scholarly journals Large Eddy Simulation (LES) for IC Engine Flows

Author(s):  
Tang-Wei Kuo ◽  
Xiaofeng Yang ◽  
Venkatesh Gopalakrishnan ◽  
Zhaohui Chen
2004 ◽  
Author(s):  
L. Thobois ◽  
G. Rymer ◽  
T. Soulères ◽  
T. Poinsot

Author(s):  
P. Schiffmann ◽  
S. Gupta ◽  
D. Reuss ◽  
V. Sick ◽  
X. Yang ◽  
...  

2017 ◽  
Vol 99 (2) ◽  
pp. 353-383 ◽  
Author(s):  
Chao He ◽  
Wibke Leudesdorff ◽  
Francesca di Mare ◽  
Amsini Sadiki ◽  
Johannes Janicka

2013 ◽  
Vol 23 (10) ◽  
pp. 925-955 ◽  
Author(s):  
Qingluan Xue ◽  
Sibendu Som ◽  
Peter K. Senecal ◽  
E. Pomraning

Author(s):  
Federico Brusiani ◽  
Gian Marco Bianchi

Today, Reynolds Averaged Navier Stokes (RANS) simulation approach remains the most widely used method in computational fluid dynamic studies of IC-Engines because it allows a good prediction of the mean flow properties at an affordable computational cost. The main limit of the RANS approach resides in the method used to predict turbulence that fails in the reproduction of anisotropic turbulence conditions. It can result in a lack of accuracy in reproducing the main physical processes, as spray evolution (mixture formation), heat transfer, and combustion, governing the IC-Engine physics. To fix this problem, the large Eddy Simulation (LES) approach can be considered. In LES the governing equations are filtered in space, rather than time-averaged as in RANS. It allows the direct solution of all the turbulent scales up to a cut-off length defined by the filter dimension. Therefore, in LES a more accurate description of the turbulence and of all the physical processes correlated to it has to be expected. However, even if the LES method allows an irrefutable improvement in turbulent flow solution accuracy, today its application to industrial IC-Engine design is still rare because of its high computational cost. During the last few years, significant advances in numerical methods, sub-grid scale models, and hardware performance have supported LES applications in many industrial fields. This paper is intended to work in the same direction by presenting a new LES methodology based on the coupling between LES and an adaptive mesh refinement (AMR) procedure. The main goal of this procedure is to guarantee a good resolution of the turbulent flow field adapting the filter size to the local turbulence length scale. The developed procedure allows a significant reduction of the total mesh size and, therefore, of the computational cost. The LES-AMR method was tested on an IC-Engine geometry for which experimental results were available.


Energies ◽  
2019 ◽  
Vol 12 (13) ◽  
pp. 2620 ◽  
Author(s):  
Kaushal Nishad ◽  
Florian Ries ◽  
Yongxiang Li ◽  
Amsini Sadiki

Apart from electric vehicles, most internal combustion (IC) engines are powered while burning petroleum-based fossil or alternative fuels after mixing with inducted air. Thereby the operations of mixing and combustion evolve in a turbulent flow environment created during the intake phase and then intensified by the piston motion and influenced by the shape of combustion chamber. In particular, the swirl and turbulence levels existing immediately before and during combustion affect the evolution of these processes and determine engine performance, noise and pollutant emissions. Both the turbulence characteristics and the bulk flow pattern in the cylinder are strongly affected by the inlet port and valve design. In the present paper, large eddy simulation (LES) is appraised and applied to studying the turbulent fluid flow around the intake valve of a single cylinder IC-engine as represented by the so called magnetic resonance velocimetry (MRV) flow bench configuration with a relatively large Reynolds number of 45,000. To avoid an intense mesh refinement near the wall, various subgrid scale models for LES; namely the Smagorinsky, wall adapting local eddy (WALE) model, SIGMA, and dynamic one equation models, are employed in combination with an appropriate wall function. For comparison purposes, the standard RANS (Reynolds-averaged Navier–Stokes) k- ε model is also used. In terms of a global mean error index for the velocity results obtained from all the models, at first it turns out that all the subgrid models show similar predictive capability except the Smagorinsky model, while the standard k- ε model experiences a higher normalized mean absolute error (nMAE) of velocity once compared with MRV data. Secondly, based on the cost-accuracy criteria, the WALE model is used with a fine mesh of ≈39 millions control volumes, the averaged velocity results showed excellent agreement between LES and MRV measurements, revealing the high prediction capability of the suggested LES tool for valve flows. Thirdly, the turbulent flow across the valve curtain clearly featured a back flow resulting in a high speed intake jet in the middle. Comprehensive LES data are generated to carry out statistical analysis in terms of (1) evolution of the turbulent morphology across the valve passage relying on the flow anisotropy map, (2) integral turbulent scales along the intake-charge stream, (3) turbulent flow properties such as turbulent kinetic energy, turbulent velocity and its intensity within the most critical zone in intake-port and along the port length, it further transpires that the most turbulence are generated across the valve passage and these are responsible for the in-cylinder turbulence.


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