Analysis of cycle-to-cycle variations in IC engines using highspeed Tomographic Particle-Image Velocimetry

现代内燃机的发展着眼于通过降低污染物排放和燃油消耗来提高能源效率。新的燃烧概念，如受控汽车点火(CAI)和均质充气压缩点火(HCCI)，需要在进气和压缩冲程期间产生的均质空气-燃料混合物，因此在点火之前。然而，在这些冲程中演变的三维非定常大尺度流动结构在空间和时间发展上受到不同周期的影响。一方面，这些周期间波动(CCV)的原因及其影响因素尚未完全分析。另一方面，抑制和控制这些循环变化的影响是成功应用上述燃烧概念不可或缺的先决条件。因此，要详细分析内燃机的三维和随时间变化的循环变化，就需要一种具有高空间和时间分辨率的体积测量技术。这项建议的目的是证明高速层析粒子图像测速仪(HS-TPIV)能够通过研究进气和压缩行程期间的CCV来分析这些循环变化，该参数集具有定义的进气条件。为此，将使用HS-TPIV测量气缸内的三维流场，并在三维POD算法的基础上进行分析，以确定影响循环变化发展的因素。

The development of modern combustion engines focuses on the increase of the energy efficiency by simultaneously reducing pollutant emissions and fuel consumption. Novel combustion concepts like controlled auto ignition (CAI) and homogeneous charge compression ignition (HCCI) require a homogeneous air-fuel mixture which is generated during the inlet and compression stroke, hence prior to ignition. However, the three-dimensional unsteady large-scale flow structures that evolve during those strokes are subject to variations from cycle to cycle with respect to their spatial and temporal development. On the one hand, the reasons for these cycle-to-cycle variations (CCV) and their influencing factors are not completely analyzed, yet. On the other hand, the suppression and controlled influence of these cyclic variations is an indispensable prerequisite for the successful application of the aforementioned combustion concepts. Hence, a detailed analysis of the three-dimensional and time-dependent cyclic variations in internal combustion engines demands for a volumetric measurement technique with high spatial and temporal resolution. The objective of this proposal is to prove that high-speed tomographic particle-image velocimetry (HS-TPIV) is capable of analyzing these cyclic variations by investigating the CCV during the inlet and compression stroke for an exemplary parameter set with defined inlet conditions. For this purpose, the three-dimensional flow field in the cylinder will be measured using HS-TPIV and analyzed on the basis of three-dimensional POD-algorithms to determine the influencing factors for the development of cycle-to-cycle variations.