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LiDAR sensors are common in automated driving due to their high accuracy. However, LiDAR processing algorithm development suffers from lack of diverse training data, partly due to sensors’ high cost and rapid development cycles. Public datasets (e.g. KITTI) offer poor coverage of edge cases, whereas these samples are essential for safer self-driving. We address the unmet need for abundant, high-quality LiDAR data with the development of a synthetic LiDAR point cloud generation tool and validate this tool’s performance using the KITTI-trained PIXOR object detection model. The tool uses a single camera raycasting process and filtering techniques to generate segmented and annotated class specific datasets.

Mixture formation and combustion dynamics are the primary contributors to the performance and emission characteristics of direct-injected spark ignition (SI) engines. This requires assessing the benefits and tradeoffs of the design and control factors that influence mixing and the subsequent combustion event. In this study, Taguchi's L18 orthogonal array design of experiment (DoE) methodology has been applied to assess contributions and tradeoffs of varied compression ratio, piston bowl design, intake port tumble design, injector spray pattern, injection timing, injection pressure, exhaust gas recirculation (EGR) rate, and intake valve closing timing in a single-cylinder boosted gasoline direct injection (GDI) SI engine. This multiparameter study has been carried out across three speed-load conditions representative of typical automotive application operating ranges.

Understanding cylinder-kit tribology is pivotal to durability, emission management, reduced oil consumption, and efficiency of the internal combustion engine. This work addresses the understanding of the fundamental aspects of oil transport and combustion gas flow in the cylinder kit, using simulation tools and high-performance computing. A dynamic three-dimensional multi-phase, multi-component modeling methodology is demonstrated to study cylinder-kit assembly tribology during the four-stroke cycle of a piston engine. The percentage of oil and gas transported through different regions of the piston ring pack is predicted, and the mechanisms behind this transport are analyzed. The velocity field shows substantial circumferential flow in the piston ring pack, leading to blowback into the combustion chamber during the expansion stroke.

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.

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.

The automotive industry is subject to stringent regulations in emissions and growing customer demands for better fuel consumption and vehicle performance. Engine calibration, a process that optimizes engine performance by tuning engine controls (actuators), becomes challenging nowadays due to significant increase of complexity of modern engines. The traditional sweep-based engine calibration method is no longer sustainable. To tackle the challenge, this work considers two powerful global optimization methods: genetic algorithm (GA) and Bayesian optimization for steady-state engine calibration for single speed-load point. GA is a branch of meta-heuristic methods that has shown a great potential on solving difficult problems in automotive engineering. Bayesian optimization is an efficient global optimization method that solves problems with computationally expensive testing such as hyperparameter tuning in deep neural network (DNN), engine testing, etc.

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.

Three dimensional numerical simulation of the transient turbulent jet and ignition processes of a premixed methane-air mixture of a turbulent jet ignition (TJI) system is performed using Converge computational software. The prechamber initiated combustion enhancement technique that is utilized in a TJI system enables low temperature combustion by increasing the flame propagation rate and therefore decreasing the burn duration. Two important components of the TJI system are the prechamber where the spark plug and injectors are located and the nozzle which connects the prechamber to the main chamber. In order to model the turbulent jet of the TJI system, RANS k-ε and LES turbulent models and the SAGE chemistry solver with a reduced mechanism for methane are used.

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.

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.

A three-dimensional piston ring model has been developed using finite element method with eight-node hexahedral elements. The model predicts the piston ring conformability with the cylinder wall as well as the separation gap between the interfaces if existing in the radial direction. In addition to the radial interaction between the ring front face and the cylinder wall, the model also predicts the contact between the ring and groove sides in the axial direction. This means, the ring axial lift, ring twist, contact forces with the groove sides along the circumferential direction are all calculated simultaneously with the radial conformability prediction. The ring/groove side contact can be found for scraper ring at static condition, which is widely used as the second compression ring in a ring pack. Thermal load is believed having significant influence on the ring pack performance.

Fully three-dimensional computational fluid dynamic simulations with detailed chemistry of a single-orifice turbulent jet ignition device installed in a rapid compression machine are presented. The simulations were performed using the computational fluid dynamics software CONVERGE and its RANS turbulence models. Simulations of propane fueled combustion are compared to data collected in the optically accessible rapid compression machine that the model's geometry is based on to establish the validity and limitations of the simulations and to compare the behavior of the different air-fuel ratios that are used in the simulations.

Camless Variable Valve Actuation (VVA) technologies have been known for improving fuel economy, reducing emissions, and enhancing engine performance. VVA can be divided into electro-magnetic, electro-hydraulic, and electro-pneumatic actuation. A family of camless VVA designs (called LGD-VVA or Gongda-VVA) has been presented in an earlier SAE publication (SAE 2007-01-1295) that consists of a two-spring actuation, a bypass passage, and an electrohydraulic latch-release mechanism. The two-spring pendulum system is used to provide efficient conversion between the moving mass kinetic energy and the spring potential energy for reduced energy consumption and to be more robust to the operational temperature than the conventional electrohydraulic actuation; and the electrohydraulic mechanism is intended for latch-release function, energy compensation and seating velocity control.

A multicomponent droplet evaporation model which discretizes the one-dimensional mass and temperature profiles inside a droplet with a finite volume method has been developed and implemented into a large-eddy simulation (LES) model for spray simulations. The LES and multicomponent models were used along with the KH-RT secondary droplet breakup model to simulate realistic fuel sprays in a closed vessel. The effect of various spray and ambient gas parameters on the liquid penetration length of different single component and multicomponent fuels was investigated. The numerical results indicate that the spray penetration length decreases non-linearly with increasing gas temperature or pressure and is less sensitive to changes in ambient gas conditions at higher temperatures or pressures. The spray models and LES were found to predict the experimental results for n-hexadecane and two multicomponent surrogate diesel fuels reasonably well.

Large Eddy Simulations of atomization and evaporation of liquid fuel sprays in diesel engine conditions are performed with stochastic breakup and non-equilibrium droplet heat and mass transfer models. The size and number density of the droplets generated by the breakup model are assumed to be governed by a Fokker-Planck equation, describing the evolution of the PDF of droplet radii. The fragmentation intensity spectrum is considered to be Gaussian and the scale of Lagrangian relative velocity fluctuations is included in the breakup frequency calculations. The aerodynamic interactions of droplets in the dense part of the spray are modeled by correcting the relative velocity of droplets in the wake of other droplets. The stochastic breakup model is employed together with the wake interaction model for simulations of non-evaporating and evaporating sprays in various gas temperature and pressure conditions.

EGR coolers are used in combustion engines to reduce NOx emissions. However, heat transfer in these coolers also results in thermophoresis-temperature-gradient driven motion of suspended particles towards cooler regions-which leads to significant soot deposition. A simple one-dimensional model is proposed to predict the deposition velocity and soot layer thickness that compares reasonably well with experimental data. The behavior of soot deposits on cooled surfaces is complex, with the thickness of the soot layer stabilizes after around 100 hours, reaching a uniform, thickness over the entire heat-exchanger surface. An analysis of this trend and a tentative mechanism to explain this type of behavior is given, based on experimental observations.

Air-to-fuel (A/F) ratio is the mass ratio of the air-to-fuel mixture trapped inside a cylinder before combustion begins, and it affects engine emissions, fuel economy, and other performances. Using an A/F ratio and dual-fuel ratio control oriented engine model, a multi-input-multi-output (MIMO) sliding mode control scheme is used to simultaneously control the mass flow rate of both port fuel injection (PFI) and direct injection (DI) systems. The control target is to regulate the A/F ratio at a desired level (e.g., at stoichiometric) and fuel ratio (ratio of PFI fueling vs. total fueling) to any desired level between zero and one. A MIMO sliding mode controller was designed with guaranteed stability to drive the system A/F and fuel ratios to the desired target under various air flow disturbances.

This paper focuses on the numerical investigation of the mixing and combustion of ethanol and gasoline in a single-cylinder 3-valve direct-injection spark-ignition engine. The numerical simulations are conducted with the KIVA code with global reaction models. However, an ignition delay model mitigates some of the deficiencies of the global one-step reaction model and is implemented via a two-dimensional look-up table, which was created using available detailed kinetics models. Simulations demonstrate the problems faced by ethanol operated engines and indicate that some of the strategies used for emission control and downsizing of gasoline engines can be employed for enhancing the combustion efficiency of ethanol operated engines.

To increase robustness of the crush mode and to decrease repair costs after a crash, it is desirable for front and rear rails in an automotive vehicle to crush progressively. Here, progressive crush refers to a mode of axial crush that initiates near the tip of the rail and then progresses rearward in a controlled fashion. In this study, a new strategy is investigated to achieve progressively crushing designs during an automated design optimization study. This strategy employs the definition of crush zones along the length of a rail, and a design optimization problem statement that encourages maximum energy absorption in any particular crush zone to occur prior to any energy absorption in rearward zones. It is demonstrated that high performing designs with progressive crush can be obtained using the proposed approach.

Human intraspecific variation is a complex problem, but may be better understood by using computational models in tandem with knowledge about the genetic bases of phenotypic traits. These results can be used in a multitude of settings. To move closer to this goal, biologically-realistic mappings between genotype and phenotype are constructed using genetic algorithm and neural network-like models. These models allow for gene-gene and gene-environment interactions to be characterized in the resulting phenotype. Two types of model are introduced: a simple, two-layer model, and a more complex model. The final section will focus on trends of growth and development in relation to relationship to modeling anthropometric traits and other morphological phenomena.