerical pass-by simulations [Nordborg 2000]. Based on these findings, various multi-arm spiral geometries have been proposed (e.g. [Underbrink 2001]; [Hald 2002]; [Noh 2014]; [Zhang 2018b]) that have become widely used, not only for use in pass-by measurements, but also in the context of wind tunnel experiments ([Underbrink 2002]). [Prime 2013] compared six different spiral shapes regarding their MSL and BW properties, finding that the Underbrink spiral [Underbrink 2002] offers the best overall performance. The various spiral arrangements consist of many parameters influencing the individual properties of the spatial array response. [Sarradj 2016] developed a single-parameter approach that yields arrangements with pareto-optimal properties regarding BW and MSL, based on variations of Vogel’s spiral [Vogel 1979]. The design approach was compared to variations of the Underbrink spiral. The study revealed that the BW of the Underbrink spiral shows only small variation for different parameter values, whereas the single-parameter method can be tuned towards a small BW or MSL. To date, this design approach has not been used in context of pass-by measurements.

Figure 15: MSL and BW for different arrangements with M=64, He = 10 and r = 0.5D [Sarradj 2016]

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Figure 16 : Examples for different parameter values with M= 64 with Voronoi diagrams showing approximately the area per microphone [Sarradj 2016]

In contrast to parameterized approaches, numerical optimization methods have been applied in few studies related to train pass-by applications in order to find arrangements with optimal properties ([Le Courtois 2016]; [Zhang 2018b]). Le Courtois et al. developed a genetic algorithm method to design arrangements on a discrete grid [Le Courtois 2016]. By including the variance of BW and MSL over multiple directions in the objective function, the resulting arrangement offers a less shift-variant PSF. A Monte-Carlo experiment revealed that the proposed genetic optimized arrangement can improve the separation of static and moving sources mainly at low frequencies compared to classical shapes (Star, single branch spiral). However, it was not compared to more advanced arrangements like multi-branch or Vogel’s spiral. The resolution improvement using the optimized geometry was additionally validated in a high-speed train pass-by experiment. Genetic optimization was also applied by Zhang in order to find an appropriate arrangement for train pass-by applications [Zhang 2018b]. The objective function to be minimized includes a weighted sum of the MSL and its variance over various frequencies. Solutions that result in a large BW were discarded from the genetic pool. Compared to two multi-arm spirals, the numerically optimized array did not achieve an improved MSL and BW, but combined the best properties of the two different arrays (see black line: Figure 17 from [Zhang 2018b]). The publications of the last decades in the field of vehicle pass-by reveal a development from regular to irregular or random arrangements, some of them numerically optimized. In addition, the number of sensors drastically increased which can be attributed to the constantly decreasing cost of measurement equipment.

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Figure 17: BW (a) and MSL (b) over frequency from [Zhang 2018b]. The black line indicates the genetic optimized array

3 INDUSTRIAL APPLICATIONS FOR ARRAY METHODS

OVERVIEW OUTSIDE RAILWAY APPLICATION On aircraft many noise sources, like engine, slats, flaps, and landing gears, have comparable strengths, especially during the landing phase. As a consequence, the reduction of aircraft noise requires a detailed knowledge of the locations and strengths of the different sources. For that purpose, experimental investigations on wind tunnel models and on actual flying aircraft, are essential to identify common noise sources, especially in landing phase with around 100 m altitude. Numerous experiments have been applied in Europe using microphone antenna for source directivity analysis [Siller 2002] or source contribution [Siller 2018], [Fleury 2011], [Lamotte 2009]. Several research works have also led to the improvement of techniques for the location [Sijtsma 2012] and especially quantification of sound sources with advanced processing [Guerin 2008]. The experiments in [Siller 2002] were aimed at testing the effects of modifications to the airframe and to one of the engines on the noise emission of the aircraft. The test set-up on the ground included a large phased array of microphones consisting of 238 relatively cheap electret microphones for the localisation of the noise sources on the flying aircraft (Figure 18). The sampling rate was 38 kHz to ensure high accuracy in the dedopplerisation process. The acquired data was

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analysed at different angles from 45 to 90° and processed by using two different methods to obtain the directivity:

• Dedopplerised frequency spectra require the computation of the power-spectral density of an unspecified number of dedopplerised microphone signals followed by an averaging over these microphones.

• Focused frequency spectra require a beamforming algorithm consisting of an averaging in the time domain of the dedopplerised time series of the phased array sensors and subsequent computation of the power-spectral density of the resulting averaged time series.

Figure 18: Directivity measurement for flyover: microphone array and results [Siller 2002]

The experiments in [Siller 2018] permits to compare flyovers in the same or in different plane configurations and ranking the individual sources according to their contribution to the overall sound pressure (Figure 19). Source maps were obtained from a hybrid deconvolution method. The total power in the whole integration area matches the power in the far-field spectra, apart from contributions from sources on the ground plane which are reduced by focusing the array on the aircraft. The data analysis was initially performed with a standard beamforming in the time domain adapted to moving sources, but was later improved by applying a hybrid deconvolution method which post-processes the beamforming maps with the point-spread function of the microphone array in the frequency domain.

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Figure 19 : Source contributions and overall level: Results from [Siller 2018]

[Lamotte 2009] reports results from simulations and different campaigns carried out by Airbus for aircraft external noise. Airbus has selected an optional 5 arms array geometry. The quantification method developed by EADS-IW based on NNLS is very efficient, but it is based on an assumption of uncorrelated monopole sources.

Figure 20: Investigation of engine noise through amplitude method on A340-300 [Lamotte 2009]

In the United States, the NASA Langley Research Center (LaRC) has a long history of successfully utilizing low-cost electret microphone arrays. In 2006, a 167- microphone array was deployed at the NASA Wallops Flight Facility (WFF) to conduct an extensive series of baseline airframe noise

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measurements on two Gulfstream aircraft. While useful measurements were obtained during the 2006 campaign, there were a number of issues with the operational performance of the array that led to the development of a completely new array design suitable for long-duration outdoor deployment [Humphreys 2016].

Figure 21: Large microphone arrays from [Humphreys 2016]

In Japan, [Hald 2012] has validated a Brüel & Kjaer system used for a series of flyover measurements on a Business Jet type MU300 from Mitsubishi Heavy Industries. The microphone array, applied during flyover, can be considered as small compared to the preceding applications. (The array diameter was 12 meters with 9 radial line arrays each with 12 microphones.) The work of Hald et al. compares the averaged power spectra of all array microphones with the source spectrum obtained from array processing. Due to the step of redopplerising the beamformed and deconvolved source spectrum, good agreement has been be achieved. In the automotive industry, the source separation techniques are used on test track in the configurations of current normative pass-by noise measurements using single sensors like ISO 362 requirements. There are several methods which can be found in the literature. Although the microphone array technologies for moving sources were applied successfully to locate and to analyse noise sources on airplanes and trains shown by numerous publications, only few car/automotive related studies exist. The typical microphone antenna distance is between 2 and 5 meters from the track. The half wheel geometry usually takes advantage of road reflection. Despite the relatively low speeds involved in the road context compared to the railway or flyover ones, the proximity of the track to the acquisition

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system implies a very short useable measurement time frame in order to avoid focusing the antenna at large tracking angles. This short duration makes the frequency domain analysis of signals difficult, with a poor frequency resolution and a non-negligible Doppler Effect. A second difficulty is that the sources are relatively close to each other, even practically confused regarding emission of the engine and rolling noise at front wheel level.

Figure 22 : Car pass-by noise measurement with small microphone array [Ballesteros 2015]

[Ballesteros 2015] has applied methods in the time domain or mixed ones with deconvolution in the frequency domain. Recently, [Cousson 2018] have evaluated Beamforming in the frequency domain in comparison to the CLEANT method based on synthetic data that was parameterised according to typical road vehicle pass-by scenarios. While the frequency method points out its limits for fast moving sources close to the antenna with spread localization, the new approach CLEANT yields very encouraging results, and is a clear improvement from the conventional beamforming, especially at low frequency. Applying it to a road vehicle in real-world conditions highlights a potentially troublesome behaviour of the method, and the solution brought by CLEANT's frequency filtered version, or by adapting its various parameters. Recently, an underwater pass-by measurement with hydrophone array has also been applied for a vessel [Oudompheng 2015]. The array arrangement was linear with frequency domain processing due to low speed. It permits to obtain the source contribution of a bow wave pattern with spilling and plunging breaker in real application.

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Figure 23: CLEANT results from car pass-by noise measurement [Cousson 2018]

OVERVIEW OF RAILWAY APPLICATIONS The issue of railway noise in the environment remains significant and its reduction, for rail transportation, is a priority notably for high speed trains. Due to the increasing transport traffic and speed over the past decades, the application of source separation methods in the railway context is of growing interest. The first large microphone arrays were deployed along the track to test beamforming on moving sources for high speed trains [Poisson 1996].

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The railway application has the particularity of considering only a few temporal samples because of the high speed of the train/sources and the generally short measurement distance of a few meters (3 to 10 meters). Due to the implied high Doppler Effect, the time domain analysis is commonly used [Bruhl 2000, Courtois 2012]. In some publications, advanced deconvolution methods were applied on source maps in combination with spatial integration over areas of interest to obtain an estimate of the source power [Courtois 2012], [Zhang 2018], [Kujawski 2020]. Due to the absence of a time averaging, the deconvolution results are fairly noisy [Courtois 2012]. Many publications ([Schulte 2003], [Poisson 1996]) are available in the literature about different applications mainly led by high speed train manufacturers and countries affected by their noise pollution. In Europe, several collaborative research projects in railway noise have contributed to method improvement and advanced applications. The METARAIL (Methodologies and Actions for Rail Noise and Vibration Control) project, completed in 1999, has developed and improved methodology, techniques and systems for measurement of railway including microphone array measurements [Dittrich 2000]. Measurements were performed at various speeds using a T-array with 48 microphones positioned at 2.7 m and 7.5 m from the track centreline, and using the moving focus technique. Interesting results were only obtainable at short distances and have shown that the emission level of the superstructure of a freight train travelling at around 80 km/h was at least 15 dB below that of the wheels and track (see Figure 24).

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Figure 24 : METARAIL results using a T-array with 48 microphones positioned at a distance of 2.7 m from the track.

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More recently, a study ([Thompson 2018]) performed in the Roll2Rail project has compared various methods, including beamforming and PBA, to achieve a separation of wheel and track noise with the accuracy needed for regulatory purposes, with minimum increase in costs for vehicle homologation and with the aim to improve the reproducibility of vehicle noise quantification. This objective remains in TRANSIT WP3. Following field tests carried out in June 2016, five separation methods have been applied to the recorded data. Three of these methods have been developed for this project:

• Beamforming with a microphone array (for the wheel component) together with track radiation models (which can also be applied separately for the track component)

• Wave Signature Extraction (WSE) method (for the rail component) • Advanced Transfer Path Analysis (for the track component)

Two other methods that were developed in previous EU projects have also been studied for comparison:

• Pass-By Analysis (PBA) method (for the track and wheel components) • Multiple Input Single Output separation (MISO) method (for the track component)

The measurement data were analysed in one-third octave bands and are mostly presented over the frequency range from 315 Hz to 5000 Hz. A spiral microphone array with 72 microphones at 8.25 m from the centre of the track was used to capture data for beamforming.

Figure 25: Microphone arrays for rail pass-by measurement [Thompson 2018]

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Separation of wheel from rail noise using beamforming was attempted based on the observation that the rail radiation is orientated at a certain angle to the rail and should therefore not be detected by an array directed towards the rail. Unfortunately, beamforming method did not sufficiently suppress the rail component and therefore did not give accurate results for the wheel in the current tests. Estimating the wheel component by “subtraction” of the track noise from the total noise was not allowed as a separation method in the Roll2Rail work, as it was found to be unreliable at frequencies where the wheel does not dominate the total noise.

Table 1: Wheel contributions estimated from microphone array measurements and track contributions estimated from rail vibration together with a radiation model for different

test speeds [Roll2Rail 2017].

Many other experiments have been conducted in Europe. Within the Deutsche Bahn ‘low-noise railway’ project [Schulte 2003], measurements with a spiral 96 microphone array were performed to identify noise sources from ICE high-speed trains in the 200–3150 Hz frequency range. Furthermore, noise source identification experiments on TGV trains moving at speeds from 250 km/h to 320 km/h were carried out in France with SNCF. The distribution of aerodynamic and wheel/rail rolling noise was analysed from beamforming [Mellet 2006]. [Poisson 2008] carried out noise source identification for TGV trains and arrived at the conclusion that the first bogie and the pantograph gradually became the main aerodynamic sound sources as the train speed increased. They also found that the contribution of the rolling noise remains important at more than 300 km/h. [Nagakura 2008] performed wind tunnel tests using a 1/5 scale Shinkansen train model and analysed the distribution of aerodynamic noise sources. One of the conclusions confirmed that the noise source at the front bogie of the leading car is much stronger than that at other bogies. Below 2000 Hz, the main sound sources are the bogies. Above 4000 Hz, the sound originates along the entire train height, especially at the first coach.

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The spiral-like microphone array was also used for the Japanese Shinkansen train and for noise source identification tests of Fastech 360S trains [Wakabayashi 2008]. When the speed of the trains reached 340 km/h, they also concluded that the maximum noise came from their wheels. All the source identification tests used the classic delay-and-sum beamforming technique, which is recognized for the identification of moving sound sources. [Koh 2014] also carried out a noise source identification of Korean high-speed trains. In the frequency range 2500–4500 Hz, the noise was found to be distributed along the train height. Another noise source identification of Korean high-speed trains was carried out by [Noh 2014]. In China, the noise generated by high-speed trains is also a sensitive issue, as high-speed lines are built in densely populated areas, where prior noise levels were very low. [Zhang 2018] and [He 2014] present the experimental analysis of the external noise produced by a Chinese high-speed train traveling on a deck at different speeds up to 390 km/h. The experimental set-up used the B&K system with beamforming for moving sources. The experiment and its analysis showed that the main noise produced by the train in the frequency range 500-5000 Hz originates in three areas: the bogies, the pantograph, confirming [Poisson 2008] and the inter-coach gaps of the train. The noise generated from the bogie areas includes the wheel/rail rolling noise, gear noise, and aerodynamic noise generated at the bogies under the carriages.

Figure 26 : Measurement setup from [Zang 2018] and [He 2014] for a Chinese high speed train

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Figure 27: Localisation result from [He 2014] for a Chinese high speed train

In 2012, quantitative processing based on deconvolution to characterise the whole acoustic sources on the railway stock were developed and tested [Le Courtois 2012]. A method was also proposed to extract acoustic contributions of the wheels and the rail. Using the radiation spectra of the elements, space-frequency domains are associated to the radiation of the wheels and the rail. The rail was then identified as a more important source.

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4 STATE OF THE ART: PBA-BASED METHOD

INTRODUCTION PBA stands for Pass-by Analysis, a term used for railway noise pass-by methods employing both trackside sound pressure and rail vibration measurements. Vertical rail vibration allows to derive combined roughness, and together with the sound pressure also a total rolling noise transfer function. The PBA method [Janssens 2006], developed originally in the STAIRRS project [Beer de 2002] in 2002, was also documented in the CEN Technical report CEN TR 16891: 2016 [CEN TR 16891] and applied in the Acoutrain [Acoutrain] and Roll2Rail [Roll2Rail] projects and several other previous ones. The conversion of a pass-by sound pressure level to a sound power level, as required for prediction models, is also addressed in the CEN draft standard on measurement methods for railway noise source terms [CEN TR 16891]. Environmental prediction models employ directional sound power terms (i.e. apparent sound power, sufficient to predict pass-by levels).

PBA-BASED METHOD FOR PASS-BY NOISE SEPARATION

4.2.1 Definitions The pass-by rolling noise level of trains Lp,roll in dB re 20 µPa can be decomposed into a rolling noise transfer function level LHpR,nl in dB re 20 Pa/√m (combination of track and vehicle, normalized to the number of axles Nax per unit vehicle length ℓveh) and the effective combined roughness level LR in dB re 1 µm:

𝑳𝑳𝒑𝒑,𝒓𝒓𝒓𝒓𝒓𝒓𝒓𝒓(𝑽𝑽,𝒇𝒇𝒕𝒕𝒓𝒓) = 𝑳𝑳𝑯𝑯𝒑𝒑𝑯𝑯,𝒏𝒏𝒓𝒓(𝒇𝒇𝒕𝒕𝒓𝒓) + 𝟏𝟏𝟏𝟏𝒓𝒓𝒓𝒓𝒍𝒍𝟏𝟏𝟏𝟏𝑵𝑵𝒂𝒂𝒂𝒂𝓵𝓵𝒗𝒗𝒗𝒗𝒉𝒉

+ 𝑳𝑳𝑯𝑯(𝝀𝝀(𝑽𝑽,𝒇𝒇𝟏𝟏)) (2)

where fto denotes the one-third octave band centre frequency and LR the total effective roughness. The roughness levels and pass-by levels are train speed dependent. The transfer function levels depend on the track and vehicle design but are independent of the train speed and the roughness levels. Together with the train speed, the roughness levels characterise the excitation of rolling noise at the wheel-rail contact patch. The ratios of the resulting trackside wheel and track noise components to the roughness excitation are expressed as transfer functions, which fully characterise the vibro-acoustic transmission of the vehicle or track. If the combined roughness level of the considered vehicle/track combination is known, the expected pass-by level can now be calculated for each desired speed, as illustrated by Figure 28.

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Figure 28: Illustration of the summation of a speed-dependant roughness level spectrum (left) and a speed independent transfer function level spectrum to obtain the pass-by level

spectrum. Transfer functions The combined transfer function for rolling noise LHpR,nl can be found experimentally by subtracting the total roughness level from the measured sound pressure level:

𝑳𝑳𝑯𝑯𝒑𝒑𝑯𝑯,𝒏𝒏𝒓𝒓(𝒇𝒇𝒕𝒕𝒓𝒓) = 𝑳𝑳𝒑𝒑,𝒓𝒓𝒓𝒓𝒓𝒓𝒓𝒓(𝑽𝑽,𝒇𝒇𝒕𝒕𝒓𝒓) − 𝟏𝟏𝟏𝟏𝒓𝒓𝒓𝒓𝒍𝒍𝟏𝟏𝟏𝟏𝑵𝑵𝒂𝒂𝒂𝒂𝓵𝓵𝒗𝒗𝒗𝒗𝒉𝒉

− 𝑳𝑳𝑯𝑯�𝝀𝝀(𝑽𝑽,𝒇𝒇𝟏𝟏)� (3)

Since the roughness is a function of a single wheel–rail contact but the sound levels depend on the spacing of the axles, the transfer functions must be normalized for the number of axles per unit vehicle length Nax/ℓveh, indicated by subscript nl. This normalisation allows for comparison of transfer functions independent from the number of axles and vehicle length. Effective roughness The wheel and rail roughness amplitude levels are expressed as one-third octave spectra with respect to wavelength λ. The so-called effective roughness is used. This is the roughness spectrum as if the total contact patch were represented by a single point, which means that the effective roughness already includes the averaging and ‘filtering’ effects of the wheel/rail contact patch. The combined effective roughness of wheel and rail can be derived directly from the operational measurement of vertical rail vibrations during train pass-bys [CEN TR 16891]. In order to be able to conduct this conversion the vertical decay rate of the track needs to be known. This can also be determined from the operational track vibration measurements during train pass-bys [CEN TR 16891].

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4.2.2 Pass-by noise separation The method uses the combined transfer function LHpR,nl to characterize the rolling noise. Theoretically this function is speed independent for any given vehicle/track combination. The key assumption is that any spectral deviations from the rolling noise transfer functions are due to other sources. This is illustrated in Figure 29 by an example of a low-frequency deviation resulting from aerodynamic sources and a mid-frequency deviation resulting from a traction source.

Figure 29: Indicative graph of deviation from the rolling noise transfer function due to other sources in different frequency regions.

To obtain a good estimation of the rolling noise transfer function, measurement of the pass-by levels and rail acceleration levels at a defined range of pass-by speeds is required. This allows to obtain required combined roughness levels over a larger wavelength domain and also averaging over multiple pass-bys. The measurement of pass-by levels for a range of speeds is also important if the non-rolling noise source level spectra are train speed dependent. The rolling noise transfer function can be determined operationally from pass-bys with predominantly rolling noise. It is anticipated to achieve this in different ways:

• Consider part of the vehicle pass-by without traction or low traction noise, or lower aerodynamic noise (e.g. middle part high-speed train);

• Consider a high-speed train at medium and low pass-by speeds, at which aerodynamic sources are negligible;

• Use other “rolling noise only” train types (e.g. coaches); • Consider a vehicle with high wheel roughness (or even wheel flats).

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The rolling noise transfer function can also be determined from stationary measurements on track and vehicle by hammer impact or reciprocal measurements. This is addressed in WP3 where this measurement technique will be used for vehicle/track separation. If the combined roughness level of the considered vehicle/track combination is known, the expected pass-by level due to purely rolling noise Lp,roll can now be calculated for each desired speed by equation (2). Next, the non-rolling noise source contribution can be assessed from the energy difference between (a selection of) the measured pass-by level Lptot and calculated rolling noise level part. Since this is potentially subject to large errors when total and calculated pass-by levels are similar, the following criterion is applied prior to the subtraction, keeping a constant energy sum of traction and rolling pass-by levels:

If Lp,tot(f) – Lp,rolling(f) ≤ 1: Lp,rolling(fi) = Lp,tot(fi) - 1 dB Lp,traction(fi) = Lp,tot(fi) - 7 dB (4)

A comparison to the measured total pass-by level at the considered speeds reveals the spectral contributions of other sources, see Figure 30.

Figure 30: Indicative contributions of other sources in different frequency regions to the overall pass-by level.

4.2.3 Illustration of method In this section, the proposed method is applied to two different datasets, on a high-speed line and a TSI compliant ballasted track. Only the contribution of the non-rolling noise sources to the pass-

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by level is separated from the rolling noise, so no sound power levels and directivity are obtained. These cases illustrate that the pass-by noise separation works for cases with a high track contribution, such as tracks with soft rail pads, and for a high wheel roughness. This could only be achieved with high source levels of the other sources. For cases with lower source levels it is expected that lower pass-by speeds are required to obtain sufficient signal-to-noise ratios for rolling noise.

Separation of aerodynamic noise A first attempt to separate aerodynamic noise was made by [Beer de 2002]. Only three pass-bys at two different speeds were available for the separation (160 and 300 km/h). A larger range of pass-by speeds with repeated runs is desirable and is required in the TRANSIT high speed measurement campaign. The first case is a high-speed Thalys train on a high-speed slab track. The slab track noise is shown to have a high contribution to the rolling noise due to its very soft rail supports. The left figure in Figure 31 shows the large low frequency deviation from the total transfer function obtained operationally from one high-speed pass-by. The mid-speed pass-by at 160 km/h was taken as the rolling-noise transfer function. The right figure shows the spectral contribution of aerodynamic sources to the high-speed pass-by level. For the Thalys at 300 km/h on Rheda slab track, rolling noise still dominates the A-weighted total noise level. The lower frequency range is dominated by aerodynamic noise (86 dB(A) in the frequency range up to 400 Hz).

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Figure 31: Total transfer function levels near the Rheda track and a ballast track, measured with various train types, as a function of frequency in third-octave bands (left). Measured and calculated pass-by level of the Thalys on a Rheda high speed track at 300 km/h as a function of frequency in third-octave bands (right).

Separation of traction noise The next example is a stopping train on a TSI-compliant track. This Dutch rolling stock (MAT’64) has been taken out of service since 2015. The train had cast iron brake blocks resulting in high wheel roughness levels. Therefore, this rolling stock was known to have a relatively high pass-by noise levels. The train was driven by four traction motors located in the two centre bogies; the front and rear bogies were not powered. This is visible from the spectrogram of the pass-by at a speed of 113 km/h, see Figure 32. The tonal component is the gear meshing frequency of the gear pinion driving the axle. It controls the overall A-weighted pass-by level, see right Figure 33. The pass-by level of a quieter type of rolling stock (intercity VIRM) was used to estimate the rolling noise transfer function, see left Figure 33. At 800 Hz a peak is visible in the transfer function. When compared to

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the VIRM transfer function, it can be related to non-rolling noise sources. Via the combined roughness levels, the pass-by level of MAT64 due to rolling noise only was assessed and compared to the measurements, see right Figure 23. The contribution of the gear-meshing to the overall pass-by level is 94 dB(A) and exceeds rolling noise by 2 dB(A).

Figure 32: Photograph of the Dutch MAT’64 rolling stock (top), spectrogram of the sound pressure (centre) and vertical rail acceleration (bottom) of a pass-by at 113 km/h.

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Figure 33: Measured combined transfer function for MAT64 and VIRM as a function of frequency in third-octave bands (left). Measured (rolling+traction) and calculated (rolling) pass-by levels of MAT64.

SOUND POWER LEVELS AND DIRECTIVITY In the previous section it was described how the sound pressure level of a source on a train can be determined as an average over the pass-by time tp. As a next step this pass-by level is to be converted into a sound power level, including a directivity index. The sound power and directivity of railway noise sources is required either as input for acoustic simulation models or for environmental prediction models. Only the part of the sound power is needed that is relevant for the trackside pass-by levels at different positions. The procedures to determine source sound power levels for environmental prediction models is being addressed in the CEN working group on railway noise sources terms [CEN TR 16891].

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The sound power level of sources on a train can be at different heights and have different directivities, depending on the type of source. The instantaneous sound pressure Lp,i due to a single source is related to the directional sound power LW0,dir of the sound source and the geometrical divergence Adiv and ground attenuation Aground at a given angle in third octave bands i:

Lp,i = LW0,dir,i – Adiv,i – Aground,i (5)

For a moving source with known sound power, the sound pressure can be obtained by integration over the whole view angle of the pass-by, as long as the attenuation terms and source directivity are known. For environmental models, generally a sound power per unit length is required. The geometric divergence is calculated as for a point source. The combined attenuation can be measured with a calibrated omnidirectional loudspeaker source or by calculation models. If a model is used for this, it should include the direct path, the indirect path and for some site geometries, also the diffraction path, as shown in Figure 34:

1. Direct; 2. Reflected; 3. Ballast diffracted and reflection.

Figure 34: Three sound paths for pass-by noise When determining sound power from pass-by sound pressure measurements, the inverse calculation is applied. In particular, when the Lpeq,tp is used, an overall average for the sound power or sound power per unit length is obtained. For the purpose of environmental models the sound power per unit length LW’' is determined from the pass-by sound pressure level Lpeq,tp with a pre-calculated (or measured) attenuation transfer function Hi:

LW’' = Lpeq,Tp,i + Hi (6)

Hi is an integration over geometrical and ground attenuation, taking into account the directivity.

1

2 3

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The transfer function for combined roughness to sound power, as required for the CNOSSOS model for the rolling noise source, can be derived from the PBA rolling noise transfer function and the acoustic transfer function Hi written in the form:

LHWR,n,i = LHpR,nl,i – Hi (7)

So in order to determine sound power over a pass-by, this acoustic transfer function Hi is required. In the scope of the CEN work on source terms, the rolling noise is considered as monopole sources for the wheels and a coherent line source for the rail. However, most prediction models assume only point sources and point-to-point calculation models. A calculation model was developed by C. Jones for this purpose, but any implementation would be allowable as long as it includes the following:

• Directivity index or omnidirectivity; • Non locally reacting, spherical reflection with complex impedance from flow resistivity of

ground; • Fresnel Zone to determine area influencing ground or ballast reflection; • Diffraction over an absorbing wedge.

See Figure 35 for an example on the model inputs.

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Figure 35: Screen shot of a calculation model for the transfer functions Hi. It is not necessary to include diffraction for some standard geometries, in particular for measurement over ballast (far track) and at a 3.5 m microphone height. But, in particular, embankments sloping upwards are to be avoided as they may introduce large uncertainty. In environmental models, directivity is predefined and often given as a dipole-like characteristic, which then leads to sound power level adjusted for this. This does not have to be the case for industrial simulation models for rail vehicles. Directivity can only be measured for a single dominant source type, and is potentially complex due to presence of multiple sources and their movement. Consequentially, it is probably not feasible to determine detailed directivity patterns. But it should be evaluated to what extent the basic directivity types, such as monopole, dipole or line source, can be verified for dominant sources.

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This means that measurement procedures should be designed to determine directivity under suitable conditions, for example at specific speeds and with highest level for the required source. It is also to be expected that there will be frequency range limitations, meaning that the directivity may be determined in part of the range but not the whole range. One of the methods to be examined for directivity will be by use of spectrograms of pass-by data at different microphone positions at different distances and height. For traction noise, also dead slow pass-bys will be analysed to evaluate applicability for directivity analysis. For rolling noise, also spectrograms of both sound pressure and of transfer functions will be evaluated, which was first tested in the Roll2Rail project [Roll2Rail]. For aerodynamic noise, measurement at several speeds will be required, under the condition that it is dominant, for one or more parts of the train. Finally, the source position is important for the analysis. It is normally assumed to be at the position of the component concerned, and may also be evaluated by array measurements for example. However, this can be difficult for multiple sources, including the rolling noise, which includes wheels/rails and sleepers. Although the track and vehicle components are clear sound radiators, the sound field underneath the vehicle may result in a different effective source position when measured from beside the track.

EXPERIMENTAL MEANS AND PROCEDURES

4.4.1 Experimental means The measurement set-up is limited and allows a rapid in-service measurement. It consists of the following at a single cross-section, see also Figure 36.

• a vertical accelerometer underneath both rails of the track. The accelerometer is mounted underneath the middle of the rail foot, near a sleeper.

• two trackside microphones at 7.5 m distance from the centre of the track and 1.2 m and 3.5 m height above the rail surface,

• one microphone at larger distance, 10-25 m. The best location will be determined during the TRANSIT project based on the measurement campaign results. This position is intended is for checking directivity and/or for deriving sound power.

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Figure 36: Overview measurement set-up for PBA-based pass-by source separation. For sound power estimation, the site acoustic transfer function will be required, to be obtained either by measurement with a loudspeaker source, or by calculation, assuming a basic directivity profile, or predefined tabulated data.

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4.4.2 Procedures This section provides a first outline of a measurement procedure for pass-by source separation in the context of TRANSIT. A flow chart of the procedure is drawn in Figure 37 and further described below.

Figure 37: Flow chart of pass-by source separation. A. Record pass-by levels p(t) and vertical rail accelerations a(t).

The required range of pass-by speeds depends on the type of rolling stock that is considered. A suggested selection is set out in Table 2 . For the required speeds according to the TSI procedure, 3 pass-bys are needed to check consistency. The total number of pass-bys is 12, which may be possible to perform within one day.

Table 2: Target number of pass-bys for each indicated speed for high-speed and

conventional trains Train speed [km/h]

<10 40 60 80 100 120 140 160 200 240 280 High-speed - - - 3 - 1 - 3 1 1 3 Conventional 1 1 1 3 1 1 1 3 - - -

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The suggested speeds may be modified depending on the findings of the measurement results of the TRANSIT measurement campaigns.

If the rolling noise transfer function is to be assessed from other (in-service) vehicles they need to be recorded as well at their operational speeds.

The pass-by levels Lpeq,tp and the indirect combined roughness levels Lr,tot of (parts of) the pass-by are determined from the sound pressure and vertical rail acceleration time signals by the PBA procedure. The track decay rate that is required for this is also determined by the PBA procedure.

B. Select a method to estimate the combined rolling noise transfer function LHpR,roll of the test vehicle on the test track.

There are various ways to estimate the combined rolling noise function. These will be further investigated in the TRANSIT measurement campaigns.

Stationary method:

• stationary measurements on track and vehicle by either hammer impact on the rail or reciprocal by irradiation of the track by a loudspeaker. This will be elaborated further in WP3 / D3.1. It requires separate measurements of the track and a stationary vehicle on the track.

Pass-by methods:

• Using part of the test vehicle pass-by without traction or low-traction noise, or lower aerodynamic noise (e.g. centre part high-speed train);

• Using a high-speed train at mid and low pass-by speeds, when aerodynamic sources are absent, see also Table 2;

• Using other “rolling noise only” train types (e.g. coaches), potentially also from other service traffic at available speeds only;

• Using a vehicle with high wheel roughness (e.g. wheel flats).

The total rolling noise function, if taken from operational pass-bys, is determined according to equation (3).

C. The combined roughness level Lr,tot of the test vehicle and test track are given by the PBA procedure.

D. The total combined transfer function LHpR,tot, containing rolling noise and other source contribution, is obtained from PBA, using: the whole pass-by signal tp1, only a selection

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around source locations tp2, or a selection around a single source tp3, as illustrated by Figure 38. The choice of averaging time will affect the SNR of the sound pressure level of a source, especially for the case in which one local sound source is spread out of the entire train length.

Figure 38: Illustration of different selections of the pass-by around source locations on a train.

E. Determine the pass-by level due to rolling noise only Leq,tp,roll by adding the combined

roughness level to the rolling noise function, according to equation (2).

F. Determine the total pass-by level Leq,tp,total of a selection of the pass-by by adding combined roughness levels to the transfer function over the selected part; this step can be skipped if a whole pass-by is considered;

G. Compare the total measured pass-by level spectrum with the rolling-noise only pass-by level spectrum and derive the spectral contribution due to other non-rolling sources Leq,tp,source, following the same criteria than in chapter 4.2.2.

H. Determine transfer function for sound power Hi from calculations or measurements.

I. Convert the pass-by level due to other source(s) to a sound power or sound power per meter vehicle LW’’ according to equation (5).

J. Convert the sound power level per meter vehicle LW” to the sound power level LW of a point source.

K. Directivity assessed from spectrograms of multiple microphones.

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5 REQUIREMENTS AND STRATEGY

REQUIREMENTS The requirements given below have been discussed with FINE2 and TRANSIT project partners. These are summarized in the table in Annex A. Future methods developed by TRANSIT will be carefully designed to provide input data to noise simulation tools. As confirmed by FINE2 and the industry partners, the scope of application will be limited to the prediction of external noise. In addition, the previous state of the art review has revealed that so far no (microphone array) procedure exists that has been used for source separation and exterior noise simulation. It implies that the experiments carried out in TRANSIT will become the first reference in that domain and any comparisons to previous work won’t be possible. The requirements are relatively open for pass-by and it is expected to obtain guidelines in the future from the TRANSIT project. It has been especially discussed to have guidelines for:

• the output accuracy, • the reproducibility, • the measurement constraints: equipment, cost, time. • the usable frequency range.

For the frequency range, the Roll2Rail project has worked in the 315-5000 Hz frequency range for rolling noise. Other sources such as equipment could be important below 315 Hz. And it is asked to go above 5 kHz in WP3 dedicated to rolling noise for this project. The Acoutrain project has pointed out that the directivity is more important at high frequencies. Regarding the output of the methods to be developed, the requirements are defined according to the current input used for simulation tool:

• The data can be calculated in third octave bands, the usual spectral accuracy in simulation tools (CNOSSOS)

• The source directivity should include vertical and horizontal data. Furthermore, the directional information may be used to decide about possible approximation by standard radiation patterns (e.g. monopole, dipole)

• The sound power is a third octave band spectrum: any tonal analysis is not considered reliable because of the short time interval to observe sources during pass-by.

It is required to propose improvements and benefits in comparison to separation techniques used in the past. The source directivity over multiple dimensions (horizontal/vertical) is a new data which has not previously been quantified from pass-by measurement in railways. It is necessary for assessing the source strength that is required for the use of simulation tools. Accuracy for the

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overall noise reconstruction and measurement reproducibility are relatively open in such a context of a new application. It has been requested to obtain +/- 3dB per third octave band for rolling noise in the frequency range [315-5000] Hz. The same objective with 3 pass-by measurements should be applicable. But the methodologies which will be developed must be compatible with TSI requirements:

• The noise separation shall work for different pass-by speeds between 40 km/h and 320 km/h

• The method shall be applicable to the following vehicle types: EMU, DMU, Locomotive, Highspeed, single and double deck trains

• The method shall be applicable to TSI-compliant tracks

Some of the desirable requirements will be confirmed after methodology developments and test:

• LpAeq,Tp, train speed and pass-by time to be provided according ISO 3095:2013 • The method shall be applicable to any type of track (ballast, slab,..) • The method shall also be applicable to the following vehicle types: Metros and trams

Some of the desirable requirements will not be met within the project:

• Validation of the track influence: only one track will be used for a given vehicle, • Repeatibility: the same vehicle will not be measured on several tracks.

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STRATEGY

5.2.1 Method synthesis The previous sections have described array methods related to the analysis of noise sources on moving vehicles and their application in an industrial context. In general, three different types of source separation methods can be found. These are spatial filtering methods (Beamforming), deconvolution methods and inverse methods. Each of those categories have representatives that are performed in time or frequency domain. Despite the large number of existing algorithms, none can meet all the requirements summarised in previous section. All frequency domain methods suffer from the fact that the Doppler frequency shift present in the signals is not constant over time and is of different magnitude at each receiver. All dedopplerisation approaches in the frequency domain require a very short time window for evaluation, depending on the speed of the source. In fact, the resulting frequency resolution can be considered as small and uncertainties are to be expected when the source velocity is high, as is the case with high-speed trains. In contrast, time domain methods such as the CLEANT method enable a better de-dopplerisation by resampling the time signals and are therefore more suitable for use at high speeds. All methods, except the Beamforming methods, provide an estimate of the sources’ individual strengths. However, omnidirectional radiation is assumed in most of the algorithms leading to misinterpretations of the results for directional sources. Only the SODIX method yields information about the radiation characteristics, but has only been used for stationary sources so far. It can be concluded that the use of a time domain procedure promises the best chances of success. An accurate dedopplerisation is possible and it is assumed that this provides reliable results even for high-speed trains. A new procedure to determine the source directivity has to be found as no current method applicable to moving sources can provide this information. Regarding array geometry design, no advantage has been proven in the literature from the use of numerical optimization methods over parametric approaches. Spiral geometries, especially Vogel’s spiral, provide good resolution properties and therefore shall be used for data acquisition during the measurement campaigns.

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M

etho

ds

Sour

ce

Mod

el

Dire

ctiv

ity

estim

ates

Soun

d po

wer

es

timat

es

Freq

uenc

y re

solu

tion

Unc

erta

int

ies

Pass

-by

appl

icat

ion

PBA-related

Monopole horizontal plane only

Yes 1/3 octave

Difficult TF estimation at low frequencies due to uncertainties in track decay rate estimation

Railway

BF (freq.)

BF-MS, Hybrid BF

Monopole No No Low difficult source separation at low frequencies, issues with high source speeds

Railway, Aeronautics, Marine, Automotive

BF (time)

Delay-and-sum BF

Monopole No No Higher difficult source separation at low frequencies


Deconv (Freq.)

DAMAS-MS, DAMAS2, VMF-B

Monopole No Yes Low issues with high source speeds


Deconv (Time)

CLEANT

Monopole No Yes Higher issues with distorted array-source distance and additional noise

Railway Automotive

Inverse (freq)

CMF, SODIX

Mono/di/quardipole

SODIX: Yes,

Yes Low never applied to linear moving sources

Rotating sources

Inverse (time)

MFAH ESM time domain

Monopole Yes Yes Low only applied to single moving sources yet Very unstable, Issues with high source speed

MFAH applied to experimental moving sources

Others SPI Monopole Directivity compensation

Yes Like BF only applied to single moving sources yet

Railway, Aeronautics

Table 3 : state of the art summary

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5.2.2 Improvement proposal for array methods

Item

Approach

1.Array data acquisition

Collecting array data from vehicle pass-by: - planar microphone array parallel to the track (<10m); - Use of a modern array geometry (Vogel’s spiral) with at least 64 sensors; - local tracking of the train trajectory via light barriers; - measurement of pass-by of vehicles with different speeds;

2. Procedures to separate and determine strength of individual sources

• spatial filtering and de-dopplerization in time domain to separate sources in vehicle area;

• Deconvolution in time domain (CLEANT) to remove influence of the PSF and to improve spatial identification of source contributions.

• spatial integration of contributions to obtain source strength for each interesting source area

• obtain the results for each third octave band

3. Procedures to determine directivity patterns

Approach 1: coupling results from previous step with techniques to determine the directivity (Evaluation of the deconvolved source contributions over different observation angles) Approach 2: definition of a new method capable of determining the radiation pattern of the sources:

• identify source areas from deconvolved beamforming results • replace source areas by an equivalent sound source • simulate pass-by with equivalent sound sources • relate simulated SPL to measured SPL at reference position • minimizing the difference by adjusting radiation characteristics or by

choosing an appropriate source model

Table 4 Overview of strategy for microphone array method development

The summary of the methods developed so far and their application in an industrial context reported in Table 3 have shown that with microphone arrays it is possible to estimate the strength of moving point sources. The next step is the development of a method that additionally captures information about the directional properties of sources from measured array data during the measurement campaigns. The experimental campaigns which will permit to validate the method are:

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- Alstom hybrid-train at Velim: with mid-speed pass-bys; - Talgo train on high-speed line: with high-speed pass-bys; The strategy for developing this method is summarized in Table 4 and a Flowchart of a potential procedure is given below.

A. The procedure will make use of data generated by measurements of the Talgo train on the high-speed line and the Alstom hybrid train at different speeds of up to 280 km/h. A planar array will be used to separate the sources in horizontal and vertical direction. The geometry will be a modern Vogel’s spiral investigated in [Sarradj 2016], which has not yet been used for train pass-by measurements. A distance to the track of about 10 m should be a compromise between the performance of spatial separation at low frequencies, the length of the tracking window and the strength of the Doppler effect.

B. Beamforming in the time domain will be used as a method for spatially separating individual source signals. The Doppler effect will be removed by applying a conventional de-dopplerization method by moving the focus grid with the trajectory and resampling of the time data.

C. The application of an appropriate deconvolution method (e.g. CLEANT) enables to remove the influence of the PSF.

D. To address the problem of finding the unknown radiation pattern of each source, different approaches are conceivable to be examined in the upcoming work package. One approach may consist of coupling the results obtained from deconvolution with an existing principle to determine the directivity (e.g. [Poisson 1996]). As an alternative, a new procedure may be developed that replaces the source areas obtained by the array method by equivalent sources. The sound pressure resulting from the simulation with a certain equivalent source constellation can then be related to the measured sound pressure. A minimization of the error could be achieved by adjusting the directivity accordingly or by finding an appropriate source model for the equivalent sources.

E. The assignment of individual contributions on the scan-grid to a source region can be achieved through spatial integration.

F. Finally, the method provides the directivity (a) and sound power (b) for each evaluated source region.

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Figure 39: Flowchart of a potential procedure to obtain both directivity and sound power of a source region on a moving train.

5.2.3 STRATEGY FOR PBA-based SOURCE SEPARATION The previous state of the art section has illustrated how PBA can be applied to pass-by source separation and can estimate the pass-by level of a source on a train as an average over (selected) pass-by time. The next steps to obtain a point source sound power level with a directivity index, contain uncertainties that require attention. These items are listed in Table 5. It is also emphasized in which upcoming TRANSIT measurement campaigns [TRANSIT D5.1] the items will be

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addressed. Besides the TRANSIT measurement campaigns, TNO will perform in-house measurements on a dedicated test track prior to these campaigns to address the transfer function for sound power estimation.

Item Experimental campaigns Approach 1 Rolling noise function

estimation Metro de Madrid test train: - Powered/unpowered bogies; Talgo high-speed line: - high-speed and low-speed pass-bys.

Evaluate best approach for LHpr,roll :

- part of the vehicle pass-by without source; - high-speed train at low pass-by speeds; - “rolling noise only” train types; - vehicle with high wheel roughness. Compare with stationary measured LHpr,roll.

2 Speed-range for high-speed and conventional trains pass-bys.

Metro de Madrid test train/Talgo train on high-speed line/Alstom hybrid-train at Velim: - Wide range of pass-by speeds.

Start averaging transfer functions over full range of pass-by speeds, decrease number of speeds down-to TSI speeds and define impact.

3 Measurement procedures to determined directivity patterns, basic directivity types such as monopole, dipole or line source.

Metro de Madrid test train: - Point source with known directivity and source level; - dead slow pass-bys Alstom hybrid-train at Velim: - dead slow pass-bys

Use of spectrograms of pass-by data at different microphone positions

4 Acoustic transfer function for sound power estimation, best microphone positions.

TNO test track experiments: Source with known source level and directivity at various heights from the track, transfer to track-side microphones with varying distance and height.

Compare to calculation model results for different source height/microphone position combinations.

Table 5: Overview of strategy for PBA-based source separation

1. The vibro-acoustic combined rolling noise transfer function for vehicle and track can be estimated by using PBA techniques. This implies combining track-side pass-by levels with indirect

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effective combined roughness levels. In order to ensure dominance of rolling noise, this can be applied on different levels, either the entire or selected parts of the test vehicle, or another rolling-noise only vehicle on the same track.

2. The balance between rolling noise and the noise of other sources depends on train speed and combined roughness levels. Therefore it is required to have multiple pass-bys at various speed ranges of the test vehicle. Further definition of the minimal speed ranges is required for both high-speed trains and conventional trains. 3/4. The acoustic transfer function Hi , which converts a pass-by level into a sound power level, can be measured with a sound source or calculated from a dedicated model. The best track-side receiver positions for varying source positions, in view of a stable transfer function, need to be addressed. In order to calculate this acoustic transfer function, yet unknown, source directivity needs to be put into the model. Therefore, measurement procedures to determine directivity under suitable conditions need to be defined. Spectrograms of pass-by data of various microphones, including dead slow pass-bys, are anticipated to contribute to this. The measurement programmes for the upcoming TRANSIT measurement campaigns have been carefully designed in order to obtain a valuable data-set, to address the open points in the PBA-based approach to pass-by source separation.

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[Sijtsma 2012] P Sijtsma. Acoustic beamforming for the ranking of aircraft noise. Technical report, NLR, 2012. [SILLER 2002] H.A. Siller and U. Michel,BUZZ-SAW NOISE SPECTRA AND DIRECTIVITY FROM FLYOVER TESTS, AMERICAN INSTITUTE OF AERONAUTICS AND ASTRONAUTICS PAPER 2002-2562 [Siller 2017] Siller, H., König, J., Funke, S., Oertwig, S., Hritsevskyy, L. – Acoustic source localization on a model engine jet with different nozzle configurations and wing installation – 2017. Int. J. Aeroacoust. (16(4–5), pp. 403–417) [SILLER 2018] H.A. Siller - Wolfram Hage - Timo Schumacher, Source localisation on aircraft in flight - new measurements with the DLR research aircraft Airbus 320 ATRA BeBeC 2018 [TRANSIT D5.2] Thompson, D. ea, Measurement plan, D5.2 TRANSIT, June 2020. [Underbrink 2001] Underbrink, J. R. – Circularly symmetric, zero redundancy, planar array having broad frequency range applications – 2001. Pat. US 6,205,224 B1. [Underbrink 2002] Underbrink, J. R. – Aeroacoustic Phased Array Testing in Low Speed Wind Tunnels – 2002. Aeroacoustic Measurements. Ed. by Thomas J. Mueller. Berlin: Springer. Chap. 3, pp. 98–217 [Vogel 1979] Vogel, H. – A better way to construct the sunflower head – 1979, Mathematical biosciences, (vol. 44(3), pp. 179–189). [Wakabayashi 2008] Wakabayashi, Y., Kurita, T., Yamada, H., et al., 2008. Noise Measurement Results of Shinkansen High-speed Test Train (FASTECH360S, Z). Noise and Vibration Mitigation for Rail Transportation Systems. Springer Berlin Heidelberg, p.63-70. [Yardibi 2008] Yardibi, T et al. – Sparsity constrained deconvolution approaches for acoustic source mapping. – 2008, The Journal of the Acoustical Society of America, (vol. 6(4), p. 2631-2642). [Zea 2017 ] E Zea, L Manzari, G Squicciarini, L Feng, DJ Thompson, and Ines Lopez Arteaga. Wavenumber-domain separation of rail contribution to pass-by noise. Journal of Sound and Vibration, 409:24–42, 2017.

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[Zhang 2015] Xiao-Zheng Zhang, Chuan-Xing Bia, Yong-Bin Zhang, and Liang Xu, “Sound source identification and sound radiation modeling in a moving medium using the time-domain equivalent source method” The Journal of the Acoustical Society of America 137, 2678 (2015); [Zhang 2017] Jin Zhang, Giacomo Squicciarini and David Thompson - Beamforming approaches for railway noise source identification. First International Conference on Rail Transportation, July 2017, Chengdu, China [Zhang 2018] Jie Zhang , Xinbiao Xiao , Dewei Wang, Yan Yang, and Jing Fan, Hindawi, Source Contribution Analysis for Exterior Noise of a High-Speed Train: Experiments and Simulations Shock and Vibration Volume 2018, Article ID 5319460, 13 pages [Zhang 2018b] Zhang, J. – Implementations of microphone arrays for railway noise identification – 2018. (Doctoral dissertation, University of Southampton) [Zhang 2019] Jin Zhang ∗, Giacomo Squicciarini, David J. Thompson. – Implications of the directivity of railway noise sources for their quantification using conventional beamforming – 2019, JSV (459) [Zhang Xuetao] “Applicable Directivity Description of Railway Noise Sources”, PhD Thesis, Chalmers University of Technology, Göteborg, Sweden (2010)

https://asa.scitation.org/author/Zhang%2C+Xiao-Zheng

https://asa.scitation.org/author/Bi%2C+Chuan-Xing

https://asa.scitation.org/author/Zhang%2C+Yong-Bin

https://asa.scitation.org/author/Xu%2C+Liang

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APPENDICES

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ANNEX A: REQUIREMENTS

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Contract No. 881771

Contract No. 881771 TRAIN PASS-BY NOISE SOURCE ...

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