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Turbulent Jet Ignition is an advanced pre-chamber initiated combustion system for an otherwise standard spark ignition engine found in current on-road vehicles. This next-generation pre-chamber design overcomes previous packaging obstacles by simply replacing the spark plug in a modern four-valve, pent roof spark ignition engine. Turbulent Jet Ignition enables very fast burn rates due to the ignition system producing multiple, distributed ignition sites, which consume the main charge rapidly and with minimal combustion variability. The fast burn rates allow for increased levels of dilution (lean burn and/or EGR) when compared to conventional spark ignition combustion, with dilution levels being comparable to other low temperature combustion technologies (homogeneous charge compression ignition - HCCI) without the complex control drawbacks.

In the development of an FAW SUV, one of the goals is to achieve a state of the art drag level. In order to achieve such an aggressive target, feedback from aerodynamics has to be included in the early stage of the design decision process. The aerodynamic performance evaluation and improvement is mostly based on CFD simulation in combination with some wind tunnel testing for verification of the simulation results. As a first step in this process, a fully detailed simulation model is built. The styling surface is combined with engine room and underbody detailed geometry from a similar size existing vehicle. From a detailed analysis of the flow field potential areas for improvement are identified and five design parameters for modifying overall shape features of the upper body are derived. In a second step, a response surface method involving design of experiments and adaptive sampling techniques are applied for characterizing the effects of the design changes.

This paper presents a computational investigation of the validity of eddy current testing (ECT) for defects embedded in steel using parametrically designed defects. Of particular focus is the depths at which defects can be detected through ECT. Building on this we characterize interior defects by parametrically describing them and then examining the response fields through measurement. Thereby we seek to establish the depth and direction of detectable cracks. As a second step, we match measurements from eddy current excitations to computed fields through finite element optimization. This develops further our previously presented methods of defect characterization. Here rough contours of synthesized shapes are avoided by a novel scheme of averaging neighbor heights rather than using complex Bézier curves, constraints and such like. This avoids the jagged shapes corresponding to mathematically correct but unrealistic synthesized shapes in design and nondestructive evaluation.

A twist beam rear suspension system is modeled, analyzed and optimized in this paper. An ADAMS model is established based on the REC (Rigid-Elastic Coupling) Theory, which is verified by FEM (Finite Element Method) approach, the effects of the geometric parameters on the twist beam suspension performance are investigated. In order to increase the calculation efficiency and improve the simulation accuracy, a neural network model and NSGA II (Non-dominated Sorting Genetic Algorithm II) are adopted to conduct a multi-objective optimization on a twist beam rear suspension system.

A turbocharger is currently widely used to boost performance of an internal combustion engine. Generally, a turbocharger consists of a compressor which typically is driven by an exhaust turbine. The compressor will influence how the low frequency engine pulsation propagates in the intake system. The compressor will also produce broad-band flow induced sound due to the turbulence flow and high frequency narrowband tonal sound which is associated with rotating blade pressures. In this paper, a practical simulation procedure based on a computational fluid dynamics (CFD) approach is developed to predict the flow induced sound of a turbocharger compressor. In the CFD model of turbocharger compressor, the unsteady, moving wheel, detached eddy simulation (DES) approach are utilized. In this manner, both the broad-band and narrow-band flow induced sound are directly resolved in the CFD computation.

Turbulent jet ignition is a pre-chamber ignition enhancement method that produces a distributed ignition source through the use of a chemically active turbulent jet which can replace the spark plug in a conventional spark ignition engine. In this paper combustion visualization and characterization was performed for the combustion of a premixed propane/air mixture initiated by a pre-chamber turbulent jet ignition system with no auxiliary fuel injection, in a rapid compression machine. Three different single orifice nozzles with orifice diameters of 1.5 mm, 2 mm, and 3 mm were tested for the turbulent jet igniter pre-chamber over a range of air to fuel ratios. The performance of the turbulent jet ignition system based on nozzle orifice diameter was characterized by considering both the 0-10 % and the 10-90 % burn durations of the pressure rise due to combustion.

The highly transient operational nature of passenger car engines makes cylinder pressure based feedback control of combustion phasing difficult. The problem is further complicated by cycle-to-cycle combustion variation. A method for fast and accurate differentiation of normal combustion variations and true changes in combustion phasing is addressed in this research. The proposed method combines the results of a feed forward combustion phasing prediction model and “noisy” measurements from cylinder pressure using an iterative estimation technique. A modified version of an Extended Kalman Filter (EKF) is applied to calculate optimal estimation gain according to the stochastic properties of the combustion phasing measurement at the corresponding engine operating condition. Methods to improve steady state CA50 estimation performance and adaptation to errors are further discussed in this research.

Autoignition delay times of two full blend gasoline fuels (high and low RON) were explored in a rapid compression machine. CO2 dilution by mass was introduced at 0%, 15%, and 30% levels with the O2:N2 mole ratio fixed at 1:3.76. This dilution strategy is used to represent exhaust gas recirculation (EGR) substitution in spark ignition (SI) engines by using CO2 as a surrogate for major EGR constituents(N2, CO2, H2O). Experiments were conducted over the temperature range of 650K-900K and at 10 bar and 20 bar compressed pressure conditions for equivalence ratios of (Φ =) 0.6-1.3. The full blend fuels were admitted directly into the combustion chamber for mixture preparation using the direct test chamber (DTC) approach. CO2 addition retarded the autoignition times for the fuels studied here. The retarding effect of the CO2 dilution was more pronounced in the NTC region when compared to the lower and higher temperature range.

Continuous efforts to improve thermal efficiency and reduce exhaust emissions of internal combustion engines have resulted in development of various solutions towards improved lean burn ignition systems in spark ignition engines. The Dual Mode, Turbulent Jet Ignition (DM-TJI) system is one of the leading technologies in that regard which offers higher thermal efficiency and reduced NOx emissions due to its ability to operate with very lean or highly dilute mixtures. Compared to other pre-chamber ignition technologies, the DM-TJI system has the distinct capability to work with a very high level of EGR dilution (up to ~40%). Thus, this system enables the use of a three-way catalyst (TWC). Auxiliary air supply for pre-chamber purge allows this system to work with such high EGR dilution rate. This work presents the results of experimental investigation carried out with a Dual Mode, Turbulent Jet Ignition (DM-TJI) optical engine equipped with a cooled EGR system.

Electronic Stop-Start (ESS) system automatically stops and restarts the engine to save energy, improve fuel economy and reduce emissions when the vehicle is stationary during traffic lights, traffic jams etc. The stop and start events cause unwanted vibrations at the seat track which induce discomfort to the driver and passengers in the vehicle. These events are very short duration events, usually taking less than a second. Time domain analysis can help in simulating this event but it is difficult to see modal interactions and root cause issues. Modal transient analysis also poses a limitation on defining frequency dependent stiffness and damping for multiple mounts. This leads to inaccuracy in capturing mount behavior at different frequencies. Most efficient way to simulate this event would be by frequency response analysis using modal superposition method.

As engines are equipped with an increased number of control actuators to meet fuel economy targets, they become more difficult to control and calibrate. The additional complexity created by a larger number of control actuators motivates the use of physics-based control strategies to reduce calibration time and complexity. Combustion phasing, as one of the most important engine combustion metrics, has a significant influence on engine efficiency, emissions, vibration and durability. To realize physics-based engine combustion phasing control, an accurate prediction model is required. This research introduces physics-based control-oriented laminar flame speed and turbulence intensity models that can be used in a quasi-dimensional turbulent entrainment combustion model. The influence of laminar flame speed and turbulence intensity on predicted mass fraction burned (MFB) profile during combustion is analyzed.

As CAFE requirements increase, automotive OEMs are pursuing innovative methods to lightweight their Body In Whites (BIWs). Within FCA US, this lightweighting research and development activity often occurs through Decoupled Innovation projects. A Decoupled Innovation team comprised of engineers from the BIW Structures Group, in collaboration with Tier 1 supplier Magna Exteriors, sought to re-design a loadbearing component on the BIW that would offer significant weight savings when the current steel component was replaced with a carbon fiber composite. This paper describes the design, development, physical validation and partnership that resulted in a composite Rear Package Shelf Assembly solution for a high-volume production vehicle. As the CAFE requirements loom closer and closer, these innovation-driven engineering activities are imperative to the successful lightweighting of FCA US vehicles.

One of the remaining challenges in the simulation of the aerodynamics of ground vehicles is the modeling of the airflows around the spinning tires and wheels of the vehicle. As in most advances in the development of simulation capabilities, it is the lack of appropriately detailed and accurate experimental data with which to correlate that holds back the advance of the technology. The flow around the wheels and tires and their interfaces with the vehicle body and the ground is a critical area for the development of automobiles and trucks, not just for aerodynamic forces and moments, and their result on fuel economy and vehicle handling and performance, but also for the airflows and pressures that affect brake cooling, engine cooling airflows, water spray management etc.

This research proposes a control system for Spark Ignition (SI) engines with external Exhaust Gas Recirculation (EGR) based on model predictive control and a disturbance observer. The proposed Economic Nonlinear Model Predictive Controller (E-NMPC) tries to minimize fuel consumption for a number of engine cycles into the future given an Indicated Mean Effective Pressure (IMEP) tracking reference and abnormal combustion constraints like knock and combustion variability. A nonlinear optimization problem is formulated and solved in real time using Sequential Quadratic Programming (SQP) to obtain the desired control actuator set-points. An Extended Kalman Filter (EKF) based observer is applied to estimate engine states, combining both air path and cylinder dynamics. The EKF engine state(s) observer is augmented with disturbance estimation to account for modeling errors and/or sensor/actuator offset.

Piston temperature plays a major role in determining details of fuel spray vaporization, fuel film deposition and the resulting combustion in direct-injection engines. Due to different heat transfer properties that occur in optical and all-metal engines, it becomes an inevitable requirement to verify the piston temperatures in both engine configurations before carrying out optical engine studies. A novel Spot Infrared-based Temperature (SIR-T) technique was developed to measure the piston window temperature in an optical engine. Chromium spots of 200 nm thickness were vacuum-arc deposited at different locations on a sapphire window. An infrared (IR) camera was used to record the intensity of radiation emitted by the deposited spots. From a set of calibration experiments, a relation was established between the IR camera measurements of these spots and the surface temperature measured by a thermocouple.

An engine exhaust manifold undergoes repeated thermal expansion and contraction due to temperature variation. Thermomechanical fatigue (TMF) arises due to the boundary constraints on thermal expansion so that mechanical strain is introduced. Therefore, TMF evaluation is very important in engine design. In this work, the mechanical properties important for TMF assessment and modeling of a silicon (Si)- and molybdenum (Mo)-containing ductile cast iron used for exhaust manifold have been evaluated. Tensile, creep, isothermal low cycle fatigue (LCF), and TMF tests have been conducted. Parameters for material modeling, such as the viscoplastic constitutive model and the Neu-Sehitoglu TMF damage model, have been calibrated, validated, and used to evaluate the TMF life of the exhaust manifold.

Two models are presented for the long life (107 cycles) axial fatigue strength of four ferrous powder metal (PM) material series: sintered and heat-treated iron-carbon steel, iron-copper and copper steel, iron-nickel and nickel steel, and pre-alloyed steel. The materials are defined at ranges of carbon content and densities using the broad data available in the Metal Powder Industries Federation (MPIF) Standard 35 for PM structural parts. The first model evaluates 107 cycles axial fatigue strength as a function of ultimate strength and the second model as a function of hardness. For all 118 studied materials, both models are found to have a good correlation between calculated and 107 cycles axial fatigue strength with a high Pearson correlation coefficient of 0.97. The article provides details on the model development and the reasoning for selecting the ultimate strength and hardness as the best predictors for 107 cycles axial fatigue strength.

Multiaxial loading on mechanical products is very common in the automotive industry, and how to design and analyze these products for durability becomes an important, urgent task for the engineering community. Due to the complex nature of the fatigue damage mechanism for a product under multiaxial state of stresses/strains which are dependent upon the modes of loading, materials, and life, modeling this behavior has always been a challenging task for fatigue scientists and engineers around the world. As a result, many multiaxial fatigue theories have been developed. Among all the theories, an existing equivalent stress theory is considered for use for the automotive components that are typically designed to prevent Case B cracks in the high cycle fatigue regime.

As engines are equipped with an increased number of control actuators to meet fuel economy targets they become more difficult to control and calibrate. The large number of control actuators encourages the investigation of physics-based control strategies to reduce calibration time and complexity. Of particular interest is spark timing control and calibration since it has a significant influence on engine efficiency, emissions, vibration and durability. Spark timing determination to achieve a desired combustion phasing is currently an empirical process that occurs during the calibration phase of engine development. This process utilizes a large number of stored surfaces and corrections to account for the wide range of operating environments and conditions that a given engine will experience. An obstacle to realizing feedforward physics-based combustion phasing control is the requirement for an accurate and fast combustion model.

Deep learning based approaches for object detection are heavily dependent on the nature of data used for training, especially for vehicles driving in cluttered urban environments. Consequently, the performance of Convolutional Neural Network (CNN) architectures designed and trained using data captured under clear weather and favorable conditions, could degrade rather significantly when tested under cloudy and rainy conditions. This naturally becomes a major safety issue for emerging autonomous vehicle platforms relying on CNN based object detection methods. Furthermore, despite a noticeable progress in the development of advanced visual deraining algorithms, they still have inherent limitations for improving the performance of state-of-the-art object detection. In this paper, we address this problem area by make the following contributions.