Search

Large-eddy simulation has been increasingly applied to internal combustion engine flows because of their improved potential to capture the spatial and temporal evolution of turbulent flow structures compared with Reynolds-averaged Navier Stokes simulation. Furthermore, large-eddy simulation is universally recognized as capable of simulating highly unsteady and random phenomena, which drive cycle-to-cycle variability and cycle-resolved events such as knocks and misfires. To identify large-scale structure fluctuations, many methods have been proposed in the literature. This article describes the application of several analysis methods for the comparison between different datasets (experimental or numerical) and the identification of large-structure fluctuations. The reference engine is the well-known TCC-III singlecylinder optical unit from the University of Michigan and GM Global R&D center; the analyses were carried out under motored engine conditions. A deep analysis of in-cylinder gas dynamics and flow structure evolution was performed by comparing the experimental results (particle image velocimetry of the velocity fields) with a dataset of consecutive largeeddy simulation cycles on four different cutting planes at engine-relevant crank angle positions. Phase-dependent proper orthogonal decomposition was used to obtain further conclusions regarding the accuracy of the simulation results and to apply conditional averaging methods. A two-point correlation and an analysis of the tumble center are proposed. Finally, conclusions are drawn to be used as guidelines in future large-eddy simulation analyses of internal combustion engines. [ABSTRACT FROM AUTHOR]

Computational fluid dynamics has become a fundamental tool for the design and development of internal combustion engines. The meshing strategy plays a central role in the computational efficiency, in the management of the moving components of the engine and in the accuracy of results. The overset mesh approach, usually referred to also as chimera grid or composite grid, was rarely applied to the simulation of internal combustion engines, mainly because of the difficulty in adapting the technique to the specific complexities of internal combustion engine flows. The article demonstrates the feasibility and the effectiveness of the overset mesh technique application to internal combustion engines, thanks to a purposely designed meshing approach. In particular, the technique is used to analyze the cycle-to-cycle variability of internal combustion engine flows using large eddy simulation. Fifty large eddy simulation cycles are performed on the wellknown TCC-III engine in motored condition. Results are analyzed in terms of tumble center trajectory and using proper orthogonal decomposition to objectively characterize the spatial and temporal evolution of turbulent flow field in internal combustion engines. In particular, an original decomposition method previously applied by the authors to the TCC-III measured flow fields is here extended to computational fluid dynamics results. [ABSTRACT FROM AUTHOR]

Experimental techniques like particle image velocimetry provide a powerful technical support for the analysis of the spatial and temporal evolution of the flow field in internal combustion engines. Such techniques can be used to investigate both ensemble-averaged flow structures and their cyclic variations. These last are among the major causes of cycle-to-cycle variability of the engine processes (mixture formation, combustion, heat transfer, emission formation), the reduction of which has become a paradigm recently in engine development. Proper orthogonal decomposition has been largely used in conjunction with particle image velocimetry to analyze flow field characteristics. Several methods involving proper orthogonal decomposition have been proposed in the recent years to analyze engine cycle-to-cycle variability. In this work, phase-invariant proper orthogonal decomposition analysis, conditional averaging and triple and quadruple proper orthogonal decomposition methods are first introduced and applied to a large database of particle image velocimetry data from a well-known research engine. Results are discussed with particular emphasis on the capability of the methods to perform both quantitative and qualitative evaluations on cycle-to-cycle variability. Second, a new quadruple proper orthogonal decomposition methodology is proposed and compared to those available in the literature. All the methods are found to be helpful to identify the turbulent structures responsible for cycle-to-cycle variability. They can be equally applied to both experimental and numerical datasets to analyze turbulent fields in detail and to make comparisons. [ABSTRACT FROM AUTHOR]