Infrared Borescopic Analysis of Ignition and Combustion Variability in a Heavy-Duty Natural-Gas Engine

Paper #:
  • 2018-01-0632

Published:
  • 2018-04-03
Abstract:
Optical imaging diagnostics of combustion are most often performed in the visible and near-ultraviolet spectral regions, in part because camera technology is most mature in these bands, but operating in the infrared (IR) provides a number of benefits. These benefits include access to emission lines of relevant chemical species (e.g. water, carbon dioxide, and carbon monoxide) and obviation of intensifiers (thus avoiding reduced spatial resolution and increased cost). High-speed IR in-cylinder imaging and extensive post-processing were used to investigate the relationships between infrared images, quantitative image-derived metrics (e.g. location of the flame centroid), and measurements made with in-cylinder pressure transducers (e.g. coefficient of variation of mean effective pressure). A Weichai 9.7-liter, inline-six, natural-gas-fueled engine was modified to enable exhaust-gas recirculation and to provide borescopic optical access to one cylinder for two Xenics cameras. A high-energy inductively coupled ignition system delivered 140 mJ of energy during each spark event. The engine was operated over a range of air-to-fuel ratios (1 to 1.68), degrees of exhaust-gas recirculation (0 to 26%), engine speeds (1000 to 1430 rev/min), and engine loads (indicated mean effective pressures of 6.8 to 9.4 bar). Strong signals from the 1.3-μm emission line of water were centered in the sensitivity band of the cameras (1.0 to 1.7 μm). Water emission can be used as a proxy for the flame front and burned-gas regions. Images were recorded every 5.5 to 8.0 degrees of crank angle (CAD); multiple measurements were interleaved to provide statistical imaging data every 0.5 CAD. The images and quantitative measurements derived therefrom show strong correlations with pressure-derived data. In addition, the greater cycle-to-cycle variation resulting from lean and dilute operation is apparent in the images and image-based metrics.
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