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In this paper, we develop a methodology to enable the isolation of the heat release contribution of knocking combustion from flame-propagation combustion. We first address the empirical modeling of individual non-autoigniting combustion history using the Wiebe function, and subsequently apply this methodology to investigate the effect of autoignition on the heat release history of knocking cycles in a spark ignition (SI) engine. We start by re-visiting the Wiebe function, which is widely used to model empirically mass burned histories in SI engines. We propose a method to tune the parameters of the Wiebe function on a cycle-by-cycle basis, i.e., generating a different Wiebe to suitably fit the heat release history of each cycle. Using non-autoigniting cycles, we show that the Wiebe function can reliably simulate the heat release history of an entire cycle, if only data from the first portion of the cycle is used in the tuning process.

The advantages of having higher stiffness to weight ratio and strength to weigh ratio that composite materials have resulted in an increased interest in them. In automotive engineering, the weight savings has positive impacts on other attributes like fuel economy and possible noise, vibration and harshness (NVH). The driveline of an automotive system can be a target for possible use of composite materials. The design of the driveshaft of an automotive system is primarily driven by its natural frequency. This paper presents an exact solution for the vibration of a composite driveshaft with intermediate joints. The joint is modeled as a frictionless internal hinge. The Euler-Bernoulli beam theory is used. Lumped masses are placed on each side of the joint to represent the joint mass. Equations of motion are developed using the appropriate boundary conditions and then solved exactly.

Crankshaft fillet is subjected to a cyclic bending stress during operation. Fatigue cracks are observed at the fillet during the fatigue test. Compressive stresses are generated by deep-rolling process in order to increase the surface hardness and improve the fatigue strength. To examine the deep-rolling effect, the residual stresses at the fillet need to be investigated. Incremental hole drilling and ISSR (interferometric strain/slope rosette) method is applied to measure the residual stresses at the bottom of the fillet. Incremental hole drilling process is to gradually remove material and mill a hole on the specimen surface in order to relax stress. The ISSR is composed of three micro-indentations, which are indented near the hole and would generate interferometric fringe patterns upon incident laser beam. With incremental drilling, stress relaxation causes the relieved strains, which in turn cause the shifts of interferometric patterns.

With increased demand for improvements in the efficiency and operation of all automotive engine components, including those in the engine cooling system, there is a need to develop a set of virtual tools that can aid in both the evaluation and design of automotive components. In the case of automotive radiators, improvements are needed in the overall pressure drop as well as the coolant flow homogeneity across all radiator tubes. The latter criterion is particularly important in the reduction of premature fouling and failure of heat exchangers. Rather than relying on ad hoc geometry changes with the goal of improving the performance of radiators, the coupling of CFD flow simulations with numerical shape optimization methods could assist in the design and testing of automotive heating and cooling components.

A fast and accurate journal bearing elastohydrodynamic analysis is presented based on a finite difference formulation. The governing equations for the oil film pressure, stiffness and damping are solved using a finite difference approach. The oil film domain is discretized using a rectangular two-dimensional finite difference mesh. In this new formulation, it is not necessary to generate a global fluidity matrix similar to a finite element based solution. The finite difference equations are solved using a successive over relaxation (SOR) algorithm. The concept of “Influence Zone,” for computing the dynamic characteristics is introduced. The SOR algorithm and the “Influence Zone” concept significantly improve the computational efficiency without loss of accuracy. The new algorithms are validated with numerical results from the literature and their numerical efficiency is demonstrated.

A comprehensive formulation is presented for the dynamics of a rotating flexible crankshaft coupled with the dynamics of an engine block through a finite difference elastohydrodynamic main bearing lubrication algorithm. The coupling is based on detailed equilibrium conditions at the bearings. The component mode synthesis is employed for modeling the crankshaft and block dynamic behavior. A specialized algorithm for coupling the rigid and flexible body dynamics of the crankshaft within the framework of the component mode synthesis has been developed. A finite difference lubrication algorithm is used for computing the oil film elastohydrodynamic characteristics. A computationally accurate and efficient mapping algorithm has been developed for transferring information between a high - density computational grid for the elastohydrodynamic bearing solver and a low - density structural grid utilized in computing the crankshaft and block structural dynamic response.

This paper presents the development of surrogate models (metamodels) for evaluating the bearing performance in an internal combustion engine without performing time consuming analyses. The metamodels are developed based on results from actual simulation solvers computed at a limited number of sample points, which sample the design space. A finite difference bearing solver is employed in this paper for generating information necessary to construct the metamodels. An optimal symmetric Latin hypercube algorithm is utilized for identifying the sampling points based on the number and the range of the variables that are considered to vary in the design space. The development of the metamodels is validated by comparing results from the metamodels with results from the actual bearing performance solver over a large number of evaluation points. Once the metamodels are established they are employed for performing probabilistic analyses.

The thermal efficiency of spark-ignition engines can be enhanced by increasing the rate of exhaust gas recirculation (EGR) such that the low temperature combustion regime could be achieved. However, there is an upper limit on the amount of EGR rate, beyond which flame speed becomes slow and unstable, and local quenching starts to hurt the combustion stability, efficiency, and emission. To resolve this issue, the concept of dedicated EGR has been proposed previously to be an effective way to enhance flame propagation under lean burn condition with even higher levels of EGR with reformate hydrogen and carbon monoxide. In this study, the effects of thermochemical fuel reforming on the reformate composition under rich conditions (1.0 < ϕ < 2.0) have been studied using detailed chemistry for iso-octane, as the representative component for gasoline.

Scuffing is one of the major problems that influence the life cycle and reliability of several auto components, including engine cylinder kits, flywheels, camshafts, crankshafts, and gears. Ferrous casting materials, such as gray cast iron, ductile cast iron and austempered ductile cast iron (ADI) are widely applied in these components due to their self-lubricating characteristics. The purpose of this research is to determine the scuffing behavior of these three types of cast iron materials and compare them with 1050 steel. Rotational ball-on-disc tests were conducted with white mineral oil as the lubricant under variable sliding speeds and loads. The results indicate that the scuffing initiation is due to either crack propagation or plastic deformation. It is found that ADI exhibits the highest scuffing resistance among these materials.

Developments in modern spark ignition (SI) engines such as intake boosting, direct-injection, and engine downsizing techniques have demonstrated improved performance and thermal efficiency, however, these strategies induce significant deviation in end-gas pressure/temperature histories from those of the traditional Research and Motor Octane Number (RON and MON) standards. Attempting to extrapolate the anti-knock performance of fuels tested under the traditional RON/MON conditions to boosted operation has yielded mixed results in both SI and advanced compression ignition (ACI) engines. This consideration motivates the present work with seeks to establish a pathway towards the development of the test conditions of a boosted octane number, which would better correlate to fuel performance at high intake pressure conditions.

The availability of computational resources has enabled an increased utilization of Design of Experiments (DoE) and metamodeling (response surface generation) for large-scale optimization problems. Despite algorithmic advances however, the analysis of systems such as water jackets of an automotive engine, can be computationally demanding in part due to the required accuracy of metamodels. Because the metamodels may have many inputs, their accuracy depends on the number of training points and how well they cover the entire design (input) space. For this reason, the space-filling properties of the DoE are very important. This paper utilizes a new group-based DoE algorithm with space-filling groups of points to construct a metamodel. Points are added sequentially so that the space-filling properties of the entire group of points is preserved. The addition of points is continuous until a specified metamodel accuracy is met.

Proper valve angles and concentric valve seats are critical to performance of an engine. If the valve seat were not right, the valve is not going to seat properly resulting in reduced power output. Although the performance of CNC machines is accurate, unavoidable human errors at the part loading position have serious repercussions on engine performance. A solution algorithm presented in this paper employs the principles of inverse kinematics wherein a faulty compound-hole angle axis in space caused by the translational and rotational errors at the part loading position is identified with an imaginary true axis in space by enforcing identity through a modified machine axes.

This paper presents an experimental study of the cycle-averaged, local surface heat transfer, from the exhaust gases to a straight pipe extension of the exhaust port of a four-cylinder spark-ignition (SI) engine, over a wide range of engine operating conditions, from 1000 rpm, light load, through 4000 rpm, full load. The local steady-state heat flux was well correlated by a Nusselt-Reynolds number relationship that included entrance effects. These effects were found to be the major contributor to the local heat transfer augmentation. The Convective Augmentation Factor (CAF), which is defined as the ratio of the measured heat flux to the corresponding heat flux for fully-developed turbulent pipe flow, was found to decrease with increasing Reynolds number and increasing axial distance from the entrance of the test section.

Improvements in the pressure drop across and flow homogeneity in the tubes of automotive radiators are needed to reduce the power demands on the vehicle water pump and increase the lifetime of the radiator. The goal of this ongoing work is to develop a set of virtual tools coupling CFD flow simulations with numerical shape optimization methods to assist in the design and testing process of automotive heating and cooling components. In SAE paper 2002-01-0952, “Towards Shape Optimization of Radiator Cooling Tanks,” the authors developed and evaluated optimization criteria for pressure drop and mass flow rate distribution in a water-to-air automotive heat exchanger. In this follow-up paper, results based on the implementation of these optimization criteria are presented. More specifically, results concerning the placement of radiator inlets and outlets are addressed.

The effects of lubricating oil on friction and wear were investigated using light-duty 2.2L compression ignition direct injection (CIDI) engine components for bench testing. A matrix of test oils varying in viscosity, friction modifier level and chemistry, and base stock chemistry (mineral and synthetic) was investigated. Among all engine oils used for bench tests, the engine oil containing MoDTC friction modifier showed the lowest friction compared with the engine oils with organic friction modifier or the other engine oils without any friction modifier. Mineral-based engine oils of the same viscosity grade and oil formulation had slightly lower friction than synthetic-based engine oils.

A vehicle drivetrain is designed to meet specific vehicle performance criteria which usually involve trade-offs among conflicting performance measures. This paper describes a methodology to optimize the drivetrain design including the axle ratio, transmission shift points and transmission shift ratios considering uncertainty. A complete vehicle dynamic model is developed using the bond graph method. The model includes the vehicle, engine, transmission, torque converter, driveline, and transmission controller. An equivalent MATLAB Simulink model is also developed in order to carry out the nonlinear dynamic analysis efficiently. A deterministic optimization is first performed to determine the optimum design in terms of fuel economy, without considering variations or uncertainties. Subsequently, a Reliability-Based Design Optimization is carried out to find the optimum design in the presence of uncertainty.

The Formula SAE® rules require the use of a 20mm intake restrictor. The presence of the restrictor necessitates the design or retuning of fuel and spark strategies that, in turn require the use of a programmable engine control unit (ECU). This paper describes a process used to establish the fuel and spark strategies for a standard production motorcycle engine operating with a restricted air intake. Honda 600cc engines were controlled by three different ECUs: a Haltech, DTA and an “in-house” ECU. Simple calculations of injection duration are suggested to provide a baseline fuel map from which the engine could be started, and then fuel maps are tuned by experiment. Similar baseline numbers for ignition timing are given.

Mobility in the automotive and transportation sectors has been experiencing a period of unprecedented evolution. A growing need for efficient, clean and safe mobility has increased momentum toward sustainable technologies in these sectors. Toward this end, battery electric vehicles have drawn keen interest and their market share is expected to grow significantly in the coming years, especially in light-duty applications such as passenger cars. Although the battery electric vehicles feature high performance and zero tailpipe emission characteristics, economic and technical issues such as battery cost, driving range, recharging time and infrastructure remain main hurdles that need to be fully addressed. In particular, the low power density of the battery limits its broad adoption in heavy-duty applications such as class 8 semi-trailer trucks due to the required size and weight of the battery and electric motor.

Real-time measurement or estimation of crank-angle-resolved engine cylinder pressure may become commonplace in the next generation of engine controllers to optimize spark, valve timing, or compression ratio. Toward the development of a real-time cylinder pressure estimator, this work presents a crank-angle-resolved engine cylinder pressure estimation model that could accept inputs such as speed, manifold pressure and throttle position, and deliver crank-angle resolved cylinder pressure in real-time, at engine speeds covering the useful operating range of most engines. The model was validated by comparing simulated cylinder pressure with thirteen sets of cylinder pressure data, from two different commercial engines from two different OEMs. Estimated pressures were compared against the actual measured pressure traces. The average relative error is about 3% while the maximum relative error is 5%. Both can be improved with further tuning.

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.