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In this work, a dynamically loaded hydrodynamic journal bearing test rig is developed and introduced. The rig is a novel design, using a hydraulic actuator with fast acting spool valves to apply load to a connecting rod. This force is transmitted through the connecting rod to the large end bearing which is mounted on a spinning shaft. The hydraulic actuator allows for fully variable control and can be used to apply either static load in compression or tension, or dynamic loading to simulate engine operation. A variable speed electric motor controls shaft speed and is synchronized to the hydraulic actuator to accurately simulate loading to represent all four engine strokes. A high precision torque meter enables direct measurements of friction torque, while shaft position is measured via a high precision encoder.

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.

This paper summarizes the Heavy-Duty In-Use Testing (HDUIT) measurement allowance program for Particulate Matter Portable Emissions Measurement Systems (PM-PEMS). The measurement allowance program was designed to determine the incremental error between PM measurements using the laboratory constant volume sampler (CVS) filter method and in-use testing with a PEMS. Two independent PM-PEMS that included the Sensors Portable Particulate Measuring Device (PPMD) and the Horiba Transient Particulate Matter (TRPM) were used in this program. An additional instrument that included the AVL Micro Soot Sensor (MSS) was used in conjunction with the Sensors PPMD to be considered a PM-PEMS. A series of steady state and transient tests were performed in a 40 CFR Part 1065 compliant engine dynamometer test cell using a 2007 on-highway heavy-duty diesel engine to quantify the accuracy and precision of the PEMS in comparison with the CVS filter-based method.

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.

Atmospheric corrosion of steels, aluminum alloys, and Al-clad aluminum alloys is a problem for many civil engineering structures, commercial and military vehicles, and aircraft. Paint is usually the primary means to prevent the corrosion of steel bridge components, automobiles, trucks, and aircraft. Under ideal conditions, the coating provides a continuous layer that is impervious to moisture. At present, maintenance cycles for commercial and military aircraft and ground vehicles, as well as engineered structures, is based on experience and appearance rather than a quantitative determination of coating integrity. To improve the maintenance process and reduce costs, sensors are often used to monitor corrosion. The present suite of sensors designed to detect corrosion and marketed to predict the lifetime of the engineered components, however, are not useful for determining the condition of the protective paint coatings.

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.

Exhaust emissions of non-methane hydrocarbon (NMHC) and methane were measured from a Tier 3 dual-fuel demonstration locomotive running diesel-natural gas blend. Measurements were performed with the typical flame ionization detector (FID) method in accordance with EPA CFR Title 40 Part 1065 and with an alternative Fourier-Transform Infrared (FTIR) Spectroscopy method. Measurements were performed with and without oxidation catalyst exhaust aftertreatment. FTIR may have potential for improved accuracy over the FID when NMHC is dominated by light hydrocarbons. In the dual fuel tests, the FTIR measurement was 1-4% higher than the FID measurement of. NMHC results between the two methods differed considerably, in some cases reporting concentrations as much as four times those of the FID. However, in comparing these data it is important to note that the FTIR method has several advantages over the FID method, so the differences do not necessarily represent error in the FTIR.

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.

The U.S. Army has long identified the need for rapid, reliable methods for analysis of fuels and lubricants on or near the battlefield. The analysis of fuels and lubricants under battlefield or near-battlefield conditions requires that the equipment be small, portable, rugged, quick, and easy to use. Over the past 15 to 20 years, several test kits and portable laboratories have been developed in response to this need. One instrumental technique that has been identified as a likely candidate to meet this need is near-infrared spectroscopy (NIR). To evaluate NIR as a candidate, a set of 280 fuel samples was used. This sample set contained samples of diesel fuel grades 1 and 2, Jet A-l, JP-5, and JP-8. Inspection data were collected on all the fuels as sample size permitted. Each sample was then scanned using a near-infrared spectrometer. Data analysis, model building, and calibration were conducted using a software package supplied with the instrument.

Research has shown that there are many factors that affect the long-term performance of nitrogen oxides (NOx) control systems used in diesel engine applications. However, if the NOx emissions can be accurately monitored, it might be possible to restore performance by making adjustments to the control systems. This paper presents results from a study that tested the durability of 25 NOx sensors exposed to heavy-duty diesel exhaust for 6,000 hours. The study, conducted by the Advanced Petroleum-Based Fuels - Diesel Emission Controls (APBF-DEC) project, tested the sensors at various locations in the exhaust stream.

This paper discusses the techniques and diagnostic significance of atomic absorption, atomic emission, and infrared spectrometric analysis of crankcase lubricants, with the use of supplementary data where pertinent. The parameters affecting used oil analytical data are discussed in terms of examples from Army general purpose vehicle test engines. Wear metals in used gear oils are also discussed and examples are given. Analytical methods and their applications are fully described, and the equipment and procedures for infrared spectroscopy and gas chromatography techniques are outlined.

An experimental sensor array that measures dynamic exhaust temperature and dynamic smoke for the purpose of diagnosing diesel engine fuel injection equipment was designed, built, and tested. The sensor array is portable and easily installed on truck tailpipes, and was tested using two 6V-53 Detroit Diesel engines. The dynamic temperature sensor is a very high response instrument capable of measuring changes in gas temperature in excess of 104°F/second. The dynamic smokemeter is an optical device designed to measure very low levels of light opacity in the smoke plume, with a response compatible with the engine firing frequency. Dynamic exhaust temperature data had more diagnostic significance than dynamic smoke in the detection of maximum power degrading fuel injection faults.

The Autonomous Underwater Vehicle (AUV) is a fully automated robot submarine that is capable of maintaining a set of electronic sensors under the polar icecap. This function is primarily an issue of automated planning. The AUV is driven by three independent, and often conflicting, goals. These are mission, survival, and covertness. The plan that must be generated is essentially a route to achieve a mission that is acceptable to all three goals. The conflicting goals are implemented as independent expert systems that place constraints on the route taken. A higher level arbiter is used to help resolve conflicts in the situations where restraints posed by the independent goals preclude any solution being found.

This paper describes a new method for measuring oil consumption using laser induced breakdown spectroscopy (LIBS). LIBS focuses a high energy laser pulse on a sample to form a transient plasma. As the plasma cools, each element produces atomic emission lines which can be used to identify and quantify the elements present in the original sample. In this work, a LIBS system was used on simulated engine exhaust with a focus on quantifying the inorganic components (termed ash) of the particulate emissions. Because some of the metallic elements in the ash almost exclusively result from lube oil consumption, their concentrations can also be correlated to an oil consumption rate. Initial testing was performed using SwRI’s Exhaust Composition Transient Operation Laboratory®(ECTO-Lab®) burner system so that oil consumption and ash mass could be precisely controlled.

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.

Engine start-up (cranking) can be an important source of particle emissions from vehicles. With the penetration of GDI vehicles in the global vehicle fleet, it is important to analyze and understand the contribution of start-up particle emissions from GDI vehicles, and the potential effects of fuel properties on that process. In this work, chassis dynamometer based investigation on the effect of several gasoline fuels (commercial and blended) on both, naturally aspirated and turbocharged GDI vehicles were conducted to understand the importance of engine start up, in particular, cranking. 10 commercially available gasoline fuels were tested on a naturally aspirated 2010 model year GDI vehicle, 3 among these commercially available fuels were tested on another 2009 model year turbocharged GDI vehicle, and 8 blended gasoline fuels were tested on 12 other GDI vehicles (7 turbocharged and 5 naturally aspirated) ranging in model years 2011-2015.

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.