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It is challenging to perform probabilistic analysis and design of large-scale structures because probabilistic analysis requires repeated finite element analyses of large models and each analysis is expensive. This paper presents a methodology for probabilistic analysis and reliability based design optimization of large scale structures that consists of two re-analysis methods; one for estimating the deterministic vibratory response and another for estimating the probability of the response exceeding a certain level. The deterministic re-analysis method can analyze efficiently large-scale finite element models consisting of tens or hundreds of thousand degrees of freedom and large numbers of design variables that vary in a wide range. The probabilistic re-analysis method calculates very efficiently the system reliability for many probability distributions of the design variables by performing a single Monte Carlo simulation.

This is a report of a workshop held in mid-August of 2002 at Northwestern University, Evanston, Illinois, to explore what it takes to make a decision regarding environmental systems in the US. The participants in the workshop represented federal government, industry, non-governmental organizations and academia. During the two and a half day workshop, discussions were held on the policy drivers, the strategies and tactics (through a SWOT analysis), the decisions the automotive industry is facing today and the tools available to support decision making.

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.

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.

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 framework for environmentally conscious manufacturing in industry is the life cycle assessment structure developed by the Society of Environmental Toxicology and Chemistry and incorporated into ISO 14000 Environmental Management Systems. Plant managers subject to this standard have the responsibility for environmental improvement projects. Often, applying these projects creates significant risks, particularly if the project is unsuccessful or requires a new technology that has not been widely applied. Plant managers are inherently risk adverse. Thus plant managers need to know not only how a project will succeed but also what could happen if the project fails or results in a state different than intended. Based on that knowledge, plants managers prepare contingency plans. This paper illustrates a method by which the optimum plan and all possible contingency plans can be selected based upon minimizing project cost while maximizing project success to arrive at an improvement goal.

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.

A Computer-Aided Engineering (CAE) model for automobile climate control system is presented to provide engineers with an cost effective analysis tool for designing, developing, and optimizing the vehicle interior climate. It is the objective of this paper to develop a mathematical model which predicts the lumped temperature and lumped humidity variations inside the passenger compartment under design and operating conditions. The transient nature of the passenger cabin temperature, average interior mass temperature, and humidity are modeled using three coupled non-linear ordinary differential equations based on mass and energy balances. These equations are then solved by a fourth-order Runge-Kutta method with adaptive step size control.