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A CAE-based optimization method is developed for Lube Oil Consumption (LOC) analysis of the piston-ring system. With accurate thermodynamic boundary conditions from 1D engine combustion simulation, piston motion, dynamics of piston ring, and characteristics of oil consumption are simulated using AVL Piston&Ring. The model is validated by comparing with available test data. Good match is achieved. The model is then applied to a diesel engine. The root cause of excessive LOC of the engine has been identified through CAE. The improved understanding has been applied to optimize the piston and piston ring. Engine dyno test, 1200-hour engine durability test, and 45000-kilometer vehicle test have been conducted to validate the optimized design. The experiment results are in good agreement with CAE predictions, and the oil consumption has been improved over the original design.

The winter of 2013-2014 provided an opportunity to operate off-road vehicles in cold weather for extended time as part of a vehicle/tier 4 diesel engine validation program. An unexpected area of study was the performance of high efficiency, on engine, fuel filters during continuous vehicle operation in cold weather. During the program we observed unexpected premature fuel filter plugging as indicated by an increase in pressure drop across the filter while in service. Field and laboratory testing was completed at John Deere and Donaldson to understand the cause of filter plugging. Although conditions were found where winter fuel additives could cause plugging of high efficiency filters, premature filter plugging occurred even when testing with #1 diesel fuel. This fuel contained no additives and was used at temperatures well above its cloud point.

Experimental studies of soot particles showed that the intensity ratio of amorphous and graphite layers measured by Raman spectroscopy correlates to soot oxidation reactivities, which is very important for regeneration of the diesel particulate filters and gasoline particulate filters. This physical mechanism is absent in all soot models. In the present paper, a novel two-layer soot model was proposed that considers the amorphous and graphite layers in the soot particles. The soot model considers soot inception, soot surface growth, soot oxidation by O2 and OH, and soot coagulation. It is assumed that amorphous-type soot forms from fullerene. No soot coagulation is considered in the model between the amorphous- and graphitic-types of soot. Benzene is taken as the soot precursor, which is formed from acetylene. The model was implemented into a commercial CFD software CONVERGE using user defined functions. A diesel engine case was simulated.

One of the factors preventing widespread use of Homogeneous Charge Compression Ignition or HCCI is the challenge of controlling the process under transient conditions. Current engine control technology does not have the ability to accurately control the individual cylinder states needed for consistent HCCI combustion. The material presented here is a new approach to engine control using a physics-based individual cylinder real time model to calculate the engine states and then controlling the engine with this state information. The model parameters and engine state information calculated within the engine controller can be used to calculate the required actuator positions so that the desired mass of air, fuel, and residual exhaust gas are achieved for each cylinder event. This approach offers a solution to the transient control problem that works with existing sensors and actuators.

An investigation has been carried out to examine the influence of up to 300 MPa injection pressure on engine performance and emissions. Experiments were performed on a 4 cylinder, 4 valve / cylinder, 4.5 liter John Deere diesel engine using the Ricardo Twin Vortex Combustion System (TVCS). The study was conducted by varying the injection pressure, Start of Injection (SOI), Variable Geometry Turbine (VGT) vane position and a wide range of EGR rates covering engine out NOx levels between 0.3 g/kWh to 2.5 g/kWh. A structured Design of Experiment approach was used to set up the experiments, develop empirical models and predict the optimum results for a range of different scenarios. Substantial fuel consumption benefits were found at the lowest NOx levels using 300 MPa injection pressure. At higher NOx levels the impact was nonexistent. In a separate investigation a Cambustion DMS-500 fast particle spectrometer, was used to sample and analyze the exhaust gas.

This paper describes the advantage of the integrated simulation platform and presents the results of performance simulations and the feed-forward air-fuel ratio (AFR) controller design of a new concept stoichiometric compression ignition (SCI) engine based on this platform. In this integrated simulation environment, the SCI engine was modeled in GT-Power and a simplified production engine control module (ECM) is implemented in Simulink/Matlab for the performance simulation and AFR control. The integrated engine and controller model was used to investigate constant-speed load-acceptance (CSLA) performance. During performance simulation, searching for operating conditions is difficult but critical for performance analysis. Trial and error method would require a long time to do. Based on the integrated simulation, a proportional-integral (PI) controller was designed to find the accurate operating conditions.

Mean value engine model (MVEM) is the basis of control design for advanced internal combustion engines. The engine performance transient process usually takes a few cycles. The MVEM provides an adequate accurate description of the engine dynamics with reasonable approximation by ignoring the heat loss and sub-cycle events. MVEM is very important for engine system control development, especially when the modern engine becomes more and more complicated when equipped with throttle, turbocharger and after-treatment systems. Usually the MVEM is developed based on data from engine tests, which is a costly and time consuming process. In this paper, the air path MVEM modeling method based on the 1D detail model is discussed for a turbocharged diesel engine. Simulation is applied to demonstrate the effectiveness of this new method. This approach could be used to get the MVEM for control design even before the prototype engine is available.

Accurate modeling of evaporating sprays is critical for diesel engine simulations. The standard spray and evaporation models in KIVA-3V tend to under-predict the vapor penetration, especially at high ambient pressure conditions. A sharp decrease of vapor penetration gradient is observed soon after the liquid spray is completely evaporated due to the lack of momentum sources beyond the liquid spray region. In this study, a gas particle model is implemented in KIVA-3V which tracks the momentum sources resulting from the evaporated spray. Lagrangian tracking of imaginary gas particles is considered until the velocity of the gas particle is comparable to that of the gas phase velocity. The gas particle continuously exchanges momentum with the gas phase and as a result the vapor penetrations are improved. The results using the present gas particle model is compared with experimental data over a wide range of ambient conditions and good levels of agreement are observed in vapor penetration.

Regulated and unregulated emissions (individual hydrocarbons, ethanol, aldehydes and ketones, polynuclear aromatic hydrocarbons (PAH), nitro-PAH, and soluble organic fraction of particulate matter) were characterized in engines utilizing duplicate ISO 8178-C1 eight-mode tests and FTP smoke tests. Certification No. 2 diesel (400 ppm sulfur) and three ethanol/diesel blends, containing 7.7 percent, 10 percent, and 15 percent ethanol, respectively, were used. The three, Tier II, off-road engines were 6.8-L, 8.1-L, and 12.5-L in displacement and each had differing fuel injection system designs. It was found that smoke and particulate matter emissions decreased with increasing ethanol content. Changes to the emissions of carbon monoxide and oxides of nitrogen varied with engine design, with some increases and some decreases. As expected, increasing ethanol concentration led to higher emissions of acetaldehyde (increases ranging from 27 to 139 percent).

A valid human biodynamic model is very useful for studying the human body's response to whole body vibration. Whole body vibration is one of the important factors in the study of vehicle ride comfort. The environmental vibrations are transferred to the human body through floor and seat. Seated posture is the most commonly used position in automobiles. Therefore, studying the human body response in a seated position has attracted a lot of attention. Because the human body is in direct contact with the seat, its design plays a very important role in vibration transmission. In seat design, two important components are seat suspension and cushion. The mechanical properties of these components are stiffness, damping and mass. These properties can be changed by adjusting cushion material and seat suspension linkages. In this paper, three types of seat models are used. The first one is a hard seat.

Inverse Numerical Acoustics (INA) is the process by which the measurements of the sound pressure near a vibrating object, such as an engine, can be used to reconstruct the surface velocities of the object. This is required when it is difficult to conduct measurement on the structure or when the full structural FE model is not available. In such scenarios, the INA technique allows to back calculate the operational vibrations based on operational near field pressure measurements. When the surface velocities of a vibrating object are known which are independent of the boundary conditions, then the object can be used as a source in any application to compute the sound pressure levels in the surrounding (far field). This paper describes an experimental procedure that relies on INA to characterize an engine noise source in this manner. To this end, a robotic manipulator first measured sound pressures at multiple points in the near acoustic field.

Pressure ringing phenomena in internal combustion engine are often observed in cylinder pressure measurement, which may be due to combustion dynamics, pressure oscillation inside the combustion chamber and/or inside a drilled probe hole for cylinder pressure sensor installation. In the present study, combustion process in a production DI diesel engine instrumented with pressure sensors in the cylinder head was analyzed using 3D combustion CFD simulation. Three combustion models (the CTC model with the Shell autoignition model, the Sage model with detailed chemistry, and the ECFM-3Z model) and three reaction mechanisms (the Shell autoignition model, the Chalmers reduced n-heptane mechanism, and the IFP PRF mechanism) were employed to validate their capability in capturing pressure ringing phenomena. Grid size within the drilled hole and speed of sound CFL number were varied to evaluate the effects on pressure ringing prediction.

As diesel engine emissions standards become increasingly stringent, a commonly employed method of emissions reduction by engine manufacturers is active control of inducted air and the use of EGR. A common system configuration includes actuators such as an EGR valve and a VGT are used to manipulate the air flow through a diesel engine to control desired in-cylinder conditions so that the combustion event produces reduced engine out emissions. This paper evaluates four different controller designs for control of a diesel engine air path using a VGT & EGR valve in order to explore trade-offs between system performance and system complexity: three built up from SISO transfer functions and one that is a fully multivariable design. Various performance metrics are analyzed to gauge the relative difference in performance capability while attempting to maintain simple controller architecture.

Hybrid electric vehicle with hydrogen fueled internal combustion engine could be an interim between current vehicles and fuel cell vehicles which are thought to be more of far term reality. This paper introduces the hydrogen engine powered parallel hybrid electric vehicle developed by Texas Tech University Advanced Vehicle Engineering Lab. It presents the innovative use of LabVIEW Real-Time controllers to develop the prototype vehicle powertrain control system. Then it discusses the hydrogen engine control issues and presents the development of the engine control unit by LabVIEW FPGA. Vehicle communication issues among the distribution management system are presented. The vehicle test results are also shown in this paper.

Interest in electric vehicles (EVs) has significantly increased from the last decade, as the whole world is concerned about the reduction of emission of greenhouse gas by reducing the use of fossil fuel in transportation. The primary issue for electric vehicles is to develop an energy storage system i.e battery that can enable high mileage, rapid charging, and high-performance driving. Hence, battery management is required to get maximum, safe, and consistent performance of electric vehicles when running in a variety of conditions. To get the most out of a battery, it's important to keep an eye on its operating conditions, especially temperature, which has been shown to have a direct impact on battery performance and life. So, a battery thermal management system (BTMS) is crucial in the control of the thermal behavior of the battery. A good system simulation tool can minimize the time and cost of designing such a complicated thermal management system.

Machine learning methods, such as decision trees and deep neural networks, are becoming increasingly important and useful for data analysis in various scientific fields including dynamics and control, signal processing, pattern recognition, fluid mechanics, and chemical synthesis, etc. For future engine design and performance optimization, there is an urgent need for a robust predictive model which could capture the major combustion properties such as autoignition and flame propagation of multicomponent fuels under a wide range of engine operating conditions, without massive experimental measurement or computational efforts. It will be shown that these long-held limitations and challenges related to complex fuel combustion and engine research could be readily solved by implementing machine learning methods.

The application of Computational Fluid Dynamics (CFD) for three-dimensional (3D) combustion analysis coupled with detailed chemistry in engine development is hindered by its expensive computational cost. Chemistry computation may occupy as much as 90% of the total computational cost. In the present paper, a new two-step iso-octane combustion model was developed for spark-ignited (SI) engine to maximize computational efficiency while maintaining acceptable accuracy. Starting from the model constants of an existing global combustion model, the new model was developed using an approach based on sensitivity analysis to approximate the results of a reference skeletal mechanism. The present model involves only five species and two reactions and utilizes only one uniform set of model constants. The validation of the new model was performed using shock tube and real SI engine cases.

In recent years, study on the ride comfort of vehicles has attracted wide attention. The vibration caused by the road is transmitted to the human body through the tire, suspension, vehicle body, and the seat. Since the human body is in contact with the seat and the vibration is transmitted directly to the human body through the seat, the seat pan angle plays an important role on the vibration response of the human body. Previous studies have explored the effects of different backrest designs on human vibration response, but ignored the effects of different seat pan angles. Therefore, this paper will use a human biodynamic model combined with a 6-DOF seat model to study the effect of seat pan angles and feet-floor interaction on human vibration response. Three cases are proposed: Case 1 has a seat pan angle 8°, Case 2 has a seat pan angle 13°, and Case 3 has a seat pan angle 17°.

Flame speed data reported in most literature are acquired in conventional apparatus such as the spherical combustion bomb and counterflow burner, and are limited to atmospheric pressure and ambient or slightly elevated unburnt temperatures. As such, these data bear little relevance to internal combustion engines and gas turbines, which operate under typical pressures of 10-50 bar and unburnt temperature up to 900K or higher. These elevated temperatures and pressures not only modify dominant flame chemistry, but more importantly, they inevitably facilitate pre-ignition reactions and hence can change the upstream thermodynamic and chemical conditions of a regular hot flame leading to modified flame properties. This study focuses on how auto-ignition chemistry affects flame propagation, especially in the negative-temperature coefficient (NTC) regime, where dimethyl ether (DME), n-heptane and iso-octane are chosen for study as typical fuels exhibiting low temperature chemistry (LTC).

In-cylinder thermal barrier coatings (TBCs) have the capability to reduce fuel consumption by reducing wall heat transfer and to increase exhaust enthalpy. Low thermal conductivity, low volumetric heat capacity thermal barrier coatings tend to reduce the gas-wall temperature difference, the driving potential for heat transfer from the gas to the combustion chamber surfaces. This paper presents a coupling between an analytical methodology for multi-layer coated wall surface temperature prediction with a fully calibrated production model in a commercial system-level simulation software package (GT-Power). The wall surface temperature at each time step was calculated efficiently by convolving the engine wall response function with the time-varying surface boundary condition, i. e., in-cylinder heat flux and coolant temperature. This tool allows the wall to be treated either as spatially uniform with one set of properties, or with independent head/piston/liner components.