Search Results

The Society of Automotive Engineers' (SAE) Clean Snowmobile Challenge (CSC) 2005 was hosted by Michigan Technological University (MTU) in Houghton, Michigan during the week of March 14, 2005. The events were held at the Keweenaw Research Center (KRC), a research arm of MTU. With energy prices on the rise and pollution regulations tightening, efficient and clean modes of transportation are becoming more important. The emissions, noise, and fuel economy events are all significant portions of this year's competition. MTU has hosted the Clean Snowmobile Challenge for the past three years, during which there have been numerous modifications to the events and logistics of the competition to make the experience as beneficial as possible. This paper looks at not only the results from the 2005 competition, but also discusses trends and common design strategies that the winning teams from each year possess.

The Society of Automotive Engineers' (SAE) Clean Snowmobile Challenge 2004 (CSC 2004) was held at Michigan Technological University in Houghton, Michigan, from March 15 - 20, 2004. The Clean Snowmobile Challenge has been a competition in the SAE Collegiate Design Series since 2000, and began in Jackson Hole, Wyoming, as a response to rising concerns about snowmobiling in environmentally-sensitive areas. Teams from fifteen universities competed in CSC 2004. The winning snowmobile (sled) was developed by the University of Wisconsin, Madison, and featured a four-stroke engine with electronic fuel injection (EFI), a two-stage tuned muffler, and catalytic exhaust aftertreatment. A hybrid-electric design was used to increase the snowmobile's powertrain output and improve acceleration. [8] Teams should be competitive in all events to gain enough points to win the competition.

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.

Off-road vehicle operation requires constant decision-making under great uncertainty. Such decisions are multi-faceted and range from acquisition decisions to operational decisions. A major input to these decisions is terrain information in the form of soil properties. This information needs to be propagated to path planning algorithms that augment them with other inputs such as visual terrain assessment and other sensors. In this sequence of steps, many resources are needed, and it is not often clear how best to utilize them. We present an integrated approach where a mission’s overall performance is measured using a multiattribute utility function. This framework allows us to evaluate the value of acquiring terrain information and then its use in path planning. The computational effort of optimizing the vehicle path is also considered and optimized. We present our approach using the data acquired from the Keweenaw Research Center terrains and present some results.

A KIVA-based CFD tool has been utilized to simulate the effect of a Common-Rail injection system applied to a large, uniflow-scavenged, two-stroke diesel engine. In particular, predictions for variations of injection pressure and injection duration have been validated with experimental data. The computational models have been evaluated according to their predictive capabilities of the combustion behavior reflected by the pressure and heat release rate history and the effects on nitric oxide formation and wall temperature trends. In general, the predicted trends are in good agreement with the experimental observations, thus demonstrating the potential of CFD as a design tool for the development of large diesel engines equipped with Common-Rail injection. Existing deficiencies are identified and can be explained in terms of model limitations, specifically with respect to the description of turbulence and combustion chemistry.

There has been an ongoing interest in replacing mineral oil with more biodegradable and/or fire-resistant hydraulic fluids in many mobile equipment applications. Although many alternative fluids may be more biodegradable, or fire-resistant, or both than mineral oil, they often suffer from other limitations such as poorer wear, oxidative stability, and yellow metal corrosion which inhibit their performance in high-pressure hydraulic systems, particularly high pressure piston pump applications. From the fluid supplier's viewpoint, the development of a definitive test, or series of tests, that provides sufficient information to determine how a given fluid would perform with various hydraulic components would be of interest because it would minimize extensive testing. This is often too slow or prohibitively expensive. Furthermore, from OEM's (original equipment manufacturer's) point of view, it would be advantageous to develop a more effective, industry accepted fluid analysis screening.

Noise Path Analysis techniques (NPA) have been developed and refined by the automotive industry for structure-borne noise and vibration evaluation of their products. Off-highway vehicles, particularly those with enclosed cabs, are excellent candidates for the application of these techniques. Like automobiles, many off-highway machines are typically driven by a rotating power source, have a well-defined acoustic receiver space, and use some form of isolation between source and receiver sub-systems. These structural characteristics make NPA a useful tool for identifying dominant sources and energy transfer paths. The objectives of this paper are to revisit the fundamental theory of matrix inversion as it applies to NPA techniques, and to address the common setup and measurement issues encountered when acquiring noise path data on off-highway machines. A general overview of the procedures involved in applying NPA to an off-highway machine will be presented.

Several very different order tracking and analysis techniques for rotating equipment have been developed recently that are available in commercial noise and vibrations software packages. Each of these order tracking methods has distinct trade-offs for many common applications and very specific advantages for special applications in sound quality or noise and vibrations troubleshooting. The Kalman, Vold-Kalman, Computed Order Tracking, and the Time Variant Discrete Fourier Transform as well as common FFT based order analysis methods will all be presented. The strengths and weaknesses of each of the methods will be presented as well as the highlights of their mathematical properties. This paper is intended to be an overview of currently available technology with all methods presented in a common format that allows easy comparison of their properties. Several analytical examples will be presented to thoroughly document each methods' behavior with different types of data.

There is an ongoing interest in biodegradable hydraulic fluids. Biodegradable fluids are often considered to include only vegetable oils, polyol esters and diester base stocks. However, other fluid base stocks including highly refined mineral oils, poly(alpha olefins) and fire-resistant fluids such as water-glycol hydraulic fluids are also biodegradable fluid alternatives. This paper will provide an overview of the international literature on biodegradable fluids, various international testing protocol, fluid base stocks, effect of oxidative stability, material compatibility and pump performance.

During the early 1990s, the Track-Type Tractors Division (TTTD) of Caterpillar Inc. experienced several challenges. The Division faced increasing global competition in the midst of an economic recession. Although intense plant modernization and reorganization occurred in the five previous years, the business unit was not profitable. In 1993, Track-Type Tractors Division instituted its solution -- a change in its culture. Previously, the culture hindered the division’s ability to move forward. This was revealed in a 1992 review detailing the major obstacles inhibiting management from achieving divisional goals. The division leaders recognized that a change in business philosophy, as opposed to further plant modernization, was required to achieve production goals and stay globally competitive.

The Vehicle Engine Cooling System Simulation (VECSS) computer code has been developed at the Michigan Technological University to simulate the thermal response of the cooling system of an on-highway heavy duty diesel powered truck under steady and transient operation. This code includes an engine cycle analysis program along with various components for the four main fluid circuits for cooling air, cooling water, cooling oil, and intake air, all evaluated simultaneously. The code predicts the operation of the response of the cooling circuit, oil circuit, and the engine compartment air flow when the VECSS is operated using driving cycle data of vehicle speed, engine speed, and fuel flow rate for a given ambient temperature, pressure and relative humidity.

To model sprays from pressure-swirl atomizers, the connection between the injector and the downstream spray must be considered. A new model for pressure-swirl atomizers is presented which assumes little knowledge of the internal details of the injector, but instead uses available observations of external spray characteristics. First, a correlation for the exit velocity at the injector exit is used to define the liquid film thickness. Next, the film must be modeled as it becomes a thin, liquid sheet and breaks up, forming ligaments and droplets. A linearized instability analysis of the breakup of a viscous, liquid sheet is used as part of the spray boundary condition. The spray angle is estimated from spray photographs and patternator data. A mass averaged spray angle is calculated from the patternator data and used in some of the calculations.

The Society of Automotive Engineers Fatigue Design & Evaluation Committee [SAE FD&E] is actively working on a total life project for weldments, in which the welding residual stress is a key contributor to an accurate assessment of fatigue life. Physics-based welding process simulation and various types of residual stress measurements were pursued to provide a representation of the residual stress field at the failure location in the fatigue samples. A well-controlled and documented robotic welding process was used for all sample fabrications to provide accurate inputs for the welding simulations. One destructive (contour method) residual stress measurement and several non-destructive residual stress measurements-surface X-ray diffraction (XRD), energy dispersive X-ray diffraction (EDXRD), and neutron diffraction (ND)-were performed on the same or similarly welded samples.

To address the need of increasing fuel economy requirements, automotive Original Equipment Manufacturers (OEMs) are increasing the number of turbocharged engines in their powertrain line-ups. The turbine-driven technology uses a forced induction device, which increases engine performance by increasing the density of the air charge being drawn into the cylinder. Denser air allows more fuel to be introduced into the combustion chamber, thus increasing engine performance. During the inlet air compression process, the air is heated to temperatures that can result in pre-ignition resulting and reduced engine functionality. The introduction of the charge air cooler (CAC) is therefore, necessary to extract heat created during the compression process. The present research describes the physics and develops the optimized simulation method that defines the process and gives insight into the development of CACs.

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.

All around the world, steps are being taken to improve the quality of our environment. Prominent among these are the definition, implementation, and attainment of increasingly stringent emissions regulations for all types of engines, including off-highway diesels. These rigorous regulations have driven use of technologies like after-treatment, advanced air systems, and advanced fuel systems. Fuel dispensed off-highway is routinely and significantly dirtier than fuel from on-highway outlets. Furthermore, fuels used in developing countries can be up to 30 times dirtier than the average fuels in North America. Poor fuel cleanliness, coupled with the higher pressures and performance demands of modern fuel systems, create life challenges greater than encountered with cleaner fuels. This can result in costly disruption of operations, loss of productivity, and customer dissatisfaction in the off-highway market.

The application of Artificial Intelligence (AI) in the automotive industry can dramatically reshape the industry. In past decades, many Original Equipment Manufacturers (OEMs) applied neural network and pattern recognition technologies to powertrain calibration, emission prediction and virtual sensor development. The AI application is mostly focused on reducing product development and validation cost. AI technologies in these applications demonstrate certain cost-saving benefits, but are far from disruptive. A disruptive impact can be realized when AI applications finally bring cost-saving benefits directly to end users (e.g., automation of a vehicle or machine operation could dramatically improve the efficiency). However, there is still a gap between current technologies and those that can fully give a vehicle or machine intelligence, including reasoning, knowledge, planning and self-learning.

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.

Optimizing the performance of the aftertreatment system used on heavy duty diesel engines requires a thorough understanding of the operational characteristics of the individual components. Within this, understanding the performance of the catalyzed particulate filter (CPF), and the development of an accurate CPF model, requires knowledge of the particulate matter (PM) distribution throughout the substrate. Experimental measurements of the PM distribution provide the detailed interactions of PM loading, passive oxidation, and active regeneration. Recently, a terahertz wave scanner has been developed that can non-destructively measure the three dimensional (3D) PM distribution. To enable quantitative comparisons of the PM distributions collected under different operational conditions, it is beneficial if the results can be discussed in terms of the axial, radial, and angular directions.

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.