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In an Adaptive Cycle Engine (ACE), thermodynamics favors combustion starting while the compressed, premixed air and fuel are still flowing into the cylinder through the transfer valve. Since the flow velocity is typically high and is predicted to reach sonic conditions by the time the transfer valve closes, the flame might be subjected to extensive stretch, thus leading to aerodynamic quenching. It is also unclear whether a single spark, or even a succession of sparks, will be sufficient to achieve complete combustion. Given that the first ACE prototype is still being built, this issue is addressed by numerical simulation using the G-equation model, which accounts for the effect of flame stretching, over a 3D domain representing a flat-piston ACE cylinder, both with inward- and outward-opening valves. A k-epsilon turbulence model was used for the highly turbulent flow field.

Autonomous vehicle operation is dependent upon accurate position estimation and thus a major concern of implementing the autonomous navigation is obtaining robust and accurate data from sensors. This is especially true, in case of Inertial Measurement Unit (IMU) sensor data. The IMU consists of a 3-axis gyro, 3-axis accelerometer, and 3-axis magnetometer. The IMU provides vehicle orientation in 3D space in terms of yaw, roll and pitch. Out of which, yaw is a major parameter to control the ground vehicle’s lateral position during navigation. The accelerometer is responsible for attitude (roll-pitch) estimates and magnetometer is responsible for yaw estimates. However, the magnetometer is prone to environmental magnetic disturbances which induce errors in the measurement.

The favorable physical properties of hydrogen (H2) make it an excellent alternative fuel for internal combustion (IC) engines and hence it is widely regarded as the energy carrier of the future. Hydrogen direct injection provides multiple degrees of freedom for engine optimization and influencing the in-cylinder combustion processes. This paper compares the results in the mixture formation and combustion behavior of a hydrogen direct-injected single-cylinder research engine using two different injector locations as well as various injector nozzle designs. For this study the research engine was equipped with a specially designed cylinder head that allows accommodating a hydrogen injector in a side location between the intake valves as well as in the center location adjacent to the spark plug.

The presented manuscript discusses the implementation of the pulsed-thermography technique for the non-intrusive evaluation of automotive parts. The study discusses the fundamentals of static and dynamic thermography through examples and case studies. Furthermore, the proposed pulse thermography system is analyzed in terms of hardware calibration i.e. pulse duration and intensity and the detector effect on the time and the spatial resolutions. Current thermography processing codes and techniques are also described and critiqued, with new processing subroutines proposed; one based on self-referencing thersholding. Additionally, new trends in infrared and visible sensors fusion are presented.

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.

The NOx reduction performance under lean conditions over γ-alumina was evaluated using a micro-reactor system and a non-thermal plasma-equipped bench test system. Various alumina samples were obtained from alumina manufacturers to assess commercial alumina materials. In addition, γ-alumina samples were synthesized at Caterpillar with a sol-gel technique in order to control alumina properties. The deNOx performances of the alumina samples were compared. The alumina samples were characterized with analytical techniques such as inductively coupled plasma (ICP) emission spectroscopy, temperature programmed desorption (TPD) and surface area measurements (BET) to understand physical and chemical properties. The information derived from these techniques was correlated with the NOx reduction performance to identify key parameters of γ-alumina for optimizing materials for lean-NOx and plasma assisted catalysis.

This paper summarizes the successful application of Computational Fluid Dynamics (CFD) analysis to predict and control the cavitation erosion in a hydraulic control valve. The accurate control of different vehicle operations demands very fine spool modulations in a hydraulic valve. The precise spool modulations create very high flow rates and high-pressure drops in the valve. The low local fluid pressure regions create cavitation inside the valve. Due to the explosion of bubbles there is a high erosion damage to the valve body as well as the spool surface. The CFD analysis has been used to predict the location of cavitation origination and also used to control the cavitation by redistributing the flow inside the valve.

An accurate estimation of cycle-by-cycle in-cylinder mass and the composition of the cylinder charge is required for spark-ignition engine transient control strategies to obtain required torque, Air-Fuel-Ratio (AFR) and meet engine pollution regulations. Mass Air Flow (MAF) and Manifold Absolute Pressure (MAP) sensors have been utilized in different control strategies to achieve these targets; however, these sensors have response delay in transients. As an alternative to air flow metering, in-cylinder pressure sensors can be utilized to directly measure cylinder pressure, based on which, the amount of air charge can be estimated without the requirement to model the dynamics of the manifold.

Measurement of internal combustion engine air flow is challenging due to the required modification of the intake system and subsequent change in the air flow pattern. In this paper, various surge tank volumes were investigated to improve the accuracy of measuring air flow rate into a 674-cm3, four-stroke, liquid-cooled, internal combustion engine. According to the experimental results, when the venturi meter is used to measure the intake air flow rate, an air surge tank is required to be installed downstream of the venturi to smoothen the air flow. Moreover, test results revealed that increasing air surge tank volume beyond a limit could have a negative effect on the engine performance parameters especially in carbureted engines where controlling AFR is difficult. Although the air flow rate into the engine changed with increasing tank volume, the air-fuel ratio was leaner for smaller tank volumes.

In this study, the impact of the intake valve timing on knock propensity is investigated on a dual-fuel engine which leverages a low octane fuel and a high octane fuel to adjust the fuel mixture’s research octane rating (RON) based on operating point. Variations in the intake valve timing have a direct impact on residual gas concentrations due to valve overlap, and also affect the compression pressure and temperature by altering the effective compression ratio (eCR). In this study, it is shown that the fuel RON requirement for a non-knocking condition at a fixed operating point can vary significantly solely due to variations of the intake valve timing. At 2000 rpm and 6 bar IMEP, the fuel RON requirement ranges from 80 to 90 as a function of the intake valve timing, and the valve timing can change the RON requirement from 98 to 104 at 2000 rpm and 14 bar IMEP.

A new press/die system for restraining force control has been developed in order to facilitate an increased level of process control in sheet metal forming. The press features a built-in system for controlling drawbead penetration in real time. The die has local force transducers built into the draw radius of the lower tooling. These sensors are designed to give process information useful for the drawbead control. This paper focuses on developing models of the drawbead actuators and the die shoulder sensors. The actuator model is useful for developing optimal control methods. The sensor characterization is necessary in order to develop a relationship between the raw sensor outputs and a definitive process characteristic such as drawbead restraining force (DBRF). Closed loop control of local specific punch force is demonstrated using the die shoulder sensor and a PID controller developed off-line with the actuator model.

A computational methodology has been developed for loss prediction in intake regions of internal combustion engines. The methodology consists of a hierarchy of four major tasks: (1) proper computational modeling of flow physics; (2) exact geometry and high quality and generation; (3) discretization schemes for low numerical viscosity; and (4) higher order turbulence modeling. Only when these four tasks are dealt with properly will a computational simulation yield consistently accurate results. This methodology, which is has been successfully tested and validated against benchmark quality data for a wide variety of complex 2-D and 3-D laminar and turbulent flow situations, is applied here to a loss prediction problem from industry. Total pressure losses in the intake region (inlet duct, manifold, plenum, ports, valves, and cylinder) of a Caterpillar diesel engine are predicted computationally and compared to experimental data.

The design of a pressure compensated hydraulic valve is optimized using CFD analysis. The valve is used in a hydraulic system to control implement movement. High flow rates through the valve resulted in unacceptably high pressure drops, leading to an effort to optimize the valve design. Redesign of the valve had to be achieved under the constraint of minimal manufacturing cost. The flow path of hydraulic oil through the valve, the spool design, and various components of the valve that caused the high pressure drops were targeted in this analysis. A commercially available CFD package was used for the 3D analysis. The hydraulic oil flow was assumed to be turbulent, isothermal and incompressible. The steady-state results were validated by comparison with experimental data.

This paper describes a multi-sensor data set, suitable for testing algorithms to detect and track pedestrians and cyclists, with an autonomous vehicle’s sensor suite. The data set can be used to evaluate the benefit of fused sensing algorithms, and provides ground truth trajectories of pedestrians, cyclists, and other vehicles for objective evaluation of track accuracy. One of the principal bottlenecks for sensing and perception algorithm development is the ability to evaluate tracking algorithms against ground truth data. By ground truth we mean independent knowledge of the position, size, speed, heading, and class of objects of interest in complex operational environments. Our goal was to execute a data collection campaign at an urban test track in which trajectories of moving objects of interest are measured with auxiliary instrumentation, in conjunction with several autonomous vehicles (AV) with a full sensor suite of radar, lidar, and cameras.

Development of Caterpillar Fuel Systems' MEUI-B injector has involved application of Computational Fluid Dynamics (CFD) in order to improve performance of the high pressure spill valve. Initial performance bench testing with concept stage experimental injectors indicated that the chamber pressure was decaying at an unacceptably slow rate, and the valve demonstrated erratic behavior at some operating conditions. The slow pressure decay and inconsistent spill valve motion were believed to be caused by flow forces generated during the low lift portion of the spill valve opening event. This theory was pursued by utilizing CFD to design two valves for testing in the next phase of the injector development cycle: A baseline geometry, similar to the original concept injector valve, and a new design incorporating localized seat geometry changes for inducing flow force assisted valve opening.

Using a previously validated and documented CFD methodology, this research simulated the flow field in the intake region (inlet duct, plenum, ports, valves, and cylinder) involving the four cylinders (#1, #3, #4, #6) of a straight-six IC engine. Each cylinder was studied with its intake valves set at high, medium and low valve lifts. All twelve viscous 3-D turbulent flow simulation models had high density, high quality computational grids and complete domains. Extremely fine grid density were applied for every simulation up to 1,000,000 finite volume cells. Results for all the cases presented here were declared “fully converged” and “grid independent”. The relative magnitude of total pressure losses in the entire intake region and loss mechanisms were documented here. It was found that the total pressure losses were caused by a number of flow mechanisms.

The Electronic Control Unit (ECU) of an Electric Power Steering (EPS) system is a core device to decide how much assistance an electric motor applies on a steering wheel. The EPS ECU plays an important role in EPS systems. The effectiveness of an ECU needs to be thoroughly tested before mass production. Hardware-in-the-loop simulation provides an efficient way for the development and testing of embedded controllers. This paper focuses on the development of a HiL system for testing EPS controllers. The hardware of the HiL system employs a dSPACE HiL simulator. The EPS plant model is an integrated model consisting of a Vehicle Dynamics model of the dSPACE Automotive Simulation Model (ASM) and the Nexteer Steering model. The paper presents the design of an EPS HiL system, the simulation of sensors and actuators, the functions of the ASM Vehicle Dynamics model, and the integration method of the ASM Vehicle Dynamics model with a Steering model.

To reduce the number and severity of accidents, automakers have invested in active safety systems to detect and track neighboring vehicles to prevent accidents. These systems often employ RADAR and LIDAR, which are not degraded by low lighting conditions. In this research effort, reflections from deer were measured using two sensors often employed in automotive active safety systems. Based on a total estimate of one million deer-vehicle collisions per year in the United States, the estimated cost is calculated to be $8,388,000,000 [1]. The majority of crashes occurs at dawn and dusk in the Fall and Spring [2]. The data includes tens of thousands of RADAR and LIDAR measurements of white-tail deer. The RADAR operates from 76.2 to 76.8 GHz. The LIDAR is a time-of-flight device operating at 905 nm. The measurements capture the deer in many aspects: standing alone, feeding, walking, running, does with fawns, deer grooming each other and gathered in large groups.

The advancement in embedded systems and positional accuracy with base station GPS modules created opportunity to develop high performance autonomous ground vehicles. However, the development of vehicle model and making accurate state estimations play vital role in reducing the cross track error. The present research focus on developing Linear Quadratic Gaussian (LQG) with Kalman estimator for autonomous ground vehicle to track various routes, that are made with the series of waypoints. The model developed in the LQG controller is a kinematic bicycle model, which mimics 1/5th scale truck. Further, the cubic spline fit has been used to connect the waypoints and generate the continuous desired/target path. The testing and implementation has been done at APS labs, MTU on the mentioned vehicle to study the performance of controller. Python has been used for simulations, controller coding and interfacing the sensors with controller.

The proliferation of small silicon micro-chips has led to a large assortment of low-cost transducers for data acquisition. Production vehicles on average exploit more than 60 on board sensors, and that number is projected to increase beyond 200 per vehicle by 2020. Such a large increase in sensors is leading the fourth industrial revolution of connectivity and autonomy. One major downfall to installing many sensors is compromises in their accuracy and processing power due to cost limitations for high volume production. The same common errors in data acquisition such as sampling, quantization, and multiplexing on the CAN bus must be accounted for when utilizing an entire array of vehicle sensors. A huge advantage of onboard sensors is the ability to calculate vehicle parameters during a daily drive cycle to update ECU calibration factors in real time. One such parameter is driveline damping, which changes with gear state and drive mode. A damping value is desired for every gear state.