Method for obtaining the wheel-rail contact location and its application to the normal problem calculation through CONTACT

[EN] This work presents a robust methodology for calculating inter-penetration areas between railway wheel and rail surfaces, the profiles of which are defined by a series of points. The method allows general three-dimensional displacements of the wheelset to be considered, and its characteristics make it especially suitable for dynamic simulations where the wheel-rail contact is assumed to be flexible. The technique is based on the discretization of the geometries of the surfaces in contact, considering the wheel as a set of truncated cones and the rail as points. By means of this approach, it is possible to reduce the problem to the calculation of the intersections between cones and lines, the solution for which has a closed-form expression. The method has been used in conjunction with the CONTACT algorithm in order to solve the static normal contact problem when the lateral displacement of the wheelset, its yaw angle and the vertical force applied in the wheelset centroid are prescribed. The results consist of smooth functions when the dependent coordinates are represented as a function of the independent ones, lacking the jump discontinuities that are present when a rigid contact model is adopted. Example results are shown and assessed for the normal contact problem for different lateral and yaw positions of the wheelset on the track.This work was supported by the financial contribution of the European Union’s Shift2Rail programme (RUN2Rail project, grant number 777564), the Spanish Ministry of Economy, Industry and Competitiveness and the European Regional Development Fund (projects TRA2013-45596-C2-1-R and TRA2017-84701-R).Baeza González, LM.; Thompson, DJ.; Squicciarini, G.; Denia, FD. (2018). Method for obtaining the wheel-rail contact location and its application to the normal problem calculation through CONTACT. Vehicle System Dynamics. 56(11):1734-1746. https://doi.org/10.1080/00423114.2018.1439178S173417465611Garg, V. K., & Dukkipati, R. V. (1984). Wheel–Rail Rolling Contact Theories. Dynamics of Railway Vehicle Systems, 103-134. doi:10.1016/b978-0-12-275950-5.50009-2Wickens, A. H. (1965). The dynamic stability of railway vehicle wheelsets and bogies having profiled wheels. International Journal of Solids and Structures, 1(3), 319-341. doi:10.1016/0020-7683(65)90037-5DE PATER, A. D. (1988). The Geometrical Contact between Track and Wheelset. Vehicle System Dynamics, 17(3), 127-140. doi:10.1080/00423118808968898Yang G. Dynamic analysis of railway wheelsets and complete vehicle systems (PhD thesis). Delft: Delft University of Technology; 1993.Negretti, D. (2012). A third-order approximation method for three-dimensional wheel–rail contact. Vehicle System Dynamics, 50(3), 431-448. doi:10.1080/00423114.2011.595804Shabana AA, Zaazaa KE, Escalona JL, et al. Modeling two-point wheel/rail contacts using constraint and elastic-force approaches. In: Paidoussis MP, editor. ASME 2002 International Mechanical Engineering Congress and Exposition; 2002 Nov 17–22; New Orleans, Louisiana: American Society of Mechanical Engineers, Rail Transportation Division (Publication) RTD, p. 35–50.Netter, H., Schupp, G., Rulka, W., & Schroeder, K. (1998). NEW ASPECTS OF CONTACT MODELLING AND VALIDATION WITHIN MULTIBODY SYSTEM SIMULATION OF RAILWAY VEHICLES. Vehicle System Dynamics, 29(sup1), 246-269. doi:10.1080/00423119808969563Pombo, J., Ambrósio, J., & Silva, M. (2007). A new wheel–rail contact model for railway dynamics. Vehicle System Dynamics, 45(2), 165-189. doi:10.1080/00423110600996017Polach, O. (2010). Characteristic parameters of nonlinear wheel/rail contact geometry. Vehicle System Dynamics, 48(sup1), 19-36. doi:10.1080/00423111003668203Santamaría, J., Vadillo, E. G., & Gómez, J. (2006). A comprehensive method for the elastic calculation of the two-point wheel–rail contact. Vehicle System Dynamics, 44(sup1), 240-250. doi:10.1080/00423110600870337Cuperus, J. L., & Venter, G. (2016). Numerical simulation and parameterisation of rail–wheel normal contact. Proceedings of the Institution of Mechanical Engineers, Part F: Journal of Rail and Rapid Transit, 231(4), 419-430. doi:10.1177/0954409716631009Chollet, H., Sébès, M., Maupu, J. L., & Ayasse, J. B. (2013). The VOCO multi-body software in the context of real-time simulation. Vehicle System Dynamics, 51(4), 570-580. doi:10.1080/00423114.2013.768771Pascal, J.-P., & Soua, B. (2016). Solving conformal contacts using multi-Hertzian techniques. Vehicle System Dynamics, 54(6), 784-813. doi:10.1080/00423114.2016.1161201Piotrowski, J., & Chollet, H. (2005). Wheel–rail contact models for vehicle system dynamics including multi-point contact. Vehicle System Dynamics, 43(6-7), 455-483. doi:10.1080/00423110500141144Vollebregt EAH, Weidemann C, Kienberger A. Use of “CONTACT” in multi-body vehicle dynamics and profile wear simulation: initial results. in: S. Iwinicki (Ed.) 22nd International Symposium on Dynamics of Vehicles on Roads and Tracks (IAVSD2011), Manchester: Manchester Metropolitan University; 2011.Liu, B., Bruni, S., & Vollebregt, E. (2016). A non-Hertzian method for solving wheel–rail normal contact problem taking into account the effect of yaw. Vehicle System Dynamics, 54(9), 1226-1246. doi:10.1080/00423114.2016.1196823Kalker, J. J. (1990). Three-Dimensional Elastic Bodies in Rolling Contact. Solid Mechanics and Its Applications. doi:10.1007/978-94-015-7889-9Pombo, J., & Ambrosio, J. (2005). A computational efficient general wheel-rail contact detection method. Journal of Mechanical Science and Technology, 19(S1), 411-421. doi:10.1007/bf02916162Kaiser, I., & Popp, K. (2006). Interaction of elastic wheelsets and elastic rails: modelling and simulation. Vehicle System Dynamics, 44(sup1), 932-939. doi:10.1080/00423110600907675Falomi, S., Malvezzi, M., & Meli, E. (2011). Multibody modeling of railway vehicles: Innovative algorithms for the detection of wheel–rail contact points. Wear, 271(1-2), 453-461. doi:10.1016/j.wear.2010.10.039Meli, E., Magheri, S., & Malvezzi, M. (2011). Development and implementation of a differential elastic wheel–rail contact model for multibody applications. Vehicle System Dynamics, 49(6), 969-1001. doi:10.1080/00423114.2010.504854Burgelman N. The wheel–rail contact problem in vehicle dynamic simulation, in: Railahead Group [PhD thesis]. Technische Universiteit Delft; 2016.Ren, Z., Iwnicki, S. D., & Xie, G. (2011). A new method for determining wheel–rail multi-point contact. Vehicle System Dynamics, 49(10), 1533-1551. doi:10.1080/00423114.2010.539237Yang, X., Gu, S., Zhou, S., Zhou, Y., & Lian, S. (2015). A method for improved accuracy in three dimensions for determining wheel/rail contact points. Vehicle System Dynamics, 53(11), 1620-1640. doi:10.1080/00423114.2015.1066508Johnson, K. L. (1985). Contact Mechanics. doi:10.1017/cbo9781139171731European Standards, Railway applications – testing for the acceptance of running characteristics of railway vehicles – testing of running behaviour and stationary tests, in: EN 14363:2005

Determination of the resonance response in an engine cylinder with a bowl-in-piston geometry by the finite element method for inferring the trapped mass

[EN] Cylinder resonance phenomenon in reciprocating engines consists of high-frequency pressure oscillations excited by the combustion. The frequency of these oscillations is proportional to the speed of sound on pent-roof combustion chambers and henceforth the resonance frequency can be used to estimate the trapped mass, but in bowl-in-piston chambers a geometrical factor must be added in order to deal with the bowl disturbance. This paper applies the finite element method (FEM) to provide a resonance calibration for new design combustion chambers, which are commonly dominated by the bowl geometry near the top dead centre. The resonance calibration does not need any sensor information when it is solved by a FEM procedure, and consequently, is free from measurement errors. The calibration is proven to be independent of the chamber conditions and the results obtained are compared with experimental data by using spectral techniques and measuring precisely the trapped mass.[EN]This research has been partially supported by the European Union in framework of the POWERFUL project, seventh framework program FP7/2007-2013, theme 7, sustainable surface transport (grant agreement number SCP8-GA-2009-234032).Broatch Jacobi, JA.; Guardiola, C.; Bares-Moreno, P.; Denia Guzmán, FD. (2016). Determination of the resonance response in an engine cylinder with a bowl-in-piston geometry by the finite element method for inferring the trapped mass. International Journal of Engine Research. 17(5):534-542. https://doi.org/10.1177/1468087415589701S534542175Powell, J. D. (1993). Engine Control Using Cylinder Pressure: Past, Present, and Future. Journal of Dynamic Systems, Measurement, and Control, 115(2B), 343-350. doi:10.1115/1.2899074Desantes, J. M., Galindo, J., Guardiola, C., & Dolz, V. (2010). Air mass flow estimation in turbocharged diesel engines from in-cylinder pressure measurement. Experimental Thermal and Fluid Science, 34(1), 37-47. doi:10.1016/j.expthermflusci.2009.08.009Finol, C. A., & Robinson, K. (2006). Thermal modelling of modern engines: A review of empirical correlations to estimate the in-cylinder heat transfer coefficient. Proceedings of the Institution of Mechanical Engineers, Part D: Journal of Automobile Engineering, 220(12), 1765-1781. doi:10.1243/09544070jauto202Torregrosa, A. J., Broatch, A., Martín, J., & Monelletta, L. (2007). Combustion noise level assessment in direct injection Diesel engines by means of in-cylinder pressure components. Measurement Science and Technology, 18(7), 2131-2142. doi:10.1088/0957-0233/18/7/045Luján, J. M., Bermúdez, V., Guardiola, C., & Abbad, A. (2010). A methodology for combustion detection in diesel engines through in-cylinder pressure derivative signal. Mechanical Systems and Signal Processing, 24(7), 2261-2275. doi:10.1016/j.ymssp.2009.12.012Payri, F., Broatch, A., Tormos, B., & Marant, V. (2005). New methodology for in-cylinder pressure analysis in direct injection diesel engines—application to combustion noise. Measurement Science and Technology, 16(2), 540-547. doi:10.1088/0957-0233/16/2/029Zhen, X., Wang, Y., Xu, S., Zhu, Y., Tao, C., Xu, T., & Song, M. (2012). The engine knock analysis – An overview. Applied Energy, 92, 628-636. doi:10.1016/j.apenergy.2011.11.079Draper C. S. The physical effects of detonation in a closed cylindrical chamber. Technical report, National Advisory Committee for Aeronautics, 1938.Payri, F., Olmeda, P., Guardiola, C., & Martín, J. (2011). Adaptive determination of cut-off frequencies for filtering the in-cylinder pressure in diesel engines combustion analysis. Applied Thermal Engineering, 31(14-15), 2869-2876. doi:10.1016/j.applthermaleng.2011.05.012Hickling, R., Feldmaier, D. A., Chen, F. H. K., & Morel, J. S. (1983). Cavity resonances in engine combustion chambers and some applications. The Journal of the Acoustical Society of America, 73(4), 1170-1178. doi:10.1121/1.389261Bodisco, T., Reeves, R., Situ, R., & Brown, R. (2012). Bayesian models for the determination of resonant frequencies in a DI diesel engine. Mechanical Systems and Signal Processing, 26, 305-314. doi:10.1016/j.ymssp.2011.06.014Guardiola, C., Pla, B., Blanco-Rodriguez, D., & Bares, P. (2014). Cycle by Cycle Trapped Mass Estimation for Diagnosis and Control. SAE International Journal of Engines, 7(3), 1523-1531. doi:10.4271/2014-01-1702Torregrosa, A. J., Broatch, A., Margot, X., Marant, V., & Beauge, Y. (2004). Combustion chamber resonances in direct injection automotive diesel engines: A numerical approach. International Journal of Engine Research, 5(1), 83-91. doi:10.1243/146808704772914264Broatch, A., Margot, X., Gil, A., & Christian Donayre, (José). (2007). Computational study of the sensitivity to ignition characteristics of the resonance in DI diesel engine combustion chambers. Engineering Computations, 24(1), 77-96. doi:10.1108/02644400710718583Payri, F., Molina, S., Martín, J., & Armas, O. (2006). Influence of measurement errors and estimated parameters on combustion diagnosis. Applied Thermal Engineering, 26(2-3), 226-236. doi:10.1016/j.applthermaleng.2005.05.006Mechel, F. P. (Ed.). (2008). Formulas of Acoustics. doi:10.1007/978-3-540-76833-3Samimy, B., & Rizzoni, G. (1996). Mechanical signature analysis using time-frequency signal processing: application to internal combustion engine knock detection. Proceedings of the IEEE, 84(9), 1330-1343. doi:10.1109/5.535251Lapuerta, M., Armas, O., & Hernández, J. J. (1999). Diagnosis of DI Diesel combustion from in-cylinder pressure signal by estimation of mean thermodynamic properties of the gas. Applied Thermal Engineering, 19(5), 513-529. doi:10.1016/s1359-4311(98)00075-1FUENMAYOR, F. J., DENIA, F. D., ALBELDA, J., & GINER, E. (2002). H -ADAPTIVE REFINEMENT STRATEGY FOR ACOUSTIC PROBLEMS WITH A SET OF NATURAL FREQUENCIES. Journal of Sound and Vibration, 255(3), 457-479. doi:10.1006/jsvi.2001.4165Benajes, J., Molina, S., García, A., Belarte, E., & Vanvolsem, M. (2014). An investigation on RCCI combustion in a heavy duty diesel engine using in-cylinder blending of diesel and gasoline fuels. Applied Thermal Engineering, 63(1), 66-76. doi:10.1016/j.applthermaleng.2013.10.052Chen, A., & Dai, X. (2010). Internal combustion engine vibration analysis with short-term Fourier-transform. 2010 3rd International Congress on Image and Signal Processing. doi:10.1109/cisp.2010.5646222Stanković, Lj., & Böhme, J. F. (1999). Time–frequency analysis of multiple resonances in combustion engine signals. Signal Processing, 79(1), 15-28. doi:10.1016/s0165-1684(99)00077-8Costa, A. H., & Boudreaux-Bartels, G. F. (1999). An overview of aliasing errors in discrete-time formulations of time-frequency representations. IEEE Transactions on Signal Processing, 47(5), 1463-1474. doi:10.1109/78.75724

Railway rolling noise mitigation through optimal track design

[EN] The main goal of the present work lies in the identification of the railway track properties that influence acoustic radiation, as well as in the analysis of these properties for the reduction of sound levels. This is achieved through a dynamic model of the railway wheel and track that allows the study of rolling noise, produced as a result of the wheel/rail interaction. Once the vibrational response of the railway components is determined, the sound power radiated by them is evaluated. The influence of the track properties on the sound radiation is determined by analysing the acoustic power results of different track configurations. From the results obtained, a number of guidelines are presented for noise mitigation of the involved railway elements. Between the worst and the best track design, there are differences of approximately 7.4 dB(A) in the radiation considering the wheel, rail and sleeper noise.The authors gratefully acknowledge the financial support of Agencia Estatal de Investigación and European Regional Development Fund (grant FPU18/03999, project TRA2017-84701-R and project PID2020-112886RA-I00)Andrés Ruiz, V.; Martínez Casas, J.; Carballeira Morado, J.; Denia Guzmán, F.; Thompson, DJ. (2022). Railway rolling noise mitigation through optimal track design. En Proceedings of the YIC 2021 - VI ECCOMAS Young Investigators Conference. Editorial Universitat Politècnica de València. 313-319. https://doi.org/10.4995/YIC2021.2021.12583OCS31331

Seguimiento de las guías españolas para el manejo del asma por el médico de atención primaria: un estudio observacional ambispectivo

Objetivo Evaluar el grado de seguimiento de las recomendaciones de las versiones de la Guía española para el manejo del asma (GEMA 2009 y 2015) y su repercusión en el control de la enfermedad. Material y métodos Estudio observacional y ambispectivo realizado entre septiembre del 2015 y abril del 2016, en el que participaron 314 médicos de atención primaria y 2.864 pacientes. Resultados Utilizando datos retrospectivos, 81 de los 314 médicos (25, 8% [IC del 95%, 21, 3 a 30, 9]) comunicaron seguir las recomendaciones de la GEMA 2009. Al inicio del estudio, 88 de los 314 médicos (28, 0% [IC del 95%, 23, 4 a 33, 2]) seguían las recomendaciones de la GEMA 2015. El tener un asma mal controlada (OR 0, 19, IC del 95%, 0, 13 a 0, 28) y presentar un asma persistente grave al inicio del estudio (OR 0, 20, IC del 95%, 0, 12 a 0, 34) se asociaron negativamente con tener un asma bien controlada al final del seguimiento. Por el contrario, el seguimiento de las recomendaciones de la GEMA 2015 se asoció de manera positiva con una mayor posibilidad de que el paciente tuviera un asma bien controlada al final del periodo de seguimiento (OR 1, 70, IC del 95%, 1, 40 a 2, 06). Conclusiones El escaso seguimiento de las guías clínicas para el manejo del asma constituye un problema común entre los médicos de atención primaria. Un seguimiento de estas guías se asocia con un control mejor del asma. Existe la necesidad de actuaciones que puedan mejorar el seguimiento por parte de los médicos de atención primaria de las guías para el manejo del asma. Objective: To assess the degree of compliance with the recommendations of the 2009 and 2015 versions of the Spanish guidelines for managing asthma (Guía Española para el Manejo del Asma [GEMA]) and the effect of this compliance on controlling the disease. Material and methods: We conducted an observational ambispective study between September 2015 and April 2016 in which 314 primary care physicians and 2864 patients participated. Results: Using retrospective data, we found that 81 of the 314 physicians (25.8%; 95% CI 21.3–30.9) stated that they complied with the GEMA2009 recommendations. At the start of the study, 88 of the 314 physicians (28.0%; 95% CI 23.4–33.2) complied with the GEMA2015 recommendations. Poorly controlled asthma (OR, 0.19; 95% CI 0.13–0.28) and persistent severe asthma at the start of the study (OR, 0.20; 95% CI 0.12–0.34) were negatively associated with having well-controlled asthma by the end of the follow-up. In contrast, compliance with the GEMA2015 recommendations was positively associated with a greater likelihood that the patient would have well-controlled asthma by the end of the follow-up (OR, 1.70; 95% CI 1.40–2.06). Conclusions: Low compliance with the clinical guidelines for managing asthma is a common problem among primary care physicians. Compliance with these guidelines is associated with better asthma control. Actions need to be taken to improve primary care physician compliance with the asthma management guidelines

Method for obtaining the wheel–rail contact location and its application to the normal problem calculation through ‘CONTACT’

This work presents a robust methodology for calculating inter-penetration areas between railway wheel and rail surfaces, the profiles of which are defined by a series of points. The method allows general three-dimensional displacements of the wheelset to be considered, and its characteristics make it especially suitable for dynamic simulations where the wheel&ndash;rail contact is assumed to be flexible. The technique is based on the discretisation of the geometries of the surfaces in contact, considering the wheel as a set of truncated cones and the rail as points. By means of this approach, it is possible to reduce the problem to the calculation of the intersections between cones and lines, the solution for which has a closed-form expression. The method has been used in conjunction with the CONTACT algorithm in order to solve the static normal contact problem when the lateral displacement of the wheelset, its yaw angle and the vertical force applied in the wheelset centroid are prescribed. The results consist of smooth functions when the dependent coordinates are represented as a function of the independent ones, lacking the jump discontinuities that are present when a rigid contact model is adopted. Example results are shown and assessed for the normal contact problem for different lateral and yaw positions of the wheelset on the track.</span

Computational performance of analytical methods for the acoustic modelling of automotive exhaust devices incorporating monoliths

[EN] The acoustic modelling of automotive exhaust devices, such as catalytic converters (CC) and diesel particulate filters (DPF), usually requires the use of multidimensional analytical and numerical techniques. The presence of higher order modes and three-dimensional waves in the expansion and contraction subdomains, as well as sound propagation within the monolith capillary ducts, can be considered through the finite element method (FEM), although this approach is traditionally thought to be very time consuming. With a view to overcome this limitation and to reduce the computational effort of the FEM, alternative modelling techniques are presented in the current work to speed up transmission loss calculations in exhaust devices incorporating monoliths. These approaches are based on the point collocation technique and the mode matching method. As shown in earlier studies, the sound attenuation of an exhaust device incorporating a monolith can be properly predicted if the latter is replaced by a plane wave four-pole transfer matrix providing a relationship between the acoustic fields at both sides of the monolithic region. Therefore, this work combines the presence of multidimensional higher order modes in the expansion and contraction regions with one-dimensional wave propagation within the capillary ducts of the central monolith. The point collocation technique and the mode matching method are applied to the compatibility conditions of the acoustic fields at all the subdomain interfaces to couple the solutions of the wave equation in the corresponding exhaust device subcomponents. For the particular case of rigid circular ducts, Bessel functions are considered as transversal pressure modes. The computational efficiency and accuracy of the results associated with the two analytical modelling techniques presented here are assessed, including the effect of the number of modes and collocation points, as well as their location. All the analytical approaches proposed in this work provide accurate predictions of the device attenuation performance and outperform the computational expenditure of a FE computation. Some differences are found, however, among the various analytical schemes in terms of computational speed and solution accuracy. From the results presented here, the mode matching method is the most efficient technique for the particular configurations under study, mainly due to the possibility of exploiting the orthogonality properties of the transverse pressure modes.This work has been supported by Ministerio de Economia y Competitividad and the European Regional Development Fund (project TRA2013-45596-C2-1-R), as well as Generalitat Valenciana (projects Prometeo/2016/007 and GV/2016/011 of Conselleria d'Educacio, Investigacio, Cultura i Esport).Denia, FD.; Martínez Casas, J.; Carballeira, J.; Nadal, E.; Fuenmayor Fernández, FJ. (2018). Computational performance of analytical methods for the acoustic modelling of automotive exhaust devices incorporating monoliths. Journal of Computational and Applied Mathematics. 330:995-1006. https://doi.org/10.1016/j.cam.2017.03.010S995100633

Using a 2.5D BE model to determine the sound pressure on the external train surface

In this paper, a wavenumber-domain boundary element (2.5D BE) approach is adopted to predict the transmission of noise from the wheels, the rails and the sleepers to the train external surfaces. In the 2.5D models, only the cross-section of the vehicle is created by using boundary elements, while the third direction is taken into account in terms of wavenumbers. After the sound pressure on the train cross-section is obtained, an inverse Fourier transform is applied to obtain the spatial distribution of the sound on the train surfaces. To validate this approach, the 2.5D boundary element method was used to predict the sound distribution on the train surfaces due to a point source below the vehicle, and due to the vibration of the track. The prediction of the sound distribution from the 2.5D method shows the sound pressure levels on the train floor are 20 dB higher than the pressure on the sides, and the pressure on the train roof caused by the sources below the vehicle is negligible. The 2.5D boundary element method was also used to predict the sound pressure spectrum on the train sides when the train was in running operation. Reasonable agreement was found with measurements.</p

A framework to predict the airborne noise inside railway vehicles with application to rolling noise

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

Using a 2.5D boundary element model to predict the sound distribution on train external surfaces due to rolling noise

[EN] In order to be able to predict train interior noise, it is first important to calculate the external sound pressure distribution on the floor, sidewalls and roof. This can then be combined with the transmission loss of the train panels to determine the interior noise. Traditional techniques such as the finite element and boundary element (FE/BE) methods in three dimensions (3D) can achieve this result but are computationally very expensive. In this paper, a wavenumber-domain boundary element (2.5D BE) approach is instead adopted to predict the propagation of rolling noise from the wheels, rails and sleepers to the train external surfaces. In the 2.5D models, only the cross-section of the vehicle is represented by using boundary elements, while the third direction is considered in terms of a spectrum of wavenumbers. The rail is treated directly in the wavenumber domain but, to include the wheel, a method of representing point sources in a 2.5D approach is developed. An inverse Fourier transform is applied to obtain the spatial distribution of the sound pressure on the train surfaces. The validity of this approach has been verified by comparison with experimental data. The 2.5D BE method was first used to predict the sound distribution on a 1:5 scale train surfaces due to a point source below the vehicle, and later it was used to predict the sound pressure on a full-scale metro vehicle due to a loudspeaker. Comparisons of predictions with measurements on the scale model and on the metro vehicle showed good agreements. For a point source below the vehicle, the sound pressure levels on the train floor were found to be around 20 dB higher than on the sides, and the sound pressure on the train roof was negligible. The 2.5D BE method was also used to predict the sound pressure on the metro vehicle surfaces in running operation, in which the predicted sound pressure levels on the train external surfaces agreed with measurements to within 3 dB and similar trends were found in terms of spectra and longitudinal distribution of pressure.The work presented in this paper has received funding from China Scholarship Council and the Shift2Rail Joint Undertaking under the European Union's Horizon 2020 research and innovation programme (grant agreement no. 777564). The contents of this publication only reflect the authors' view and the Joint Undertaking is not responsible for any use that may be made of the information contained in the paper. The authors would also like to thank Dr. Hongseok Jeong for his assistance in the laboratory measurements and Metro de Madrid for assistance in the field tests. The authors are grateful to Dr. Xianying Zhang for providing the measured vibration of the 1:5 scale rail. All data published in this paper are openly available from the University of Southampton repository at 10.5258/SOTON/D1483Li, H.; Thompson, D.; Squicciarini, G.; Liu, X.; Rissmann, M.; Denia, FD.; Giner Navarro, J. (2020). Using a 2.5D boundary element model to predict the sound distribution on train external surfaces due to rolling noise. Journal of Sound and Vibration. 486:1-22. https://doi.org/10.1016/j.jsv.2020.115599S12248