Cycle-to-cycle variation analysis of two-colour PLIF temperature measurements calibrated with laser induced grating spectroscopy in a firing GDI engine

In-cylinder temperatures and their cyclic variations strongly influence many aspects of internal combustion engine operation, from chemical reaction rates determining the production of NOx and particulate matter to the tendency for auto-ignition leading to knock in spark ignition engines. Spatially resolved measurements of temperature can provide insights into such processes and enable validation of Computational Fluid Dynamics simulations used to model engine performance and guide engine design. This work uses a combination of Two-Colour Planar Laser Induced Fluorescence (TC-PLIF) and Laser Induced Grating Spectroscopy (LIGS) to measure the in-cylinder temperature distributions of a firing optically accessible spark ignition engine. TC-PLIF performs 2-D temperature measurements using fluorescence emission in two different wavelength bands but requires calibration under conditions of known temperature, pressure and composition. Here the TC-PLIF technique is calibrated in-situ using high precision (<1%) LIGS point measurements. Temperature distributions were recorded during the compression stroke for fired operation with Direct Injection and with Plenum Fuel Injection of three two-component fuels containing toluene and iso-octane. Temperature inhomogeneity was observed for all fuels and injection strategies, with mm-scale regions having temperatures up to 10% higher than the local environment. Charge cooling of 3% due to direct injection was resolved. Proper Orthogonal Decomposition (POD) was used to quantify the cycle-to-cycle variation of the temperature data. Low-order POD modes featured most of the cyclic variation in temperature and the corresponding mode coefficients were used to investigate correlations with combustion analysis, fuel injection strategies and toluene content of the fuel. Additionally, the low-order POD mode coefficients provided an opportunity to identify cycles containing local hotspots or outlier measurements.