Flow structures around a high-speed train extracted using Proper Orthogonal Decomposition and Dynamic Mode Decomposition
2012 (English)In: Computers & Fluids, ISSN 0045-7930, E-ISSN 1879-0747, Vol. 57, 87-97 p.Article in journal (Refereed) Published
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.
Place, publisher, year, edition, pages
2012. Vol. 57, 87-97 p.
Detached Eddy Simulation, Aerodynamic Train Model, Proper Orthogonal Decomposition, Dynamic Mode Decomposition, Slipstream, Train aerodynamics
IdentifiersURN: urn:nbn:se:kth:diva-65731DOI: 10.1016/j.compfluid.2011.12.012ISI: 000301683300007ScopusID: 2-s2.0-84857034877OAI: oai:DiVA.org:kth-65731DiVA: diva2:483591
FunderSwedish e‐Science Research CenterTrenOp, Transport Research Environment with Novel Perspectives
QC 201204112012-01-252012-01-252013-04-10Bibliographically approved