Towards Identifying Flame Patterns in Multiple, Late Injection Schemes on a Single-Cylinder Optical Diesel Engine

Hardalupas, Y.; Hong, Ch.; Keramiotis, Ch.; Ramaswamy, G.K.; Soulopoulos, N.; Taylor, A. M. K. P.; Touloupis, Dimitrios P.; Vourliotakis, George; Founti, M.

doi:10.1080/00102202.2016.1211884

Cited by 1 publication

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References 45 publications

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“…Often, either a set of single-cycle or a set of ensemble averaged images are presented to qualitatively illustrate combustion structures and support discussion of quantitative measurements such as AHRR and ignition delay (e.g. [24][25][26]) in a compact and intuitive format. This approach can be problematic when analyzing combustion processes with high spatial and temporal variability, such as auto-ignition processes (e.g.…”

Section: Introductionmentioning

confidence: 99%

Robust image segmentation for feature extraction from internal combustion engine in-cylinder images

Rochussen¹,

Kirchen²

2020

Meas. Sci. Technol.

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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.

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Section: Introductionmentioning

confidence: 99%

Robust image segmentation for feature extraction from internal combustion engine in-cylinder images

Rochussen¹,

Kirchen²

2020

Meas. Sci. Technol.

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show abstract

Towards Identifying Flame Patterns in Multiple, Late Injection Schemes on a Single-Cylinder Optical Diesel Engine

Abstract: The work investigates the effect of various post-injection strategies on the flame patterns in a Ricardo Hydra optical single cylinder light duty diesel engine, operated in a partially premixed combustion mode (PPC), under low load (IMEP: ca. 2.3 bar) low speed (1200 rpm

Cited by 1 publication

References 45 publications

Robust image segmentation for feature extraction from internal combustion engine in-cylinder images

Robust image segmentation for feature extraction from internal combustion engine in-cylinder images

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