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We propose a new metamodeling method to characterize the output (response) random process of a dynamic system with random parameters, excited by input random processes. The metamodel can be then used to efficiently estimate the time-dependent reliability of a dynamic system using analytical or simulation-based methods. The metamodel is constructed by decomposing the input random processes using principal components or wavelets and then using a few simulations to estimate the distributions of the decomposition coefficients. A similar decomposition is also performed on the output random process. A kriging model is then established between the input and output decomposition coefficients and subsequently used to quantify the output random process corresponding to a realization of the input random parameters and random processes. What distinguishes our approach from others in metamodeling is that the system input is not deterministic but random.

This article presents an approach for comparing alternative repairable systems and calculating the value of information obtained by testing a specified number of such systems. More specifically, an approach is presented to determine the value of information that comes from field testing a specified number of systems in order to appropriately estimate the reliability metric associated with each of the respective repairable systems. Here the reliability of a repairable system will be measured by its failure rate. In support of the decision-making effort, the failure rate is translated into an expected utility based on a utility curve that represents the risk tolerance of the decision-maker. The algorithm calculates the change of the expected value of the decision with the sample size. The change in the value of the decision represents the value of information obtained from testing.

Importance Sampling is a popular method for reliability assessment. Although it is significantly more efficient than standard Monte Carlo simulation if a suitable sampling distribution is used, in many design problems it is too expensive. The authors have previously proposed a method to manage the computational cost in standard Monte Carlo simulation that views design as a choice among alternatives with uncertain reliabilities. Information from simulation has value only if it helps the designer make a better choice among the alternatives. This paper extends their method to Importance Sampling. First, the designer estimates the prior probability density functions of the reliabilities of the alternative designs and calculates the expected utility of the choice of the best design. Subsequently, the designer estimates the likelihood function of the probability of failure by performing an initial simulation with Importance Sampling.

Warranty forecasting of repairable systems is very important for manufacturers of mass produced systems. It is desired to predict the Expected Number of Failures (ENF) after a censoring time using collected failure data before the censoring time. Moreover, systems may be produced with a defective component resulting in extensive warranty costs even after the defective component is detected and replaced with a new design. In this paper, we present a forecasting method to predict the ENF of a repairable system using observed data which is used to calibrate a Generalized Renewal Processes (GRP) model. Manufacturing of products may exhibit different production patterns with different failure statistics through time. For example, vehicles produced in different months may have different failure intensities because of supply chain differences or different skills of production workers, for example.

The current system requirements for the power management subsystem and ground combat vehicles for the Future Combat System require higher power and voltages for greater energy efficiency, advanced mobility, lethality and survivability. Efficient and reliable electrical power management is an essential capability within current force ground combat vehicles and will become even more important with the increased electrical power demands of future force vehicles which will exceed the capabilities of onboard power generation/storage technologies. This paper describes how to meet the aforementioned power distribution challenges through the development of a power management software interfaces standard that will provide the flexibility required by various programs and vehicles yet still provide a consistent framework for software development providing a consistent environment for all future Army programs.

A complete probabilistic model of uncertainty in probabilistic analysis and design problems is the joint probability distribution of the random variables. Often, it is impractical to estimate this joint probability distribution because the mechanism of the dependence of the variables is not completely understood. This paper proposes modeling dependence by using copulas and demonstrates their representational power. It also compares this representation with a Monte-Carlo simulation using dispersive sampling.

In the automotive industry, lost foam casting is a relatively new technology, which is gaining popularity among manufacturers. Lost foam casting is a process in which an expanded polystyrene pattern is formed into the shape of the part to be cast. More complex parts are fabricated by simply gluing several simple patterns together. The pattern is then coated with a refractory material consisting of a mineral mixture and binders. Finally, hot metal is poured into the pattern, evaporating the expanded polystyrene and taking shape of the coating shell. However, the automotive industry has observed that a significant number of these fabricated, coated patterns are damaged, or do not meet specifications prior to casting. These are not reusable and inevitably are landfilled. It is the goal of this project to develop a simple, reliable, and inexpensive technology to recover expanded polystyrene from the glue and coating constituents.

Real-time driving simulations are an important tool for verifying vehicle and vehicle component designs with a driver in the loop. They not only provide a cost effective solution but also an ability to verify designs in a safe and controlled operating environment. A real-time driving experiment has been developed for Stryker to compare the ride and handling performance of a baseline passive suspension to that of a Magneto-Rheological (MR) semi-active damper suspension. The Tank Automotive Research Development and Engineering Center (TARDEC) has integrated this new suspension into a real time vehicle dynamics model of the Stryker using the MR suspension model developed by the Original Equipment Manufacturer (OEM). Using this real-time model and the TARDEC Ride Motion Simulator (RMS), TARDEC associates, along with associates from the Stryker Program Management office and the suspension OEM were able to drive and compare the passive and MR Stryker in a virtual environment.

Too often many heat management problems are not solved with thermal analysis because of excessive complexity, time, and cost. A method for quickly solving a sophisticated thermal/fluid system with minimal user interaction and with common desktop computer resources is presented. A desktop (Microsoft Windows™) thermal analysis package, WinTherm, consists of the Generic Processor (pre-processing software), the 3-D Thermal Model (a finite difference nodal network solver), and an Image Viewer (wireframe and animated thermal display). The theoretical basis for this thermal analysis toolkit will be discussed as well as examples of its implementation.

Steady state and dynamic spray characteristics of compound silicon micro machined port fuel injector orifices have been analyzed. Primary interest was placed on the Sauter mean diameter and the spray distribution. Orifice design parameters that influence droplet size and spray distribution were identified. The influence of injection pressure was investigated. The results of this investigation indicate that spray characteristics can be controlled by orifice geometry. Peak dynamic droplet sizes have been found to be significantly larger than steady state droplet sizes. Moderate increases in injector line pressure reduce spray droplet size without significantly affecting spray distribution.

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.

Diesel particulate matter in both the diluted and undiluted state is subject to the processes of coagulation, condensation or evaporation, and nucleation which causes continuous changes in its physical characteristics. The Electrical Aerosol Analyzer (EAA) is used to measure the diesel particle size distribution in the MTU dilution tunnel for a naturally aspirated direct-injection diesel engine operated on the EPA 13 mode cycle. The design and development of accurate and repeatable sampling methods using the EAA are presented. These methods involve both steady-state tunnel and bag measurements. The data indicate a bimodal nature within the 0.001 to 1 μm range. The first mode termed the “embroynic mode” has a saddle point between 0.005 to 0.015 μm and the second mode termed the “aggregation mode” lies between .08 to .15 μm for the number distribution.