Large Eddy Simulation and Hilbert Huang Transform for fluctuation pressure of high speed train

Author(s):  
Yanan Wang ◽  
Chunju Chen ◽  
Hongyang He
2014 ◽  
Vol 1049-1050 ◽  
pp. 1022-1025
Author(s):  
Xiao Feng Zhang ◽  
You Gang Xiao ◽  
Liang Sun ◽  
Yu Shi

In order to reduce aerodynamic noise in high-speed train cab,the SEA model of cab is established. The fluctuation pressures from train head surface are calculated by large eddy simulation method. Using fluctuation pressure as excitation force, power flow caused by airflow among sub-systems of SEA model of cab is obtained. Two schemes are put forward to reduce the aerodynamic noise in cab, namely interior decoration modification and windowpane thickness increase. The results show that when a layer of splint with 0.01 m thickness, 0.5 loss factor is added to the original decoration in cab, the overall sound pressure level (SPL) at driver head location will reduce 1.23 dB(A). When the cab windowpane thickness is increased to 5 mm from 4 mm, the overall SPL at the driver head location will reduce 0.87 dB(A).


Author(s):  
Xiaofeng Yang ◽  
Saurabh Gupta ◽  
Tang-Wei Kuo ◽  
Venkatesh Gopalakrishnan

A comparative cold flow analysis between Reynolds-averaged Navier–Stokes (RANS) and large eddy simulation (LES) cycle-averaged velocity and turbulence predictions is carried out for a single cylinder engine with a transparent combustion chamber (TCC) under motored conditions using high-speed particle image velocimetry (PIV) measurements as the reference data. Simulations are done using a commercial computationally fluid dynamics (CFD) code CONVERGE with the implementation of standard k-ε and RNG k-ε turbulent models for RANS and a one-equation eddy viscosity model for LES. The following aspects are analyzed in this study: The effects of computational domain geometry (with or without intake and exhaust plenums) on mean flow and turbulence predictions for both LES and RANS simulations. And comparison of LES versus RANS simulations in terms of their capability to predict mean flow and turbulence. Both RANS and LES full and partial geometry simulations are able to capture the overall mean flow trends qualitatively; but the intake jet structure, velocity magnitudes, turbulence magnitudes, and its distribution are more accurately predicted by LES full geometry simulations. The guideline therefore for CFD engineers is that RANS partial geometry simulations (computationally least expensive) with a RNG k-ε turbulent model and one cycle or more are good enough for capturing overall qualitative flow trends for the engineering applications. However, if one is interested in getting reasonably accurate estimates of velocity magnitudes, flow structures, turbulence magnitudes, and its distribution, they must resort to LES simulations. Furthermore, to get the most accurate turbulence distributions, one must consider running LES full geometry simulations.


2015 ◽  
Author(s):  
Peter Janas ◽  
Mateus Dias Ribeiro ◽  
Andreas Kempf ◽  
Martin Schild ◽  
Sebastian A. Kaiser

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