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

现代内燃机的发展重点是通过同时减少污染物排放和燃料消耗来提高能源效率。新型的燃烧概念如受控自动点火（CAI）和均质充量压缩点火（HCCI）需要在进气和压缩冲程期间（因此在点火之前）产生均质的空气-燃料混合物。然而，在这些中风期间演变的三维非定常大尺度流动结构的变化，从周期到周期的空间和时间的发展。一方面，这些周期间变化（CCV）的原因及其影响因素还没有完全分析。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的必不可少的先决条件。因此，在内燃机中的三维和时间相关的循环变化的详细分析需要具有高的空间和时间分辨率的体积测量技术。本提案的目的是证明高速层析粒子图像测速（HS-TPIV）能够通过研究具有定义的进气条件的示例性参数集的进气和压缩冲程期间的CCV来分析这些循环变化。为此，将使用HS-TPIV测量气缸内的三维流场，并根据三维POD算法进行分析，以确定循环变化发展的影响因素。

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.