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A 1-D 2-layer model developed previously at MTU was used in this research to predict the pressure drop, filtration characteristics and various properties of the particulate filter and the particulate deposit layer. The model was used along with dilute emission data to characterize two catalyzed particulate filters (CPFs) having different catalyst loading and catalyst application processes. The model was calibrated and validated with data obtained from steady state experiments conducted using a 1995 Cummins M11-330E heavy-duty diesel engine with manual EGR with different fuels for the two different CPFs. The two different catalyzed particulate filters were CPF III (5 gms/ft3 Pt) and CPF V (50 gms/ft3 Pt). Both the CPFs had cordierite substrates with CPF III and CPF V had MEX and NEX catalyst type formulation respectively. The CPF III filter was catalyzed using a solution-impregnated process while the CPF V filter was catalyzed using a wash coat process.

This investigation is a continuation of a previous study by these authors in which a Lagrange polynomial interpolation method was developed to evaluate spray source terms and also to distribute the source terms onto the gas mesh; the method was applied to the liquid-gas momentum exchange. For this investigation, the method has been extended to the mass exchange between the liquid and gas phases due to evaporation. The Lagrange polynomial interpolation and source term distribution methods are applied to the liquid-gas mass and momentum exchange and are evaluated for evaporating sprays using KIVA3 as a modeling platform. These methods are compared with the standard “nearest neighbor” method of KIVA3, and experimental data are used to establish their validity. The evaluation criteria used include the liquid and vapor spray penetration, gas velocities and the computational stability.

Physical, chemical, and biological characterization data for the particulate emissions from a Caterpillar 3208 diesel engine with and without Corning porous ceramic particulate traps are presented. Measurements made at EPA modes 3,4,5,9,lO and 11 include total hydrocarbon, oxides of nitrogen and total particulate matter emissions including the solid fraction (SOL), soluble organic fraction (SOF) and sulfate fraction (SO4), Chemical character was defined by fractionation of the SOF while biological character was defined by analysis of Ames Salmonella/ microsome bioassay data. The trap produced a wide range of total particulate reduction efficiencies (0-97%) depending on the character of the particulate. The chemical character of the SOF was significantly changed through the trap as was the biological character. The mutagenic specific activity of the SOF was generally increased through the trap but this was offset by a decrease in SOF mass emissions.

A study has been made of piston ring wear and total engine wear using literature data and new experimental results. The main purpose of the study was to establish the effects of oil and coolant temperatures on engine wear. Wear trends that were found in the early 1960's may not be valid any longer because of the development of higher BMEP turbocharged diesel engines, better metallurgical wear surfaces and improved lube oil properties. New data are presented for the purpose of describing present wear trends. A direct-injection, 4-cycle, turbocharged diesel engine was used for the wear tests. The radioactive tracer technique was used to measure the top piston ring chrome face wear. Atomic emission spectroscopy was employed to determine the concentration of wear metals in the oil to determine total engine wear based on iron and lead. The data were analyzed and compared to the results found in the literature from previous investigators.

Robust autonomous navigation in unstructured environments is an unsolved problem and critical to the operation of autonomous military and rescue ground vehicles. Two-dimensional path planners operating on occupancy grids or costs maps can produce infeasible paths when the operational area includes complex terrain. Recently, sample-based path planners that plan on LiDAR-acquired point-cloud maps have been proposed. These approaches require no discretization of the operational area and provide direct pose estimation by modeling vehicle and terrain interaction. In this paper, we show that direct sample-based path planning on point clouds is effective and robust in unstructured environments. Robustness is demonstrated by completing a system parameter sensitivity analysis of the system in an Unreal simulation environment and partnered with field validation.

The steering assembly is a part of an automotive suspension system that provides control and stability. It provides control of direction, stability, and control over placement of the car. Optimization of the vehicle in weight results in enhanced performance and low fuel consumption, more so for an all-terrain race car. Optimization in this paper loosely refers to weight reduction and achieving the optimum stiffness to weight ratio of each component. This research encompasses various aspects linked to conceptualizing, designing, analysing, optimizing, and finally manufacturing the steering sub-system. Analytical calculations for mechanical design were performed using data from various experiments and jigs. CAD was developed using SolidWorks, and various analyses were performed using Altair HyperWorks. Finite Element Analysis (FEA) was primarily used to build stress plots and locate weak spots aiding optimization.

The introduction of a thin fluid layer between two layers of sheet metal offers a highly effective and economical alternative to the use of constrained viscoelastic damping layers in sheet metal structures. A diesel engine gear cover, which is constructed of two sheet metal sections spot welded together, takes advantage of fluid layer damping to produce superior vibration and sound radiation performance. In this paper, the bending of a double layered plate coupled through a thin fluid layer is modeled using a traveling wave approach which results in a impedance function that can be used to assess the vibration and sound radiation performance of practical double layered plate structures. Guided by this model, the influence of fluid layer thickness and inside-to-outside sheet thickness is studied.

The study of spray-wall interaction is of great importance to understand the dynamics during fuel-surface impingement process in modern internal combustion engines. The identification of droplet post-impingement pattern (contact, transition, non-contact) and droplet characteristics can quantitatively provide an estimation of energy transfer for spray-wall interaction, thus further influencing air-fuel mixing and emissions under combusting conditions. Theoretical criteria of single droplet post-impingement pattern on a dry wall have been experimentally and numerically studied by many researchers to quantify the hydrodynamic droplet behaviors. However, apart from model fidelity, another issue is the scalability. A theoretical criterion developed from one case might not be well suited to another scenario. In this paper, a data-driven approach for single droplet-dry wall post-impingement pattern utilizing arithmetical machine learning classification methods is proposed and demonstrated.

This paper reports an experimental and numerical investigation on the spatial and temporal liquid- and vapor-phase distributions of diesel fuel spray under engine-like conditions. The high pressure diesel spray was investigated in an optically-accessible constant volume combustion vessel for studying the influence of the k-factor (0 and 1.5) of a single-hole axial-disposed injector (0.100 mm diameter and 10 L/d ratio). Measurements were carried out by a high-speed imaging system capable of acquiring Mie-scattering and schlieren in a nearly simultaneous fashion mode using a high-speed camera and a pulsed-wave LED system. The time resolved pair of schlieren and Mie-scattering images identifies the instantaneous position of both the vapor and liquid phases of the fuel spray, respectively. The studies were performed at three injection pressures (70, 120, and 180 MPa), 23.9 kg/m3 ambient gas density, and 900 K gas temperature in the vessel.

An automobile seat’s thermal performance can be challenging to quantify since it requires comprehensive human subject testing. Seat manufacturers must rely on subjective ratings to understand how the construction of a seat and its underlying heating and cooling technology may compare to other seats. Other factors may influence seat ratings published by global marketing information services companies (e.g., JD Power and Associates). In particular, occupants may be biased by the vehicle class in which a seat is installed and by how much the contribution of a specific vehicle’s HVAC system performance affects the perception of seat thermal comfort. Therefore, there is a need for an objective testing methodology that does not rely on human participants but is still capable of producing a thermal performance rating in terms of established thermal comfort scales.

Off road navigation demands ground robots to traverse complex and often changing terrain. Classification and assessment of terrain can improve path planning strategies by reducing travel time and energy consumption. In this paper we introduce a terrain classification and assessment framework that relies on both exteroceptive and proprioceptive sensor modalities. The robot captures an image of the terrain it is about to traverse and records corresponding vibration data during traversal. These images are manually labelled and used to train a support vector machine (SVM) in an offline training phase. Images have been captured under different lighting conditions and across multiple locations to achieve diversity and robustness to the model. Acceleration data is used to calculate statistical features that capture the roughness of the terrain whereas angular velocities are used to calculate roll and pitch angles experienced by the robot.

The AutoDrive Challenge competition sponsored by General Motors and SAE gives undergraduate and graduate students an opportunity to get hands-on experience with autonomous vehicle technology and development as they work towards their degree. Michigan Technological University has participated in the AutoDrive Challenge since its inception in 2017 with students participating through MTU’s Robotic System Enterprise. The MathWorks Simulation Challenge has been a component of the competition since its second year, tasking students with the development of perception, control and testing algorithms using MathWorks software products. This paper presents the pedagogical approach graduate student mentors used to enable students to build their understanding of autonomous vehicle concepts using familiar tools. This approach gives undergraduate students a productive experience with these systems that they may not have encountered in coursework within their academic program.

A 2-D computational model was developed to describe the flow and filtration processes, in a honeycomb structured ceramic diesel particulate trap. This model describes the steady state trap loading, as well as the transient behavior of the flow and filtration processes. The theoretical model includes the effect of a copper fuel additive on trap loading and transient operation. The convective terms were based on a 2-D analytical flow field solution derived from the conservation of mass and momentum equations. The filtration theory incorporated in the time dependent numerical code included the diffusion, inertia, and direct interception mechanisms. Based on a measured upstream particle size distribution, using the filtration theory, the downstream particle size distribution was calculated. The theoretical filtration efficiency, based on particle size distribution, agreed very well (within 1%) with experimental data for a number of different cases.