Search Results

A simulation-based, system reliability-based design optimization (RBDO) method is presented that can handle problems with multiple failure regions and correlated random variables. Copulas are used to represent the correlation. The method uses a Probabilistic Re-Analysis (PRRA) approach in conjunction with a trust-region optimization approach and local metamodels covering each trust region. PRRA calculates very efficiently the system reliability of a design by performing a single Monte Carlo (MC) simulation per trust region. Although PRRA is based on MC simulation, it calculates “smooth” sensitivity derivatives, allowing therefore, the use of a gradient-based optimizer. The PRRA method is based on importance sampling. It provides accurate results, if the support of the sampling PDF contains the support of the joint PDF of the input random variables. The sequential, trust-region optimization approach satisfies this requirement.

Piston design is a challenging engineering problem which involves complex physics and requires satisfying multiple performance objectives. Uncertainty in piston operating conditions and variability in piston design variables are inevitable and must be accounted for. The piston assembly can be a major source of engine mechanical friction and cold start noise, if not designed properly. In this paper, an analytical piston model is used in a deterministic and probabilistic (reliability-based) multi-objective design optimization process to obtain an optimal piston design. The model predicts piston performance in terms of scuffing, friction and noise, In order to keep the computational cost low, efficient and accurate metamodels of the piston performance metrics are used. The Pareto set of all optimal solutions is calculated allowing the designer to choose the “best” solution according to trade-offs among the multiple objectives.

Vehicle restraint system design is a difficult optimization problem to solve because (1) the nature of the problem is highly nonlinear, non-convex, noisy, and discontinuous; (2) there are large numbers of discrete and continuous design variables; (3) a design has to meet safety performance requirements for multiple crash modes simultaneously, hence there are a large number of design constraints. Based on the above knowledge of the problem, it is understandable why design of experiment (DOE) does not produce a high-percentage of feasible solutions, and it is difficult for response surface methods (RSM) to capture the true landscape of the problem. Furthermore, in order to keep the restraint system more robust, the complexity of restraint system content needs to be minimized in addition to minimizing the relative risk score to achieve New Car Assessment Program (NCAP) 5-star rating.

We have recently obtained experimental data and used them to develop computational models to quantify occupant impact responses and injury risks for military vehicles during frontal crashes. The number of experimental tests and model runs are however, relatively small due to their high cost. While this is true across the auto industry, it is particularly critical for the Army and other government agencies operating under tight budget constraints. In this study we investigate through statistical simulations how the injury risk varies if a large number of experimental tests were conducted. We show that the injury risk distribution is skewed to the right implying that, although most physical tests result in a small injury risk, there are occasional physical tests for which the injury risk is extremely large. We compute the probabilities of such events and use them to identify optimum design conditions to minimize such probabilities.

A common approach to the validation of simulation models focuses on validation throughout the entire design space. A more recent methodology validates designs as they are generated during a simulation-based optimization process. The latter method relies on validating the simulation model in a sequence of local domains. To improve its computational efficiency, this paper proposes an iterative process, where the size and shape of local domains at the current step are determined from a parametric bootstrap methodology involving maximum likelihood estimators of unknown model parameters from the previous step. Validation is carried out in the local domain at each step. The iterative process continues until the local domain does not change from iteration to iteration during the optimization process ensuring that a converged design optimum has been obtained.

Reliability is an important engineering requirement for consistently delivering acceptable product performance through time. As time progresses, the product may fail due to time-dependent operating conditions and material properties, component degradation, etc. The reliability degradation with time may increase the lifecycle cost due to potential warranty costs, repairs and loss of market share. Reliability is the probability that the system will perform its intended function successfully for a specified time interval. In this work, we consider the first-passage reliability which accounts for the first time failure of non-repairable systems. Methods are available in the literature, which provide an upper bound to the true reliability which may overestimate the true value considerably. Monte-Carlo simulations are accurate but computationally expensive.

Understanding reliability is critical in design, maintenance and durability analysis of engineering systems. A reliability simulation methodology is presented in this paper for vehicle fleets using limited data. The method can be used to estimate the reliability of non-repairable as well as repairable systems. It can optimally allocate, based on a target system reliability, individual component reliabilities using a multi-objective optimization algorithm. The algorithm establishes a Pareto front that can be used for optimal tradeoff between reliability and the associated cost. The method uses Monte Carlo simulation to estimate the system failure rate and reliability as a function of time. The probability density functions (PDF) of the time between failures for all components of the system are estimated using either limited data or a user-supplied MTBF (mean time between failures) and its coefficient of variation.

This paper discusses simplified lumped parameter thermal modeling of power train components. In particular, it discusses the tradeoff between model complexity and the ability to correlate the predicted temperatures and flow rates with measured data. The benefits and problems associated with using a three lumped mass model are explained and the value of this simpler model is promoted. The process for correlation and optimization using modern software tools is explained. Examples of models for engines and transmissions are illustrated along with their predictive abilities over typical driving cycles.

In this article we present an approach to identify the system topology using simulation for reliability calculations. The system topology provides how all components in a system are functionally connected. Most reliability engineering literature assumes that either the system topology is known and therefore all failure modes can be deduced or if the system topology is not known we are only interested in identifying the dominant failure modes. The authors contend that we should try to extract as much information about the system topology from failure or success information of a system as possible. This will not only identify the dominant failure modes but will also provide an understanding of how the components are functionally connected, allowing for more complicated analyses, if needed. We use an evolutionary approach where system topologies are generated at random and then tested against failure or success data. The topologies evolve based on how consistent they are with test data.

Design optimization often relies on computational models, which are subjected to a validation process to ensure their accuracy. Because validation of computer models in the entire design space can be costly, a recent approach was proposed where design optimization and model validation were concurrently performed using a sequential approach with both fixed and variable-size local domains. The variable-size approach used parametric distributions such as Gaussian to quantify the variability in test data and model predictions, and a maximum likelihood estimation to calibrate the prediction model. Also, a parametric bootstrap method was used to size each local domain. In this article, we generalize the variable-size approach, by not assuming any distribution such as Gaussian. A nonparametric bootstrap methodology is instead used to size the local domains. We expect its generality to be useful in applications where distributional assumptions are difficult to verify, or not met at all.

For designing new products or developing new specifications, the reliability performance of systems and components experienced by the customer provides invaluable information for the engineer. This information, not only provides for the visibility of reliability requirements, but also an awareness of potential degradation of the systems and components during its life cycle. In this paper, a method is presented for predicting vehicle system and component reliability from vehicle fleet repair data. This method combines sampling stratification, computer data analysis and statistical modeling techniques into a reliability analysis procedure to provide reliability prediction. Specifically, published vehicle fleet data was used to provide the basis for predicting the vehicle system and component reliability at any mileage level.

This paper discusses the feasibility and issues associated with integrating a consumer off-the shelf product into a vehicle. For this evaluation, we selected a handheld personal computer (HPC), cellular telephone and modem to integrate with the vehicle audio, climate and system controls. Connectivity between the HPC and the vehicle is established by the use of the standard infrared serial data link that comes with the HPC. Connectivity outside the vehicle uses a cellular telephone for voice and a cellular digital packet data (CDPD) modem for data. This system is built into the Dodge ESX-2 hybrid powered concept vehicle for demonstration.

The rapid growth of information technology has the potential to affect many of the reasons why people drive. The Internet is arguably the most significant recent milestone in the growth of information technology. This paper examines the ways Internet communication might affect the travel experience by a) eliminating traditional reasons for personal travel, b) providing new reasons, c) changing the balance between personal and freight travel, and d) changing trip length distribution. Changes of the types listed could affect the product demand "mix" for electric, hybrid-electric and fuel cell vehicles being developed.

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.

Embedded systems continue to become more complex. As a result, more companies are utilizing model-based design (MBD) development methods and tools. The use of MBD methods and tools is helpful in increasing time to market and having instant feedback on the system design. One area that continues to mature is the testing and verification of the MBD systems. This paper introduces a hybrid approach to functional tests. The test system is composed of simulation software and real-time hardware. It is not always necessary to test a system in a real-time environment, but it is recommended if the goal is to deploy the system to a situation that requires real-time response. Vehicle drive cycles and powertrain control are utilized in this research as the example test case for this paper. In order to test the algorithms on a real-time system, it is necessary to understand the target controller's computing limitations and adjust the algorithms to meet these limitations.

Adhesive bonding has many applications in the automotive industry. The single-lapped bonded joint is the most typically used among various bonding types. This paper presents experimental research for determining the strain field of the single-lapped joint under tensile loading. The materials for the joint are epoxy-based structural adhesive and low-carbon electrolytic zinc steel plate. In the study, a DIC (digital image correlation) system was adopted to measure the strain distribution of the bonded joint during a tensile test. The bonded steel coupons in the tensile test were prepared according to the ASTM standard. During the measurement, images of the coupon joint were taken before and after the deformation process. Then the DIC system measured the strain of bonded joint by comparing two consecutive images. The measured data from the DIC was compared to data taken simultaneously from a traditional extensometer.

The automobile industry has an increasing demand for lightweight components, improve product performance, efficiency and increase safety. For optimization of design and manufacturing of these components the detail measurement of critical material properties such as strain limit, strength coefficients, anisotropy coefficients et al, are required. The most commonly used method for finding the material properties is tensile test with extensometer. But this system only provides an average strain over the specimen gauge length and is not applicable to post diffuse necking. Digital Image Correlation (DIC) technique is the latest state of art technique. Because of its capability of fast data acquisition, this technique is suited well for characterization of material properties both in the elastic and plastic ranges. It also has advantages of full field, non-contact, and considerately high precision for displacement and strain measurements.

Higher horsepower per liter engines have put more demand on the crankshaft, often requiring the use of forged steel. This paper examines cost reduction opportunities to offset the penalties associated with forged steel, with raw material and machinability being the primary factors evaluated. A cost model for crankshaft processing is utilized in this paper as a design tool to select the lowest cost material grade. This model is supported by fatigue and machinability data for various steel grades. Materials considered are medium carbon, low alloy, and microalloy steels; the effects of sulfur as a machining enhancer is also studied.

The automobile industry has been using high-density polyethylene (HDPE) as a material to fabricate fuel tanks. Because untreated HDPE is permeable to the primary constituents of gasoline, these fuel tanks are now being produced with various barrier technologies that significantly reduce this permeation rate. Four currently available barrier technologies are fluorination, sulfonation, coextrusion, and the laminar barrier technology. These technologies have successfully proven to decrease the permeation rate of pure gasoline. However, it is suspected that their effectiveness may be reduced when alcohols are introduced into the fuel blend. In this work, we determine the permeation rates of gasoline-alcohol fuel blends through HDPE by conducting tests on 22-gallon HDPE fuel tanks and on small HDPE bottles fabricated with and without these barrier technologies. The goal of this study is to provide a comprehensive evaluation of these four barrier technologies.

Advances in computerized solid modeling techniques allow the realistic representation of exterior body panels as solid models, at the concept stage of part design. A flow chart of the process is presented on the use of solid models to create exterior body panels. The flow chart allows a study of the process and is extended to the next generation of capabilities.