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.

A telemetry linkage system has been developed for piston temperature measurements in a direct-injection diesel engine. In parallel with the development of the telemetry linkage system, fast response thermocouples were installed at three piston locations - two on the bowl surface and one on the crown surface. A novel design was used to achieve electrical continuity between the piston and the connecting rod by means of a flexible steel strap pivoted on the piston skirt. The telemetry linkage system was then used to transport the electrical wires from the thermocouples to the external data acquisition system. A series of tests was run to determine the effects of location and load on piston surface temperatures. Surface temperature profiles varied substantially among the three locations, reflecting the differences in the combustion and heat flow characteristics of their surrounding regions.

A naturally-aspirated, Miller cycle, Spark-Ignition (SI) engine that controls output with variable intake valve closure is compared to a conventionally-throttled engine using computer simulation. Based on First and Second Law analyses, the two load control strategies are compared in detail through one thermodynamic cycle at light load conditions and over a wide range of loads at 2000 rpm. The Miller Cycle engine can use late intake valve closure (LIVC) to control indicated output down to 35% of the maximum, but requires supplemental throttling at lighter loads. The First Law analysis shows that the Miller cycle increases indicated thermal efficiency at light loads by as much as 6.3%, primarily due to reductions in pumping and compression work while heat transfer losses are comparable.

A prototype chromel-alumel overlapping thin-film thermocouple (TFTC) has been developed for transient heat transfer measurements in ceramic-coated combustion chambers. The TFTC has been evaluated using various metallurgical techniques such as scanning electron microscopy, energy dispersive x-ray detection, and Auger electron spectroscopy. The sensor was calibrated against a standard thermocouple in ice, boiling water, and a furnace at 1000°C. The microstructural and chemical analysis of the thin-films showed the alumel film composition was very similar to the bulk material, while the chromel film varied slightly. An initial set of ceramic plug surface temperatures was taken while motoring and firing the engine at 1900 rpm to verify thermocouple operation. The data shows a 613 K mean temperature and a 55 K swing for the ceramic surface compared with a 493 K mean temperature and a 20 K swing for the metal surface at the same location.

The paper deals with the investigation of pressure, flow and temperature sensor performance under unsteady conditions using advanced 1-D codes for simulation of engine operation. Approach of internal combustion engine (ICE) sensor modeling in an engine simulation code is described. Some new external modules have been developed to couple engine-and-pipe model to sensors. Sensor dynamic and engine dynamic effects are separated by combining a sensor model with an engine model. The models were tuned to match real data with the goal of uncovering the transfer function between the measured signal and the actual signal. Procedure for estimation of the in-cylinder pressure pattern from distorted pattern at sensor location using empirical transfer function is presented. The developed model seems to have a wide application, e.g. for investigation of dynamical characteristics of lambda sensors or gas analyzer probes.

Increasing functionality of electronic control units has enhanced our ability to control engine operation utilizing calibration static maps that provide the values of several controllable variables. State-of-the-art simulation-based calibration methods permit the development of these maps with respect to extensive steady-state and limited transient operation of particular driving cycles. However, each individual driving style is different and rarely meets those test conditions. An alternative approach was recently implemented that considers the derivation of these maps while the engine is running the vehicle. In this approach, a self-learning controller selects in real time the optimum values of the controllable variables for the sequences of engine operating point transitions, corresponding to the driver's driving style.

The number of control actuators available on spark-ignition engines is rapidly increasing to meet demand for improved fuel economy and reduced exhaust emissions. The added complexity greatly complicates control strategy development because there can be a wide range of potential actuator settings at each engine operating condition, and map-based actuator calibration becomes challenging as the number of control degrees of freedom expand significantly. Many engine actuators, such as variable valve actuation and flow control valves, directly influence in-cylinder combustion through changes in gas exchange, mixture preparation, and charge motion. The addition of these types of actuators makes it difficult to predict the influences of individual actuator positioning on in-cylinder combustion without substantial experimental complexity.

Flexible fuel vehicles (FFVs) are able to operate on a blend of ethanol and gasoline in any volumetric concentration of up to 85% ethanol (93% in Brazil). The estimation of ethanol content is crucial for optimized and robust performance in such vehicles. Even if an ethanol sensor is utilized, an estimation scheme independent of the ethanol sensor measurement retains advantages in enhancing the reliability of ethanol estimation and allowing on-board diagnostics. It is well-known that an exhaust gas oxygen (EGO) sensor could be utilized to estimate the ethanol content, which exploits the difference in stoichiometric air-to-fuel ratio (SAFR) between ethanol (9.0) and gasoline (14.6). The SAFR-based ethanol estimation has been shown to be prone to large errors with mass air flow sensor bias and/or fuel injector shift.

Adjusting the Residual Gas Fraction (RGF) by means of Variable Valve Actuation (VVA) is a strong candidate for controlling the ignition timing in Homogeneous Charge Compression Ignition (HCCI) engines. However, at high levels of residual gas fraction, insufficient mixing can lead to the presence of considerable temperature and composition variations. This paper extends previous modeling efforts to include the effect of RGF distribution on the onset of ignition and the rate of combustion using a multi-dimensional fluid mechanics code (KIVA-3V) sequentially with a multi-zone code with detailed chemical kinetics. KIVA-3V is used to simulate the gas exchange processes, while the multi-zone code computes the combustion event. It is shown that under certain conditions the effect of composition stratification is significant and cannot be captured by a single-zone model or a multi-zone model using only temperature zones.

Variable geometry turbines (VGT) are of particular interest to advanced diesel powertrains for future conventional trucks, since they can dramatically improve system transient response to sudden changes in speed and load, characteristic of automotive applications. VGT systems are also viewed as the key enabler for the application of the EGR system for reduction of heavy-duty diesel emissions. This paper applies an artificial neural network methodology to VGT modeling in order to enable representation of the VGT characteristics for any blade (nozzle) position. Following validation of the ANN model of the baseline, fixed geometry turbine, the VGT model is integrated with the diesel engine system. The latter is linked to the driveline and the vehicle dynamics module to form a complete, high-fidelity vehicle simulation.

Conventional spark plugs ignite a fuel-air mixture via an electric-to-plasma energy transfer; the effectiveness of which can be described by an electric-to-plasma energy efficiency. Although conventional spark plug electric-to-plasma efficiencies have historically been viewed as adequate, it might be wondered how an increase in such an efficiency might translate (if at all) to improvements in the flame initiation period and eventual engine performance of a spark-ignition engine. A modification can be made to the spark plug that places a peaking capacitor in the path of the electrical current; upon coil energizing, the stored energy in the peaking capacitor substantially increases the energy delivered by the spark. A previous study has observed an improvement in the electric-to-plasma energy efficiency to around 50%, whereas the same study observed conventional spark plug electric-to-plasma energy efficiency to remain around 1%.

We have developed an artificial neural network (ANN) based combustion model and have integrated it into a fluid mechanics code (KIVA3V) to produce a new analysis tool (titled KIVA3V-ANN) that can yield accurate HCCI predictions at very low computational cost. The neural network predicts ignition delay as a function of operating parameters (temperature, pressure, equivalence ratio and residual gas fraction). KIVA3V-ANN keeps track of the time history of the ignition delay during the engine cycle to evaluate the ignition integral and predict ignition for each computational cell. After a cell ignites, chemistry becomes active, and a two-step chemical kinetic mechanism predicts composition and heat generation in the ignited cells. KIVA3V-ANN has been validated by comparison with isooctane HCCI experiments in two different engines.

Cam-phasing is increasingly considered as a feasible Variable Valve Timing (VVT) technology for production engines. Additional independent control variables in a dual-independent VVT engine increase the complexity of the system, and achieving its full benefit depends critically on devising an optimum control strategy. A traditional approach relying on hardware experiments to generate set-point maps for all independent control variables leads to an exponential increase in the number of required tests and prohibitive cost. Instead, this work formulates the task of defining actuator set-points as an optimization problem. In our previous study, an optimization framework was developed and demonstrated with the objective of maximizing torque at full load. This study extends the technique and uses the optimization framework to minimize fuel consumption of a VVT engine at part load.

A comprehensive model for sprays emerging from high-pressure swirl injectors in DISI engines has been developed accounting for both primary and secondary atomization. The model considers the transient behavior of the pre-spray and the steady-state behavior of the main spray. The pre-spray modeling is based on an empirical solid cone approach with varying cone angle. The main spray modeling is based on the Liquid Instability Sheet Atomization (LISA) approach, which is extended here to include the effects of swirl. Mie Scattering, LIF, PIV and Laser Droplet Size Analyzer techniques have been used to produce a set of experimental data for model validation. Both qualitative comparisons of the evolution of the spray structure, as well as quantitative comparisons of spray tip penetration and droplet sizes have been made. It is concluded that the model compares favorably with data under atmospheric conditions.

The development and use of a simulation of an Integrated Starter Alternator (ISA) for a High Mobility Multi-purpose Wheeled Vehicle (HMMWV) is presented here. While the primary purpose of an ISA is to provide electric power for additional accessories, it can also be utilized for mild hybridization of the powertrain. In order to explore ISA's potential for improving HMMWV's fuel economy, an ISA model capable of both producing and absorbing mechanical power has been developed in Simulink. Based on the driver's power request and the State of Charge of the battery (SOC), the power management algorithm determines whether the ISA should contribute power to, or absorb power from the crankshaft. The system is also capable of capturing some of the braking energy and using it to charge the battery. The ISA model and the power management algorithm have been integrated in the Vehicle-Engine SIMulation (VESIM), a SIMULINK-based vehicle model previously developed at the University of Michigan.

This paper describes a test cell setup for concurrent running of a real engine and a vehicle system simulation, and its use for evaluating engine performance when integrated with a conventional and a hybrid electric driveline/vehicle. This engine-in-the-loop (EIL) system uses fast instruments and emission analyzers to investigate how critical in-vehicle transients affect engine system response and transient emissions. Main enablers of the work include the highly dynamic AC electric dynamometer with the accompanying computerized control system and the computationally efficient simulation of the driveline/vehicle system. The latter is developed through systematic energy-based proper modeling that tailors the virtual model to capture critical powertrain transients while running in real time. Coupling the real engine with the virtual driveline/vehicle offers a chance to easily modify vehicle parameters, and even study two different powertrain configurations.

Variable Valve Actuation (VVA) technology provides high potential in achieving high performance, low fuel consumption and pollutant reduction. However, more degrees of freedom impose a big challenge for engine characterization and calibration. In this study, a simulation based approach and optimization framework is proposed to optimize the setpoints of multiple independent control variables. Since solving an optimization problem typically requires hundreds of function evaluations, a direct use of the high-fidelity simulation tool leads to the unbearably long computational time. Hence, the Artificial Neural Networks (ANN) are trained with high-fidelity simulation results and used as surrogate models, representing engine's response to different control variable combinations with greatly reduced computational time. To demonstrate the proposed methodology, the cam-phasing strategy at Wide Open Throttle (WOT) is optimized for a dual-independent Variable Valve Timing (VVT) engine.

The transient cone angle of pressure swirl sprays from injectors intended for use in gasoline direct injection engines was measured from 2D Mie scattering images. A variety of injectors with varying nominal cone angle and flow rate were investigated. The general cone angle behavior was found to correlate well qualitatively with the measured fuel line pressure and was affected by the different injector specifications. Experimentally measured modulations in cone angle and injection pressure were forced on a comprehensive spray simulation to understand the sensitivity of pulsating injector boundary conditions on general spray structure. Ignoring the nozzle fluctuations led to a computed spray shape that inadequately replicated the experimental images; hence, demonstrating the importance of quantifying the injector boundary conditions when characterizing a spray using high-fidelity simulation tools.

The emerging Variable Valve Timing (VVT) technology complicates the estimation of air flow rate because both intake and exhaust valve timings significantly affect engine's gas exchange and air flow rate. In this paper, we propose to use Artificial Neural Networks (ANN) to model the air flow rate through a 2.4 liter VVT engine with independent intake and exhaust camshaft phasers. The procedure for selecting the network architecture and size is combined with the appropriate training methodology to maximize accuracy and prevent overfitting. After completing the ANN training based on a large set of dynamometer test data, the multi-layer feedforward network demonstrates the ability to represent air flow rate accurately over a wide range of operating conditions. The ANN model is implemented in a vehicle with the same 2.4 L engine using a Rapid Prototype Controller.

An accurate air flow rate model is critical for high-quality air-fuel ratio control in Spark-Ignition engines using a Three-Way-Catalyst. Emerging Variable Valve Timing technology complicates cylinder air charge estimation by increasing the number of independent variables. In our previous study (SAE 2004-01-3054), an Artificial Neural Network (ANN) has been used successfully to represent the air flow rate as a function of four independent variables: intake camshaft position, exhaust camshaft position, engine speed and intake manifold pressure. However, in more general terms the air flow rate also depends on ambient temperature and pressure, the latter being largely a function of altitude. With arbitrary cam phasing combinations, the ambient pressure effects in particular can be very complex. In this study, we propose using a separate neural network to compensate the effects of altitude on the air flow rate.