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Lookup tables and functions are widely used in real-time embedded automotive applications to conserve scarce processor resources. To minimize the resource utilization, these lookup tables (LUTs) commonly use custom data structures. The lookup function code is optimized to process these custom data structures. The legacy routines for these lookup functions are very efficient and have been in production for many years. These lookup functions and the corresponding data structures are typically used for calibration tables. The third-party calibration tools are specifically tailored to support these custom data structures. These tools assist the calibrators in optimizing the control algorithm performance for the targeted environment for production. Application software typically contains a mix of both automatically generated software and manually developed code. Some of the same calibration tables may be used in both auto generated and hand-code [ 1 ] [ 2 ].

Diesel particulate trap regeneration is a complex process involving the interaction of phenomena at several scales. A hierarchy of models for the relevant physicochemical processes at the different scales of the problem (porous wall, filter channel, entire trap) is employed to obtain a rigorous description of the process in a multidimensional context. The final model structure is validated against experiments, resulting in a powerful tool for the computer-aided study of the regeneration behavior. In the present work we employ this tool to address the effect of various spatial non-uniformities on the regeneration characteristics of diesel particulate traps. Non-uniformities may include radial variations of flow, temperature and particulate concentration at the filter inlet, as well as variations of particulate loading. In addition, we study the influence of the distribution of catalytic activity along the filter wall.

As demand for wall-flow Diesel Particulate Filters (DPF) increases, accurate predictions of DPF behavior, and in particular their pressure drop, under a wide range of operating conditions bears significant engineering applications. In this work, validation of a model and development of a simulator for predicting the pressure drop of clean and particulate-loaded DPFs are presented. The model, based on a previously developed theory, has been validated extensively in this work. The validation range includes utilizing a large matrix of wall-flow filters varying in their size, cell density and wall thickness, each positioned downstream of light or heavy duty Diesel engines; it also covers a wide range of engine operating conditions such as engine load, flow rate, flow temperature and filter soot loading conditions. The validated model was then incorporated into a DPF pressure drop simulator.

In terms of manufacturing processes and systems, decision-makers may encounter difficulty in making environmentally friendly choices. This difficulty arises largely because of the nascency of environmentally responsible manufacturing. There is a sparsity of environmental information on processes and a variety of seemingly unconnected tools, methods, concepts, etc. To help decisions-makers understand the nature of a process and identify the appropriate tools/methods that may be most suited to reducing environmental impact, a classification system for manufacturing processes is established. Methods and tools for environmentally responsible manufacturing are then identified for each class. Two examples are presented to show how the new classification system may be applied in environmentally responsible manufacturing.

As the pool of existing non-renewable natural resources continues to shrink, it will be necessary for government and industrial leaders to achieve a workable strategy for the intelligent allocation of scarce resources. In this paper, a method of quantifying the environmental and resource impacts of product redesign is proposed. This new method utilizes Input Output Analysis coupled with the Markovian decision making into a single matrix-based tool. The benefit of a fully developed tool would be the ability to make informed pre-production decisions leading to optimum product and process designs with minimal environmental impact. This paper illustrates this technique with an example based upon real industry data and extrapolated effects.

The spray pattern of a fuel injector is a key factor in the mixing of the fuel with the air. One effective means of determining the fuel distribution in the spray is to collect the fuel in tubes, from various regions of the spray. The amount of fuel in the tubes is measured. These measurements are used to create diagrams and curves which graphically represent the fuel distribution within the spray. The term “Patternator” has come to mean a device which determines the spray distribution, in the sense that the device determines the pattern of the spray. The objective of this paper is to describe the operation, features, and performance of an automated patternator designed and built at Michigan Technological University for Ford Motor Company. The patternator system was constructed for rapid determination of the spray pattern in order to expedite the development of automotive port fuel injectors.

A computer simulation model to predict the thermal responses of an on-highway heavy duty diesel truck in transient operation was used to study several important cooling system design and operating variables. The truck used in this study was an International Harvester COF-9670 cab-over-chassis vehicle equipped with a McCord radiator, Cummins NTC-350 diesel engine, Kysor fan-clutch and shutter system, aftercooler, and standard cab heater and cooling system components. Input data from several portions of a Columbus to Bloomington, Indiana route were used from the Vehicle Mission Simulation (VMS) program to determine engine and vehicle operating conditions for the computer simulation model. The thermostat-fan, thermostat-shutter-fan, and thermostat-winterfront-fan systems were studied.

The First Annual Blizzard Baja was hosted by Michigan Technological University's SAE Student Branch on February 7, 1981. This was a competition between student designed vehicles which had previously competed in summer Baja events. The Blizzard Baja consisted of a one hour endurance race run on ice and snow. The purpose was to provide the student engineers an opportunity to test their vehicles in cold weather, snow and icy conditions.