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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.

There is an increasing interest in transient thermal simulations of automotive brake systems. This paper presents a high-fidelity CFD tool for modeling complete braking cycles including both the deceleration and acceleration phases. During braking, this model applies the frictional heat at the interface on the contacting rotor and pad surfaces. Based on the conductive heat fluxes within the surrounding parts, the solver divides the frictional heat into energy fluxes entering the solid volumes of the rotor and the pad. The convective heat transfer between the surfaces of solid parts and the cooling airflow is simulated through conjugate heat transfer, and the discrete ordinates model captures the radiative heat exchange between solid surfaces. It is found that modeling the rotor rotation using the sliding mesh approach provides more realistic results than those obtained with the Multiple Reference Frames method.

Contamination protection of brake rotors has been a challenge for the auto industry for a long time. As contamination of a rotor causes corrosion, and that in turn causes many issues like pulsation and excessive wear of rotors and linings, a rotor splash protection shield became a common part for most vehicles. While the rotor splash shield provides contamination protection for the brake rotor, it makes brake cooling performance worse because it blocks air reaching the brake rotor. Therefore, balancing between contamination protection and enabling brake cooling has become a key critical factor when the splash shield is designed. Although the analysis capability of brake cooling performance has become quite reliable, due to lack of technology to predict contamination patterns, the design of the splash protection shield has relied on engineering judgment and/or vehicle tests. Optimization opportunities were restricted by cost and time associated with vehicle tests.

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.

Exhaust gas recirculation (EGR) has been proven an effective strategy for the ignition and combustion control in homogeneous charge compression ignition (HCCI) engines. Carbon dioxide (CO2), a major constituent in EGR, was found to pose a coupled effect on engine combustion: reduced intake oxygen concentration (dilution effect), increased gas heat capacity (thermal effect) and participation of CO2 in chemical reactions (chemical effect). In this paper, a numerical study using a detailed chemical kinetic model was conducted, aiming to isolate the dilution, thermal and chemical effects of CO2 on the two-stage auto-ignition process of n-heptane at engine-like pressure conditions. Four different initial temperatures were selected in this study, representing the low-temperature dominant region, the boundary between the low-temperature region and the negative temperature coefficient (NTC) region, the NTC region and the high temperature region, respectively.

In recent years, implementation of dual phase (DP) Advanced High Strength Steels (AHSS) and Ultra High Strength Steels (UHSS) is increasing in automotive components due to their superior structural performance and vehicle weight reduction capabilities. However, these materials are often sensitive to trimmed edge cracking if stretching along sheared edge occurs in such processes as stretch flanging. Tool wear is another major issue in the trimming of UHSS because of higher contact pressures at the interface between cutting tools and sheet metal blank caused by UHSS’s higher flow stresses and the presence of a hard martensitic in the microstructure. The objective of the present paper is to study the influence of trimming conditions and tool wear on quality of trimmed edge of DP980 steel sheet. For this purpose, mechanically trimmed edges were characterized for DP980 steel, sheared with six different cutting clearances (from 4% to 40% of the sheet thickness).

Experimental results on influence of trimming conditions on the shape of the sheared surface are combined with the results of stretching sheared samples after trimming. The objective of the research described in this paper is to study the mechanism of fracture initiation and cracks propagation during half-a-dog bone tensile test representing sheared edge stretching condition. One side of the sample had sheared surface obtained by the trimming process while the other side of the sample had a smooth surface. Significant attention was paid to understanding of fracture sources. An interrupted tensile test approach was employed to track fracture initiation and propagation during stretching of sheared surface. The results of the experimental study indicated that multiple sources of fracture were observed in the burr area for trimming with clearances exceeding 10% of the material thickness.

This paper develops a design paradigm for universal products. Universal design is term used for designing products and systems that are equally accessible to and usable by people with and without disabilities. Two common challenges for research in this area are that (1) There is a continuum of disabilities making it hard to optimize product features, and (2) There is no effective benchmark for evaluating such products. To exacerbate these issues, data regarding customer disabilities and their preferences is hard to come by. We propose a copula-based approach for modeling market coverage of a portfolio of universal products. The multiattribute preference of customers to purchase a product is modeled as Frank's Archimedean Copula. The inputs from various disparate sources can be collected and incorporated into a decision system.

Experimental results on influence of trimming conditions on the shape of the sheared surface are combined with the results of stretching sheared samples after trimming. The objective of the research described in this paper is to study the mechanism of fracture initiation and cracks propagation during half-a-dog bone tensile test representing sheared edge stretching condition. One side of the sample had sheared surface obtained by the trimming process while the other side of the sample had a smooth surface. Significant attention was paid to understanding of fracture sources. An interrupted tensile test approach was employed to track fracture initiation and propagation during stretching of sheared surface. The results of the experimental study indicated that multiple sources of fracture were observed in the burr area for trimming with clearances exceeding 10% of the material thickness.

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 propose a composite thermal model of the vehicle passenger compartment that can be used to predict and analyze thermal comfort of the occupants of a vehicle. Physical model is developed using heat flow in and out of the passenger compartment space, comprised of glasses, roof, seats, dashboard, etc. Use of a model under a wide variety of test conditions have shown high sensitivity of compartment air temperature to changes in the outside air temperature, solar heat load, temperature and mass flow of duct outlet air from the climate control system of a vehicle. Use of this model has subsequently reduced empiricism and extensive experimental tests for design and tuning of the automatic climate control system. Simulation of the model allowed several changes to the designs well before the prototype hardware is available.

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.

Implications of decision analysis (DA) on engineering design are important and well-documented. However, widespread adoption has not occurred. To that end, the authors recently proposed decision topologies (DT) as a visual method for representing decision situations and proved that they are entirely consistent with normative decision analysis. This paper addresses the practical issue of assessing the DTs of a designer using their responses. As in classical DA, this step is critical to encoding the DA's preferences so that further analysis and mathematical optimization can be performed on the correct set of preferences. We show how multi-attribute DTs can be directly assessed from DM responses. Furthermore, we show that preferences under uncertainty can be trivially incorporated and that topologies can be constructed using single attribute topologies similarly to multi-linear functions in utility analysis. This incremental construction simplifies the process of topology construction.

Response Surface Model (RSM)-based optimization is widely used in engineering design. The major strength of RSM-based optimization is its short computational time. The expensive real simulation models are replaced with fast surrogate models. However, this method may have some difficulties to reach the full potential due to the errors between RSM and the real simulations. RSM's accuracy is limited by the insufficient number of Design of Experiments (DOE) points and the inherent randomness of DOE. With recent developments in advanced optimization algorithms and High Performance Computing (HPC) capability, Direct Multidisciplinary Design Optimization (DMDO) receives more attention as a promising future optimization strategy. Advanced optimization algorithm reduces the number of function evaluations, and HPC cut down the computational turnaround time of function evaluations through fully utilizing parallel computation.

The reliability theory of repairable systems is vastly different from that of non-repairable systems. The authors have recently proposed a ‘decision-based’ framework to design and maintain repairable systems for optimal performance and reliability using a set of metrics such as minimum failure free period, number of failures in planning horizon (lifecycle), and cost. The optimal solution includes the initial design, the system maintenance throughout the planning horizon, and the protocol to operate the system. In this work, we extend this idea by incorporating flexibility and demonstrate our approach using a smart charging electric microgrid architecture. The flexibility is realized by allowing the architecture to change with time. Our approach “learns” the working characteristics of the microgrid. We use actual load and supply data over a short time to quantify the load and supply random processes and also establish the correlation between them.

An automotive cockpit module is a complex assembly, which consists of components and sub-systems. The critical systems in the cockpit module are the instrument panel (IP), the floor console, and door trim assemblies, which consist of many plastic trims. Stiffness is one of the most important parameters for the plastic trims' design, and it should be optimum to meet all the three functional requirements of safety, vibration and durability. This paper presents how the CAE application and various other techniques are used efficiently to predict the stiffness, and the strength of automotive cockpit systems, which will reduce the product development cycle time and cost. The implicit solver is used for the most of the stiffness analysis, and the explicit techniques are used in highly non-linear situations. This paper also shows the correlations of the CAE results and the physical test results, which will give more confidence in product design and reduce the cost of prototype testing.

Growing concerns about the environment, energy dependency, and unstable fuel prices have increased the market share of electric vehicles. This has led to an increased demand for energy efficient routing algorithms that are optimized for electric vehicles. Traditional routing algorithms are focused on finding the shortest distance or the least time route between two points. These approaches have been working well for fossil fueled vehicles. Electric vehicles, on the other hand, require different route optimization techniques. Negative edge costs, battery power and capacity limits, as well as vehicle parameters that are only available at query time, make the task of electric vehicle routing a challenging problem. In this paper, we present a simulated solution to the energy efficient routing for electric vehicles using Particle Swarm Optimization. Simulation results show improvements in the energy consumption of the electric vehicle when applied to a start-to-destination routing problem.

In this paper, experimental study and FEA simulation are performed to investigate the effect of three different methods for joining dissimilar metal coupons in terms of their strength and load transferring capacity. The joining techniques considered include adhesive bonding, bolting and hybrid bolting-and-bonding. Elastic-plastic material model with damage consideration is used for each of the joint components. Traction-separation rule and failure criterion is defined for adhesive. Load transfer capacity and the failure mode are assessed for each type of joining. Joint strength is examined in terms of the effects of adhesive property, bolt preload level, and friction coefficient. Results show that load transferred and failure mechanism vary significantly between samples with different joint methods; preload evolution in bolt changes with friction coefficient; hybrid joint generally has advantage over the other two methods, namely, bolting-only and bonding-only.

Frames are important structures found in many transportation applications such as automotive bodies and train cars. They are also widely employed in buildings, bridges, and other load bearing designs. When a frame is carrying multiple loads, it can potentially risk a catastrophic buckling failure. The loads on the frame may be non-proportional in that one force stays constant while the other is increased until buckling occurs. In this study the buckling problem is formulated as a constrained eigenvalue problem (CEVP). As opposed to other CEVP in which the eigenvectors are forced to comply with a number of the constraints, the eigenvalues in the current CEVP are subject to some equality constraints. A numerical algorithm for solving the constrained eigenvalue problem is presented. The algorithm is a simple trapping scheme in which the computation starts with an initial guess and a window containing the potential target for the eigenvalue is identified.

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.