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  • 1.
    Herbst, A H
    et al.
    Bombardier Transportation, Sweden.
    Muld, T W
    KTH, School of Engineering Sciences (SCI), Aeronautical and Vehicle Engineering. KTH, School of Engineering Sciences (SCI), Centres, Linné Flow Center, FLOW.
    Efraimsson, Gunilla
    KTH, School of Engineering Sciences (SCI), Aeronautical and Vehicle Engineering. KTH, School of Engineering Sciences (SCI), Centres, VinnExcellence Center for ECO2 Vehicle design.
    Aerodynamic prediction tools for high-speed trains2014In: International Journal of Rail transportation, ISSN 2324-8378, E-ISSN 2324-8386, Vol. 2, no 1Article in journal (Refereed)
    Abstract [en]

    With high-speed trains, the need for efficient and accurate aerodynamic prediction tools increases, since the influence of the aerodynamics on the overall train performance raises. New requirements on slipstream velocities and head pressure pulse in the revised Technical Specification for Interoperability (TSI) for train speeds higher than 190 km/h are more challenging to fulfil for wide-body trains, like the Green train concept vehicle Regina 250, as well as higher trains, like double-deck trains. In this paper, we give an overview of the results from a project within the Green train programme, where the objective was to increase the knowledge on slipstream air flow of wide body trains at high speeds, to understand the implications of the new requirements on the front shape and to develop a prediction methodology in order to take this into account early in the design cycle. In addition, the front design was in parallel optimized with respect to head pressure pulse and drag.

  • 2.
    Muld, Toma. W.
    et al.
    KTH, School of Engineering Sciences (SCI), Aeronautical and Vehicle Engineering.
    Efraimsson, Gunilla
    KTH, School of Engineering Sciences (SCI), Aeronautical and Vehicle Engineering.
    Henningson, D. S.
    KTH, School of Engineering Sciences (SCI), Centres, Linné Flow Center, FLOW.
    Mode decomposition of flow structures in the wake of two high-speed trains2012In: Civil-Comp Proceedings, ISSN 1759-3433, Vol. 98Article in journal (Refereed)
    Abstract [en]

    Two different train geometries, the Aerodynamic Train Model (ATM) and the CRH1, are studied in order to compare the flow structures in the wake. The flow is simulated with Detached Eddy-Simulation and then decomposed into modes with Proper Orthogonal Decomposition. This study has found that the flow structures are indeed different for the two train models although the tails are rather similar. For the CRH1 the dominant flow structures twist one of the counter-rotating vortices and leaves the other straight. The convergence of the modes are investigated and it is shown that approximately the same number of snapshots are needed for both trains. 

  • 3.
    Muld, Tomas
    et al.
    KTH, School of Engineering Sciences (SCI), Aeronautical and Vehicle Engineering. KTH, School of Engineering Sciences (SCI), Centres, Linné Flow Center, FLOW.
    Efraimsson, Gunilla
    KTH, School of Engineering Sciences (SCI), Aeronautical and Vehicle Engineering. KTH, School of Engineering Sciences (SCI), Centres, Linné Flow Center, FLOW.
    Henningson, Dan S.
    KTH, School of Engineering Sciences (SCI), Mechanics. KTH, School of Engineering Sciences (SCI), Centres, Linné Flow Center, FLOW.
    Mode Decomposition on Surface-Mounted Cube2012In: Flow Turbulence and Combustion, ISSN 1386-6184, E-ISSN 1573-1987, Vol. 88, no 3, p. 279-310Article in journal (Refereed)
    Abstract [en]

    In this paper, the flow around the surface-mounted cube is decomposed into modes using Proper Orthogonal Decomposition (POD) and Koopman mode decomposition, respectively. The objective of the paper is twofold. Firstly, a comparison of the two decomposition methods for a highly separated flow is performed. Secondly, an evaluation of Detached Eddy Simulation (DES) for simulating a time-accurate flow, to be used as input data for the two mode decomposition methods, is accomplished. The knowledge on the accuracy and usefulness of the modes computed with from DES flow fields can then be the foundation for other studies for applied geometries in vehicle aerodynamics. The flow is simulated using DES, which enables time-accurate simulations on flows around realistic vehicle geometries. Most of the first eight modes computed with DES in a reference domain can also be found among the first eight computed with LES in reference work. Since the POD modes computed with DES resemble those computed with LES, the conclusion is that DES is suitable to use for mode decomposition. When comparing the POD and Koopman modes, many similarities can be found in both the spatial and temporal modes. For this case, where the flow contains a broad band of frequencies, it is concluded that the advantage of using Koopman modes, decomposing by frequency, cannot be fully utilized, and Koopman modes are very similar to the POD modes.

  • 4.
    Muld, Tomas W.
    KTH, School of Engineering Sciences (SCI), Mechanics.
    Analysis of Flow Structures in Wake Flows for Train Aerodynamics2010Licentiate thesis, comprehensive summary (Other academic)
    Abstract [en]

    Train transportation is a vital part of the transportation system of today anddue to its safe and environmental friendly concept it will be even more impor-tant in the future. The speeds of trains have increased continuously and withhigher speeds the aerodynamic effects become even more important. One aero-dynamic effect that is of vital importance for passengers’ and track workers’safety is slipstream, i.e. the flow that is dragged by the train. Earlier ex-perimental studies have found that for high-speed passenger trains the largestslipstream velocities occur in the wake. Therefore the work in this thesis isdevoted to wake flows. First a test case, a surface-mounted cube, is simulatedto test the analysis methodology that is later applied to a train geometry, theAerodynamic Train Model (ATM). Results on both geometries are comparedwith other studies, which are either numerical or experimental. The comparisonfor the cube between simulated results and other studies is satisfactory, whiledue to a trip wire in the experiment the results for the ATM do not match.The computed flow fields are used to compute the POD and Koopman modes.For the cube this is done in two regions of the flow, one to compare with a priorpublished study Manhart & Wengle (1993) and another covering more of theflow and especially the wake of the cube. For the ATM, a region containing theimportant flow structures is identified in the wake, by looking at instantaneousand fluctuating velocities. To ensure converged POD modes two methods toinvestigate the convergence are proposed, tested and applied. Analysis of themodes enables the identification of the important flow structures. The flowtopologies of the two geometries are very different and the flow structures arealso different, but the same methodology can be applied in both cases. For thesurface-mounted cube, three groups of flow structures are found. First groupis the mean flow and then two kinds of perturbations around the mean flow.The first perturbation is at the edge of the wake, relating to the shear layerbetween the free stream and the disturbed flow. The second perturbation isinside the wake and is the convection of vortices. These groups would then betypical of the separation bubble that exists in the wake of the cube. For theATM the main flow topology consists of two counter rotating vortices. Thiscan be seen in the decomposed modes, which, except for the mean flow, almostonly contain flow structures relating to these vortices.

  • 5.
    Muld, Tomas W.
    KTH, School of Engineering Sciences (SCI), Mechanics.
    Mode Decomposition of the Flow Behind the Aerodynamic Train Model Simulated by Detached Eddy Simulation2010Report (Other (popular science, discussion, etc.))
  • 6.
    Muld, Tomas W.
    KTH, School of Engineering Sciences (SCI), Aeronautical and Vehicle Engineering. KTH, School of Engineering Sciences (SCI), Centres, Linné Flow Center, FLOW.
    Slipstream and Flow Structures in the Near Wake of High-Speed Trains2012Doctoral thesis, comprehensive summary (Other academic)
    Abstract [en]

    Train transportation is a vital part of the transportation system of today. Asthe speed of the trains increase, the aerodynamic effects become more impor-tant. One aerodynamic effect that is of vital importance for passengers’ andtrack workers’ safety is slipstream, i.e. the induced velocities by the train.Safety requirements for slipstream are regulated in the Technical Specificationsfor Interoperability (TSI). Earlier experimental studies have found that forhigh-speed passenger trains the largest slipstream velocities occur in the wake.Therefore, in order to study slipstream of high-speed trains, the work in thisthesis is devoted to wake flows. First a test case, a surface-mounted cube, issimulated to test the analysis methodology that is later applied to two differ-ent train geometries, the Aerodynamic Train Model (ATM) and the CRH1.The flow is simulated with Delayed-Detached Eddy Simulation (DDES) andthe computed flow field is decomposed into modes with Proper Orthogonal De-composition (POD) and Dynamic Mode Decomposition (DMD). The computedmodes on the surface-mounted cube compare well with prior studies, whichvalidates the use of DDES together with POD/DMD. To ensure that enoughsnapshots are used to compute the POD and DMD modes, a method to inves-tigate the convergence is proposed for each decomposition method. It is foundthat there is a separation bubble behind the CRH1 and two counter-rotatingvortices behind the ATM. Even though the two geometries have different flowtopologies, the dominant flow structure in the wake in terms of energy is thesame, namely vortex shedding. Vortex shedding is also found to be the mostimportant flow structure for slipstream, at the TSI position. In addition, threeconfigurations of the ATM with different number of cars are simulated, in orderto investigate the effect of the size of the boundary layer on the flow structures.The most dominant structure is the same for all configurations, however, theStrouhal number decreases as the momentum thickness increases. The velocityin ground fixed probes are extracted from the flow, in order to investigate theslipstream velocity defined by the TSI. A large scatter in peak position andvalue for the different probes are found. Investigating the mean velocity atdifferent distances from the train side wall, indicates that wider versions of thesame train will create larger slipstream velocities.

  • 7.
    Muld, Tomas W.
    et al.
    KTH, School of Engineering Sciences (SCI), Aeronautical and Vehicle Engineering, Aeroacoustics. KTH, School of Engineering Sciences (SCI), Centres, Linné Flow Center, FLOW.
    Efraimsson, Gunilla
    KTH, School of Engineering Sciences (SCI), Aeronautical and Vehicle Engineering, Aeroacoustics. KTH, School of Engineering Sciences (SCI), Centres, Linné Flow Center, FLOW.
    Henningson, Dan S
    KTH, School of Engineering Sciences (SCI), Mechanics. KTH, School of Engineering Sciences (SCI), Centres, Linné Flow Center, FLOW.
    Flow structures around a high-speed train extracted using Proper Orthogonal Decomposition and Dynamic Mode Decomposition2012In: Computers & Fluids, ISSN 0045-7930, E-ISSN 1879-0747, Vol. 57, p. 87-97Article in journal (Refereed)
    Abstract [en]

    In this paper, Proper Orthogonal Decomposition (POD) and Dynamic Mode Decomposition (DMD) are used to extract the most dominant flow structures of a simulated flow in the wake of a high-speed train model, the Aerodynamic Train Model (ATM). The use of decomposition methods to successfully identify dominant flow structures for an engineering geometry is achieved by using a flow field simulated with the Detached Eddy Simulation model (DES), which is a turbulence model enabling time accurate solutions of the flows around engineering geometries. This paper also examines the convergence of the POD and DMD modes for this case. It is found that the most dominant DMD mode needs a longer sample time to converge than the most dominant POD mode. A comparison between the modes from the two different decomposition methods shows that the second and third POD modes correspond to the same flow structure as the second DMD mode. This is confirmed both by investigating the spectral content of the POD mode coefficients, and by comparing the spatial modes. The flow structure associated with these modes is identified as being vortex shedding. The identification is performed by reconstructing the flow field using the mean flow and the second DMD mode. A second flow structure, a bending of the counter-rotating vortices, is also identified. Identifying this flow structure is achieved by reconstructing the flow field with the mean flow and the fourth and fifth POD modes.

  • 8.
    Muld, Tomas W.
    et al.
    KTH, School of Engineering Sciences (SCI), Aeronautical and Vehicle Engineering. KTH, School of Engineering Sciences (SCI), Centres, Linné Flow Center, FLOW.
    Efraimsson, Gunilla
    KTH, School of Engineering Sciences (SCI), Aeronautical and Vehicle Engineering. KTH, School of Engineering Sciences (SCI), Centres, Linné Flow Center, FLOW.
    Henningson, Dan S.
    KTH, School of Engineering Sciences (SCI), Mechanics. KTH, School of Engineering Sciences (SCI), Centres, Linné Flow Center, FLOW.
    Mode Decomposition and Slipstream Velocities in the Wake of Two High-Speed Trains2012In: The international Journal of railway technology, ISSN 2049-5358, E-ISSN 2053-602X, The International Journal of Railway TechnologyArticle in journal (Other academic)
    Abstract [en]

    Two different train geometries, the Aerodynamic Train Model (ATM) and the CRH1, are studied in order to compare the flow fields around the trains. This paper focuses on the flow structures and flow topologies in the wake. The flow is simulated with Detached Eddy Simulation and decomposed into modes with Proper Orthogonal Decomposition and Dynamic Mode Decomposition, respectively. The topology of the flow is found to be different for the two train geometries, where the flow behind the ATM separates with two counter-rotating vortices, while the flow behind the CRH1 separates with a separation bubble. The difference in flow topology is seen, for instance,  in the mean pressure at the tail, the mean flow in the wake and streamlines of the flow. Despite the different flow topology, there are also similar flow structures in the wake behind the ATM and the CRH1, such as vortex shedding. In order to measure the slipstream effect of the two vehicles, the velocity in a ground fixed point has to be extracted from the train fixed flow field. The resulting velocity is averaged with an equivalent of 1s time average at full scale. The contribution of the DMD modes to slipstream has been analyzed and it is found that the same flow structure that is dominant in energy is also important for slipstream.

  • 9.
    Muld, Tomas W.
    et al.
    KTH, School of Engineering Sciences (SCI), Aeronautical and Vehicle Engineering. KTH, School of Engineering Sciences (SCI), Centres, Linné Flow Center, FLOW.
    Efraimsson, Gunilla
    KTH, School of Engineering Sciences (SCI), Aeronautical and Vehicle Engineering. KTH, School of Engineering Sciences (SCI), Centres, Linné Flow Center, FLOW.
    Henningson, Dan S.
    KTH, School of Engineering Sciences (SCI), Mechanics. KTH, School of Engineering Sciences (SCI), Centres, Linné Flow Center, FLOW.
    Proper orthogonal decomposition of flow structures around a surface-mounted cube computed with detached-eddy simulation2009In: SAE World Congress & Exhibition, 2009Conference paper (Refereed)
    Abstract [en]

    In this paper the flow passing a cube mounted in a channel is studied via detached-eddy simulation (DES). The results are compared to previous studies, where large eddy simulation (LES) have been used. The mean velocity profile found in this paper was found to match with other references above the cube but not behind the cube. The shear stress was underpredicted by the DES computations on top of the cube. Behind the cube the LES and experimental studies show a much more smeared shear layer than the DES calculation. The proper orthogonal decomposition (POD) modes are calculated and investigated. The first mode, which corresponds to the mean flow, was found to contain the flow structure of the large separation above the cube. The POD modes calculated from the DES results show higher percentage of energy in the first mode than the POD calculated from LES results.

  • 10.
    Muld, Tomas W.
    et al.
    KTH, School of Engineering Sciences (SCI), Aeronautical and Vehicle Engineering. KTH, School of Engineering Sciences (SCI), Centres, Linné Flow Center, FLOW.
    Efraimsson, Gunilla
    KTH, School of Engineering Sciences (SCI), Aeronautical and Vehicle Engineering. KTH, School of Engineering Sciences (SCI), Centres, Linné Flow Center, FLOW.
    Henningson, Dan S.
    KTH, School of Engineering Sciences (SCI), Mechanics. KTH, School of Engineering Sciences (SCI), Centres, Linné Flow Center, FLOW.
    Wake characteristics of high-speed trains with different lengths2014In: Proceedings of the Institution of mechanical engineers. Part F, journal of rail and rapid transit, ISSN 0954-4097, E-ISSN 2041-3017, Vol. 228, no 4, p. 333-342Article in journal (Refereed)
    Abstract [en]

    Three different train configurations with different numbers of cars are analysed in order to investigate the effect of the train length on wake structures. The train geometry considered is the aerodynamic train model and the different versions have two, three and four cars. Due to the different lengths of the trains, the boundary-layer thickness will be different at the tail of each configuration. The flow is simulated using detached eddy simulation, and coherent flow structures are extracted via proper orthogonal decomposition and dynamic mode decomposition. As a result of reconstruction of the flow field using coupling of the mean flow and the first fluctuating proper orthogonal decomposition mode, it is found that the dominant flow structure in the wake is the same for all three cases. However, this structure has different frequencies and wavelengths depending on the boundary-layer thickness in front of the separation. It is shown that the frequency decreases as the boundary-layer thickness increases for these train configurations.

  • 11.
    Muld, Tomas W.
    et al.
    KTH, School of Engineering Sciences (SCI), Aeronautical and Vehicle Engineering. KTH, School of Engineering Sciences (SCI), Centres, Linné Flow Center, FLOW.
    Efraimsson, Gunilla
    KTH, School of Engineering Sciences (SCI), Aeronautical and Vehicle Engineering. KTH, School of Engineering Sciences (SCI), Centres, Linné Flow Center, FLOW.
    Henningson, Dan S.
    KTH, School of Engineering Sciences (SCI), Mechanics. KTH, School of Engineering Sciences (SCI), Centres, Linné Flow Center, FLOW.
    Herbst, Astrid H.
    Bombardier Transportation, Sweden.
    Orellano, Alexander
    Bombardier Transportation, Sweden.
    Analysis of flow structures in the wake of a high-speed train2016In: Proceedings aerodynamics of heavy vehicles III, buses, trucks and trains, Springer, 2016, Vol. 79Conference paper (Refereed)
    Abstract [en]

    Slipstream is the flow that a train pulls along due to the viscosity of the fluid. In real life applications, the effect of the slipstream flow is a safety concern for people on platform, tracksideworkers and objects on platforms such as baggage carts and pushchairs. The most important region for slipstream of high-speed passanger trains is the near wake, in which the flow is fully turbulent with a broad range of length and time scales. In this work, the flow around the Aerodynamic Train Model (ATM) is simulated using Detached Eddy Simulation (DES) to model the turbulence. Different grids are used in order to prove grid converged results. In order to compare with the results of experimental work performed at DLR on the ATM, where a trip wire was attached to the model, it turned out to be necessary to model this wire to have comparable results. An attempt to model the effect of the trip wire via volume forces improved the results but we were not successful at reproducing the full velocity profiles. The flow is analyzed by computing the POD and Koopman modes. The structures in the floware found to be associated with two counter rotating vortices. A strong connection between pairs of modes is found, which is related to the propagation of flow structures for the POD modes. Koopman modes and POD modes are similar in the spatial structure and similarities in frequencies of the time evolution of the structures are also found.

  • 12.
    Muld, Tomas W.
    et al.
    KTH, School of Engineering Sciences (SCI), Aeronautical and Vehicle Engineering, Aerodynamics.
    Efraimsson, Gunilla
    Henningson, Dan S.
    Herbst, Astrid H.
    Orellano, Alexander
    Detached Eddy Simulation and Validation on the Aerodynamic Train Model2009In: EUROMECH COLLOQUIUM 509: Vehicle Aerodynamics, 2009Conference paper (Other academic)
    Abstract [en]

    We present CFD-simulations of the flow around the aerodynamic train model(ATM). The turbulence modelling technique is detached eddy-simulation(DES), where the DES model is based on the k-ω SST RANS model. TheReynolds number for the simulation is 60.000 based on the hydraulic diame-ter (3m in full scale), free-stream velocity and kinematic viscosity of air. Themodel used is in 1:50 scale. The numerical results are compared to water tunnelexperimental data on the ATM available from the German Aerospace Center(DLR). The velocity field is measured using particle image velocimetry (PIV).The numerical setup is made to match the experimental setup as close as possi-ble. Focus of the analysis is on the flow in the wake of the train. Comparisonsof the averaged velocity and the velocity fluctuations in the wake shows that theoverall levels and trends are captured by the numerical simulations. However,the peak value of the velocity magnitude in the wake seems to be overestimatedby the DES technique used.

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