In-cylinder imaging diagnostics for internal combustion engines provide rich information on the structure and evolution of reaction zone features, which affect both engine out emissions and efficiency. However, the most common analysis of in-cylinder combustion luminosity imaging considers ensemble averaged images, which are not suitable for characterizing processes that vary significantly between cycles, such as ignition and soot formation and oxidation. Here, a robust image segmentation algorithm is presented for feature extraction from single-cycle in-cylinder combustion images and is used with a ‘combination of interpretations’ (COI) approach to analyze OH*-chemiluminescence imaging of premixed and non-premixed natural gas combustion modes in an optically-accessible reciprocating engine.

Dynamic thresholding and region size filtering are combined with watershed segmentation to create a parameterized adaptive watershed (PAW) segmentation algorithm. The fusion of these segmentation methods is novel to combustion imaging and is demonstrated to provide quantified improvement relative to the current state of the art segmentation methods; PAW segmentation provides increased sensitivity for early ignition processes, and more robustly identifies the reaction zones at later stages of combustion. The PAW algorithm requires no adjustment between the two considered combustion modes or for any stage of the combustion process. The reliability of the PAW output enables feature extraction of individual reaction zone location and area from the combustion images using a polar-sector coordinate system for COI analysis. This approach characterizes the cyclic variability of individual fuel jets, identifies coupling of auto-ignition behavior between adjacent reaction zones, and demonstrates systematic errors arising from measurement of auto-ignition in ensemble averaged images. Application of PAW segmentation and the analysis approach presented here can provide more complete characterization of other spatially-resolved internal combustion diagnostics, particularly where there is high process variability, overlapping image regions, or wide signal intensity ranges.

内燃发动机的缸内成像诊断可提供有关反应区特征的结构和演变的丰富信息，这些信息会影响发动机的废气排放和效率。但是，缸内燃烧光度成像的最常见分析考虑的是整体平均图像，这些图像不适合表征循环之间显着变化的过程，例如点火，烟灰形成和氧化。在此，提出了一种健壮的图像分割算法，用于从单循环缸内燃烧图像中提取特征，并将其与“解释组合”（COI）方法一起使用，以分析预混合和非预混合天然气的OH *化学发光成像光学可访问的往复式发动机的燃烧模式。

动态阈值和区域大小过滤与分水岭分割相结合，以创建参数化的自适应分水岭（PAW）分割算法。这些分割方法的融合对于燃烧成像来说是新颖的，并且被证明可以相对于当前的最新分割方法提供量化的改进。PAW分段可提高早期点火过程的灵敏度，并能更可靠地识别燃烧后期的反应区。PAW算法不需要在两种考虑的燃烧模式之间或在燃烧过程的任何阶段进行调整。PAW输出的可靠性能够使用极扇区坐标系进行COI分析，从燃烧图像中提取出各个反应区的位置和面积。这种方法表征了各个燃料喷嘴的周期性变化，确定了相邻反应区之间自燃行为的耦合，并证明了在整体平均图像中因自燃测量而产生的系统误差。PAW分段和此处介绍的分析方法的应用可以提供其他空间分辨的内燃机诊断的更完整表征，尤其是在过程可变性高，图像区域重叠或信号强度范围宽的情况下。