Near-Wall Flame and Flow Measurements in an Optically Accessible SI Engine

Abstract

Near-wall processes in internal combustion engines strongly affect heat transfer and pollutant emissions. With continuously improving capabilities to model near-wall processes, the demand for corresponding measurements increases. To obtain an in-depth understanding of the near-wall processes within spark-ignition engines, flame distributions and flow fields were measured simultaneously near the piston surface of an optically accessible engine operating with homogeneous, stoichiometric isooctane-air mixtures. The engine was operated at two engine speeds (800 rpm and 1500 rpm) and two different intake pressures (0.95 bar and 0.4 bar). Flame distributions were obtained at high spatial resolution using high-speed planar laser induced fluorescence of sulfur dioxide (SO 2). Particle tracking velocimetry was utilized to measure the flow field above the piston at high spatial resolution, which enabled the determination of hydrodynamic boundary layer profiles. Flame contours were extracted and statistical distributions of the burnt gas area determined. The burnt gas distributions were compared with the simultaneously recorded high-speed flow field measurements in the unburnt gas. A direct comparison with motored engine operation showed comparable boundary layer profiles until the flame approaches the wall. Flow acceleration due to flame expansion rapidly increases velocity gradients and the boundary layer development becomes highly transient. The interaction of flame and flow depends on the operating conditions, which results in a different evolution of burnt gas positions within the field-of-view. This has additional implications on the development of the velocity boundary layer. Depending on the operating conditions, the flame strongly affects the velocity boundary layer profiles resulting in boundary layer thicknesses (defined by 50% maximum velocity) in the order of 80−180 μm.

Deep feature learning of in-cylinder flow fields to analyze cycle-to-cycle variations in an SI engine

“…Rigorous experimental research is required to provide detailed data for the development of accurate models of engine phenomena and the reduction of CCVs. 1 Over the last decades, advancements in laser and imaging technology have enabled IC engine research to expand to high-speed, crank angle-resolved 2–4 and high-resolution boundary layer velocity and fuel film measurements 5–8 as well as multi-parameter measurements to examine the propagation of the early flame kernel. 9 With the increase in technology, also comes the ability of generating better statistics for flow and flame data obtained from advanced laser diagnostics in IC engines and the subsequent use of machine learning (ML)-based processing and analysis techniques to better understand the phenomena involved in engine CCV.…”

Correlation between heat transfer and flow in an internal combustion engine evaluated with a MEMS sensor

“…The advection speed decreases slightly during the compression stroke, and then, it increases rapidly. Schmidt et al 26 observed flow acceleration in front of a flame in an optically accessible engine, and thus, it is assumed that the combustion provoked this acceleration. The cyclic variability evaluated in standard deviation is strong, whereas it attenuates after the combustion.…”

Assessment and application of wavelet-based optical flow velocimetry (wOFV) to wall-bounded turbulent flows

“…PTV was calculated for a particle size range from 1 to 8 pixels and with a correlation window size of 8 pixels. PTV vectors were converted to a structured pixels 2 grid, as performed in previous boundary layer studies (Ding et al 2019 ; Schmidt et al 2021 ). This step was performed in DaVis using a “simple averaging / strong filter” scheme in DaVis, which provided the most reliable PTV results.…”

“…Optical flow is a method often referred to in literature as dense motion estimation, i.e., a velocity vector is calculated for every pixel in a digital image. Application of optical flow velocimetry (OFV) techniques have previously demonstrated increased accuracy and resolution over conventional correlation-based methods (Yuan et al 2007 ; Ruhnau et al 2007 ; Corpetti et al 2006 ; Héas et al 2012 ; Dérian et al 2013 ; Kadri-Harouna et al 2013 ; Schmidt and Sutton 2020 ; Schmidt et al 2021a , b ). Such studies have primarily focused on synthetic and experimental test cases involving analytical flows, isotropic turbulence and free shear flows.…”