Abstract
Computational fluid dynamics (CFD) simulations of the in-cylinder flow field are widely used in the design of internal combustion engines (ICEs) and must be validated against experimental measurements to enable a robust predictive capability. Such validation is complicated by the presence of both large-scale cycle-to-cycle variations and small-scale turbulent fluctuations in experimental measurements of in-cylinder flow fields. Reynolds averaged Navier-Stokes (RANS) simulations provide overall flow structures with acceptable accuracy and affordable computational cost for widespread industrial applications. Due to the nature of averaging physical parameters in RANS, its validation against experimental results obtained by particle image velocimetry (PIV) requires consideration of how best to average or filter the measured turbulent flows. In this paper, PIV measurements on the cross-tumble plane were recorded every five crank angle degrees for [Formula: see text] cycles during the intake process of a motored, optically accessible spark ignition direct injection (SIDI) engine. Several methods including ensemble averaging, speed-based averaging and low-order proper orthogonal decomposition (POD) reconstruction were applied to remove the fluctuations from experimental PIV vector fields and thus enable comparison to RANS simulations. Quantitative comparison metrics were used to evaluate the performances of each method in representing the intake jet. Recommendations are made on how to provide a fair validation between measured data and simulation results in highly fluctuating flow fields such as the engine intake jet.
Highlights
Internal combustion engines (ICEs) continue to serve as the main source of power for ground transportation worldwide, as vehicles with an ICE retained over 90% of the worldwide market share in 2018.1 Predictions show that the market share of battery electric vehicles (BEVs) may only reach 11%–28% by 2040,2 despite its rapid growth in recent years
Among all the 300 cycles, 77 of them have the jet angle as the unsatisfied criterion; the jet penetration length, nominal jet width and kinetic energy fraction each have 33, 37 and 32 cycles respectively; the relevance index criterion was not used in 82 cycles; the remaining 39 cycles have ‘tied’ criteria, meaning that multiple criteria give the same order of approximation and the extra one will not have any effect even if it was considered. (Cycle A is one of (a) these 39 cycles.) The results shows that the jet angle and relevance index thresholds may be too restrictive, resulting in the higher mode number when including the fifth criterion
Metrics were developed to quantify the profile of the central intake jet generated by the collision of the two intake air streams, which allowed a detailed comparison between different flow fields containing the central intake jet
Summary
Internal combustion engines (ICEs) continue to serve as the main source of power for ground transportation worldwide, as vehicles with an ICE (including both conventional ICE and hybrid vehicles) retained over 90% of the worldwide market share in 2018.1 Predictions show that the market share of battery electric vehicles (BEVs) may only reach 11%–28% by 2040,2 despite its rapid growth in recent years. The homogeneity of the mixture relies greatly on the intake air speed, flow structure and turbulence level, and it further influences the combustion quality and the emissions from the engine.[6] The development of flow simulation models using computational fluid dynamics (CFD) tools such as the Reynolds averaged Navier-Stokes (RANS) and the large eddy simulation (LES) approaches[7,8,9,10] have been accelerating engine design and reducing the number of engine tests, yet the models still need to be validated by experimental measurements to provide more accurate predictions of the highly fluctuating and reacting flow.[11] Both RANS and LES simulate the large structures of the in-cylinder flow, and LES provides an additional access to the cycle-to-cycle variation in the engine.[12] the computational cost of LES is considerably higher than RANS, and RANS is still widely used in industrial applications where the time for the engine research & development cycle is critical and limited
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