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Variable valve lift strategies were evaluated using a Water Analog Engine Simulation Rig with 3-D Particle Tracking Velocimetry (3-D PTV) to measure the 3-D flow field at the end of the intake stroke. The measurements were carried out with a 4-valve, pent-roof type head. The intake valves were actuated by independently controlled servo systems to allow various valve lift profiles to be implemented in software. For each configuration, a minimum of 100 cycles were acquired and processed. Ensemble averaged 3-D mean and fluctuating flow fields were extracted. In addition, a number of integrated parameters (total and fluctuating kinetic energy; swirl, tumble and cross-tumble ratios) were calculated for each case.

With the introduction of common rail fuel injection system enabling multiple injections per stroke and stringent pollutant emission standards, the optimization and calibration of modern compression ignition direct injection (CIDI) engines become more complex. Thus, a simple and efficient tool for CIDI combustion simulation with arbitrary fuel injection profile is required today. A crank-angle resolved combustion model was developed and validated by using a fuel injection rig and an engine dynamometer for the parameterization. With the calibrated models, accurate prediction of the in-cylinder pressure and NOx and also a virtual dynamometer mapping are possible. The results from these virtual mappings can be used to calibrate the black-box combustion models in control-oriented, dynamic Mean Value Models (MVM).

The evolution of the flow field inside an IC engine during the intake stroke was studied using 2 different experimental techniques, namely the 2–D Particle Image Velocimetry (2–D PIV) and 3–D Particle Tracking Velocimetry (3–D PTV) techniques. Both studies were conducted using a water analog engine simulation rig. The head tested was a typical pent–roof head geometry with two intake valves and one exhaust valve, and the simulated engine operating point corresponded to an idle condition. For both the 2–D PIV and 3–D PTV experiments, high–speed CCD cameras were used to record the motion of the flow tracer particles. The camera frame rate was adjusted to correspond to 1/4° of crank angle (CA), hence ensuring excellent temporal resolution for velocity calculations. For the 2–D PIV experiment, the flow field was illuminated by an Argon–ion laser with laser–sheet forming optics and this laser sheet was introduced through a transparent piston crown to illuminate the center tumble plane.

Over the last decade, many methods have been proposed for estimating the in-cylinder combustion pressure or the torque from instantaneous crankshaft speed measurements. However, such approaches are typically computationally expensive. In this paper, an entirely different approach is presented to allow the real-time estimation of the in-cylinder pressures based on crankshaft speed measurements. The technical implementation of the method will be presented, as well as extensive results obtained for a V-6 S.I. engine while varying spark timing, engine speed, engine load and EGR. The method allows to estimate the in-cylinder pressure with an average estimation error of the order of 1 to 2% of the peak pressure. It is very general in its formulation, is statistically robust in the presence of noise, and computationally inexpensive.

Typically, the combustion analysis for S.I. engines is limited to the determination of the apparent heat release from in-cylinder pressure measurements, effectively using a single zone approach with constant properties determined at some average temperature. In this paper, we follow an approach consistent with the engine modeling approach (i.e., reverse modeling) to extract heat release rate from combustion pressure data. The experimental data used here solely consists of quantities measured in a typical engine dynamometer tests, namely the crank-angle resolved cylinder pressure, as well as global measurements of the A/F ratio, engine speed, load, EGR, air mass flow rate and temperature and exhaust emissions. We then perform a two-zone, crank-angle resolved analysis of the pressure data using variable composition and properties.