Search Results

This paper investigates flow and combustion in a full-cycle simulation of a four-stroke, three-valve HCCI engine by visualizing the flow with pathlines. Pathlines trace massless particles in a transient flow field. In addition to visualization, pathlines are used here to trace the history, or evolution, of flow fields and species. In this study evolution is followed from the intake port through combustion. Pathline analysis follows packets of intake charge in time and space from induction through combustion. The local scalar fields traversed by the individual packets in terms of velocity magnitude, turbulence, species concentration and temperatures are extracted from the simulation results. The results show how the intake event establishes local chemical and thermal environments in-cylinder and how the species respond (chemically react) to the local field.

Appropriate spray modeling in multidimensional simulations of diesel engines is well known to affect the overall accuracy of the results. More and more accurate models are being developed to deal with drop dynamics, breakup, collisions, and vaporization/multiphase processes; the latter ones being the most computationally demanding. In fact, in parallel calculations, the droplets occupy a physical region of the in-cylinder domain, which is generally very different than the topology-driven finite-volume mesh decomposition. This makes the CPU decomposition of the spray cloud severely uneven when many CPUs are employed, yielding poor parallel performance of the spray computation. Furthermore, mesh-independent models such as collision calculations require checking of each possible droplet pair, which leads to a practically intractable O(np2/2) computational cost, np being the total number of droplets in the spray cloud, and additional overhead for parallel communications.

The University of Wisconsin - Madison FutureCar Team has designed and built a lightweight, charge sustaining, parallel hybrid-electric vehicle for entry into the 1999 FutureCar Challenge. The base vehicle is a 1994 Mercury Sable Aluminum Intensive Vehicle (AIV), nicknamed the “Aluminum Cow,” weighing 1275 kg. The vehicle utilizes a high efficiency, Ford 1.8 liter, turbo-charged, direct-injection compression ignition engine. The goal is to achieve a combined FTP cycle fuel economy of 23.9 km/L (56 mpg) with California ULEV emissions levels while maintaining the full passenger/cargo room, appearance, and feel of a full-size car. Strategies to reduce the overall vehicle weight are discussed in detail. Dynamometer and experimental testing is used to verify performance gains.

Optimal fuel injection strategies are obtained with a micro-genetic algorithm and an adaptive gradient method for a nonroad, medium-speed DI diesel engine equipped with a multi-orifice, asynchronous fuel injection system. The gradient optimization utilizes a fast-converging backtracking algorithm and an adaptive cost function which is based on the penalty method, where the penalty coefficient is increased after every line search. The micro-genetic algorithm uses parameter combinations of the best two individuals in each generation until a local convergence is achieved, and then generates a random population to continue the global search. The optimizations have been performed for a two pulse fuel injection strategy where the optimization parameters are the injection timings and the nozzle orifice diameters.

A numerical simulation and optimization study was conducted for a medium speed direct injection diesel engine. The engine's operating characteristics were first matched to available experimental data to test the validity of the numerical model. The KIVA-3V ERC CFD code was then modified to allow independent spray events from two rows of nozzle holes. The angular alignment, nozzle hole size, and injection pressure of each set of nozzle holes were optimized using a micro-genetic algorithm. The design fitness criteria were based on a multi-variable merit function with inputs of emissions of soot, NOx, unburned hydrocarbons, and fuel consumption targets. Penalties to the merit function value were used to limit the maximum in-cylinder pressure and the burned gas temperature at exhaust valve opening. The optimization produced a 28.4% decrease in NOx and a 40% decrease in soot from the baseline case, while giving a 3.1% improvement in fuel economy.

A study of statistical optimization of engine operating parameters was conducted. The objective of the study was to develop a strategy to efficiently optimize operating parameters of diesel engines with multiple injection and EGR capabilities. Previous studies have indicated that multiple injections with EGR can provide substantial simultaneous reductions in emissions of particulate and NOx from heavy-duty diesel engines, but careful optimization of the operating parameters is necessary in order to receive the full benefit of these combustion control techniques. The goal of the present study was to optimize the control parameters to reduce emissions and brake specific fuel consumption. An instrumented single-cylinder heavy-duty diesel engine was used with a prototype mechanically actuated (cam driven) fuel injection system.

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.

A study of partially premixed combustion (PPC) with non-oxygenated 91 pump octane number1 (PON) commercially available gasoline was performed using a heavy-duty (HD) compression-ignition (CI) 2.44 l Caterpillar 3401E single-cylinder oil test engine (SCOTE). The experimental conditions selected were a net indicated mean effective pressure (IMEP) of 11.5 bar, an engine speed of 1300 rev/min, an intake temperature of 40°C with intake and exhaust pressures of 200 and 207 kPa, respectively. The baseline case for all studies presented had 0% exhaust gas recirculation (EGR), used a dual injection strategy a -137 deg ATDC pilot SOI and a -6 deg ATDC main start-of-injection (SOI) timing with a 30/70% pilot/main fuel split for a total of 5.3 kg/h fueling (equating to approximately 50% load). Combustion and emissions characteristics were explored relative to the baseline case by sweeping main and pilot SOI timings, injection split fuel percentage, intake pressure, load and EGR levels.

The noise characteristics of four camera systems representative of those typically used for laser-imaging experiments (a back-illuminated slow-scan camera, a frame-straddling slow-scan camera, an intensified slow-scan camera and an intensified video-rate camera) were investigated, and the results are presented as a function of the signal level and illumination level. These results provide the maximum possible signal-to-noise ratio for laser-imaging experiments, and represent the limit of quantitative signal interpretation. A calibration strategy for engine data that limits the uncertainties associated with thermodynamic and optical correction was presented and applied to engine data acquired with two of the camera systems. When a rigorous analysis of the signal is performed it is seen that shot noise limits the quantitative interpretation of the data for most typical laser-imaging experiments, and obviates the use of single-pixel data.

A non-intrusive sensing technique to determine start of combustion for mixing-controlled compression-ignition engines was developed based on an accelerometer mounted to the engine block of a 4-cylinder automotive turbo-diesel engine. The sensing approach is based on a physics-based conceptual model for the signal generation process that relates engine block acceleration to the time derivative of heat release rate. The frequency content of the acceleration and pressure signals was analyzed using the magnitude-squared coherence, and a suitable filtering technique for the acceleration signal was selected based on the result. A method to determine start of combustion (SOC) from the acceleration measurements is presented and validated.

A correction for the turbulence dissipation, based on non-equilibrium turbulence considerations from rapid distortion theory, has been derived and implemented in combination with the RNG k - ε model in a KIVA-based code. This model correction has been tested and compared with the standard RNG k - ε model for the compression and the combustion phase of two heavy duty DI diesel engines. The turbulence behavior in the compression phase shows clear improvements over the standard RNG k - ε model computations. In particular, the macro length scale is consistent with the corresponding time scale and with the turbulent kinetic energy over the entire compression phase. The combustion computations have been performed with the characteristic time combustion model. With this dissipation correction no additional adjustments of the turbulent characteristic time model constant were necessary in order to match experimental cylinder pressures and heat release rates of the two engines.

A computer model developed for describing multicomponent fuel vaporization, and ignition in diesel engines has been applied in this study to understand cold-starting and the parameters that are of significant influence on this phenomena. This research utilizes recent improvements in spray vaporization and combustion models that have been implemented in the KIVA-II CFD code. Typical engine fuels are blends of various fuels species, i.e., multicomponent. Thus, the original single component fuel vaporization model in KIVA-II was replaced by a multicomponent fuel vaporization model (based on the model suggested by Jin and Borman). The modelhas been extended to model diesel sprays under typical diesel conditions, including the effect of fuel cetane number variation. Necessary modifications were carried out in the atomization and collision sub-models. The ignition model was also modified to account for fuel composition effects by modifying the Shell ignition model.

A two-dimensional transient Heat Conduction in Components code (HCC) was successfully set up and extensively used to calculate the temperature field existing in real engine combustion chambers. The Saul'yev method, an explicit, unconditionally stable finite difference method, was used in the code. Consideration of the gasket between the cylinder wall and head, and the air gap between the piston and liner were included in the code. The realistic piston bowl shape was modeled with a grid transformation and piston movement was considered. The HCC code was used to calculate the wall temperature of an Isuzu ceramic engine and a Caterpillar heavy-duty diesel engine. The code was combined with the KIVA-II code in an iterative loop, in which the KIVA-II code provided the instantaneous local heat flux on the combustion chamber surfaces, and the HCC code computed the time-averaged wall temperature distribution on the surfaces.

The three-dimensional KIVA code has been used to study the effects of multicomponent fuel droplet vaporization on diesel engine performance under both standard and cold-starting conditions. The code has also been updated with state-of-the-art submodels including: a wave breakup atomization model, drop drag with drop distortion, spray/wall interaction with sliding, rebounding, and breaking-up drops, multistep kinetics ignition and laminar-turbulent characteristic time combustion, wall heat transfer with unsteadiness and compressibility, and a crevice flow model. The baseline computational results are compared with experimental data from a single-cylinder Caterpillar research engine equipped with a high-pressure, electronically-controlled fuel injection system. The effect of multicomponent fuel droplet vaporization is studied, with particular attention to the effects of the injection ambient temperatures.

This study investigates the effect of air-injection during the late combustion period produced by an air-cell on emissions from a direct injected diesel engine. The engine considered is a Caterpillar 3401 test engine which was modeled with an air-cell included as part of the piston geometry. A version of the KIVA-II code with updated submodels for diesel combustion and emissions was modified to allow for geometries with walls interior to the domain. This modified version of KIVA-II was then used to model an air-cell equipped diesel engine for four different air-cell configurations. Of the four air-cell configurations simulated, one proved successful in reducing the predicted engine emissions by more than a factor of two while simultaneously reducing NOx by a slight amount, thus moving the engine off its particulate-NOx tradeoff curve defined by varying the fuel injection timing.

Variations in the in cylinder flow field which result from differences in the intake flow are known to have important effects on the performance and emissions behavior of diesel engines. The intake flow and combustion in a heavy duty DI diesel engine with a dual valve port have been simulated using the computational fluid dynamics code KIVA-3. Variation of the in-cylinder flow field has been achieved by varying the intake valve timing. Variations in the in-cylinder flow, including a range of length scales, degrees of inhomogeneity in a number of scalar and vector quantities, and the persistence of various flow structures, are compared, and their significance to combustion and emissions parameters are assessed. The interaction of fuel spray parameters, particularly spray-wall interaction with structures present in the flow field are evaluated.

Previous studies have shown the importance of the in-cylinder flow field which exists prior to fuel injection on performance and emissions behavior of direct injected (DI) diesel engines. Key parameters in the flow field are the turbulence level and the resolved structures, such as swirl and tumble flow. These characteristics are known to have significant effects on the fuel vaporization, droplet break-up, and fuel-air mixing. The relative importance of these effects is investigated through simulation of injection into a stirred, heated, constant volume combustion bomb, using the computational fluid dynamics codes KIVA-3 [9] and KIVA-II [10]. Initial conditions for these simulations are based on in-cylinder conditions which exist in a heavy duty DI diesel engine immediately prior to fuel injection.

The three-dimensional KIVA code has been used to study the effects of injection pressure and split injections on diesel engine performance and soot and NOx emissions. The code has been updated with state-of-the-art submodels including: a wave breakup atomization model, drop drag with drop distortion, spray/wall interaction with sliding, rebounding, and breaking-up drops, multistep kinetics ignition and laminar-turbulent characteristic time combustion, wall heat transfer with unsteadiness and compressibility, Zeldovich NOx formation, and soot formation with Nagle Strickland-Constable oxidation. The computational results are compared with experimental data from a single-cylinder Caterpillar research engine equipped with a high-pressure, electronically-controlled fuel injection system, a full-dilution tunnel for soot measurements, and gaseous emissions instrumentation.

Previous studies have shown that air motion affects the combustion process and therefore also the emissions in a DI diesel engine. Experimental studies indicate that higher engine speeds enhance the turbulence and this improves air and fuel mixing. However, there are few studies that address fundamental combustion related factors and possible limitations associated with very high speed engine operation. In this study, operation over a large range of engine speeds was simulated by using a multi-dimensional computer code to study the effect of speed on emissions, engine power, engine and exhaust temperatures. The results indicate that at higher engine speeds fuel is consumed in a much shorter time period by the enhanced air and fuel mixing. The shorter combustion duration provides much less available time for soot and NOx formations. In addition, the enhanced air/fuel mixing decreases soot and NOx by reducing the extent of the fuel rich regions.

Predictive models of soot formation during Diesel combustion are of great practical interest, particularly in light of newly proposed strict regulations on particulate emissions. A modified version of the phenomenological model of soot formation developed previously has been implemented in KIVA-II CFD code. The model includes major generic processes involved in soot formation during combustion, i.e., formation of soot precursors, formation of surface growth species, soot particle nucleation, coagulation, surface growth and oxidation. The formulation of the model within the KIVA-II is fully coupled with the mass and energy balances in the system. The model performance has been tested by comparison with the results of optical in-cylinder soot measurements in a single cylinder Cummins NH Diesel engine. The predicted soot volume fraction, number density and particle size agree reasonably well with the experimental data.