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The quasi-steady assumption is often used to determine the flow resistance of highly compressible critical or near-critical (approaching sonic velocity) pulsating flows through engine valves, EGR system and other flow restrictions for modeling and control. The quasi-steady assumption utilizes steady (non-pulsating) flow results where the discharge coefficient (Cd) of flow nozzles/orifices is solely a function of Reynolds number (Re), and Cd is constant at high Re. There exists some literature addressing the flow resistance of incompressible pulsating flows and also for compressible steady flow, but virtually no literature for the highly compressible, critical/near-critical pulsating flow typical in engines. In this work, the Cd of a square edged orifice placed in the exhaust stream of a four-cylinder diesel engine was measured and found not to be a sole function of Re, but correlated to Re.

EGR flow rate measurements on production engines are commonly made using orifices or flow nozzles. These devices increase the exhaust pressure resulting in an increase in fuel consumption. Further, they are accurate and recommended only for steady state flow, and not pulsating flow encountered in engines. In this work measurements made at the EGR cooler, such as the pressure drop across it and the inlet and outlet temperatures, have been examined for their ability to predict mass flow rate through the cooler. Direct measurements of pulsating flow through an EGR cooler were made by routing all of the engine exhaust flow through the cooler while making accurate measurements of the air and fuel flowing into the engine. Based on dimensional arguments, the flow resistance of the EGR cooler was characterized by a loss coefficient within the standard head loss energy equation.

Orifices, flow nozzles and arbitrarily shaped flow obstructing flow measurement devices are widely used to estimate EGR flow rates in engines, and also used to model flow restricting components like valves in engine analysis tools such as GT-Power. The standard assumptions about the flow discharge coefficient and its variation with Reynolds number are based on investigations of orifices across steady non-pulsating flows, widely reported in literature. In this work, the discharge coefficient for steady state pulsating flow as well as accelerating pulsating flow, commonly encountered during steady state and dynamic engine operation respectively, were investigated by installing an orifice on the exhaust side of a naturally aspirated diesel engine, while making reference flow measurements with a Laminar Flow Element on the intake side.

A hybrid calibration model combining dimensional and empirical modeling has been used to model transient smoke and demonstrate transient smoke reduction strategies during the turbocharger lag period of an electronically controlled heavy-duty diesel engine. This new hybrid approach termed the Non-Parametric Reduced Dimensionality approach (NPRD) uses GT-Power to transform the engine operating parameter model input space to a more fundamental, lower dimensional and less correlated model input space. A non-parametric nearest neighbor approach is then applied over this transformed model input space to make new predictions. The NPRD approach was used to predict transient FTP emissions of cumulative particulate matter (PM) within 7% of measured value, based solely on steady state training data.

System level modeling was used to develop a suitable control strategy for Diesel Homogeneous Charge Compression Ignition (HCCI) transient operation. Intake temperature and pressure, engine speed, engine load, cylinder wall temperature, exhaust gas recirculation, etc. all significantly affect combustion phasing generating a scenario where simple ECU mapping techniques prove inadequate. Two-stage fuels such as diesel fuel pose additional challenges for accurate combustion control. Low-temperature cool-flame chemical heat release can significantly alter the thermodynamic state of the trapped gaseous mixture and hence combustion phasing. Operator and environmentally induced transients can rapidly alter combustion phasing parameters suggesting a need for model-based control. A model-based control strategy featuring the identified essential physics has been developed to control diesel HCCI combustion phasing through transient operation.

Two advanced combustion models have been validated with the KIVA-3V Release 2 code in the context of two-stage combustion in a heavy duty diesel engine. The first model uses CHEMKIN to directly integrate chemistry in each computational cell. The second model accounts for flame propagation with the G-equation, and CHEMKIN predicts autoignition and handles chemistry ahead of and behind the flame front. A Damköhler number criterion was used in flame containing cells to characterize the local mixing status and determine whether heat release and species change should be a result of flame propagation or volumetric heat release. The purpose of this criterion is to make use of physical and chemical time scales to determine the most appropriate chemistry model, depending on the mixture composition and thermodynamic properties of the gas in each computational cell.

A comprehensive system level modeling approach is used to understand the effects of the various physical actuators during diesel HCCI transients. Control concepts during transient operations are simulated using a set of actuators suitable for combustion control in diesel HCCI engines (intake valve actuation, injection timing, cooled EGR, intake boost pressure and droplet size). The impact of these actuating techniques on the overall engine performance is quantified by investigating the amount of actuation required, timing of actuation and the use of a combination of actuators. Combined actuation improved actuation space that can be used to phase combustion timing better and in extending the operating range. The results from transient simulations indicate that diesel HCCI operation would benefit from the combined actuation of intake valve closure, injection timing, boost and cooled EGR.

The heavy-duty diesel engine industry is required to meet stringent emission standards. There is also the demand for more fuel efficient engines by the customer. In a previous study on an engine with variable intake valve closure timing, the authors found that an early single injection and accompanying premixed charge compression ignition (PCCI) combustion provides advantages in emissions and fuel economy; however, unacceptably high peak pressures and rates of pressure-rise impose a severe operating constraint. The use of a Pressure Reactive Piston assembly (PRP) as a means to limit peak pressures is explored in the present work. The concept is applied to a heavy-duty diesel engine and genetic algorithms (GA) are used in conjunction with the multi-dimensional engine simulation code KIVA-3V to provide an optimized set of operating variables.

Ignition dwell is defined as the interval between end of fuel injection and start of combustion in early injection diesel combustion that exhibits HCCI-like characteristics. In this project, the impact of in-cylinder temperature and fuel-air mixing on the ignition dwell was investigated. The engine cycle was simulated using the 3-D CFD code KIVA-3V. Work done by Klingbeil (2002) has shown that ignition dwell allows more time for fuel and air to mix and drastically reduces emissions of NOX and particulate matter. Temperature is known to have a direct impact on the duration of ignition dwell. However, initial fuel-air distribution and mixing (i.e. at the end of fuel injection) may also impact the duration of ignition dwell. To investigate this, variations in EGR, fuel injection timing, engine valve actuation and swirl were simulated. The aim was to use these techniques to generate varying levels of fuel-air mixing and to check if ignition dwell was affected.

Neural networks are useful tools for optimization studies since they are very fast, so that while capturing the accuracy of multi-dimensional CFD calculations or experimental data, they can be run numerous times as required by many optimization techniques. This paper describes how a set of neural networks trained on a multi-dimensional CFD code to predict pressure, temperature, heat flux, torque and emissions, have been used by a genetic algorithm in combination with a hill-climbing type algorithm to optimize operating parameters of a diesel engine over the entire speed-torque map of the engine. The optimized parameters are mass of fuel injected per cycle, shape of the injection profile for dual split injection, start of injection, EGR level and boost pressure. These have been optimized for minimum emissions. Another set of neural networks have been trained to predict the optimized parameters, based on the speed-torque point of the engine.

Progress on the development and validation of a CFD model for diesel engine combustion and flow is described. A modified version of the KIVA code is used for the computations, with improved submodels for liquid breakup, drop distortion and drag, spray/wall impingement with rebounding, sliding and breaking-up drops, wall heat transfer with unsteadiness and compressibility, multistep kinetics ignition and laminar-turbulent characteristic time combustion models, Zeldovich NOx formation, and soot formation with Nagle Strickland-Constable oxidation. The code also considers piston-cylinder-liner crevice flows and allows computations of the intake flow process in the realistic engine geometry with two moving intake valves. Significant progress has been made using a modified RNG k-ε turbulence model, and a multicomponent fuel vaporization model and a flamelet combustion model have been implemented.