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This article begins by describing the need for a new method and tool for performing a sustainability assessment for manufacturing processes and systems. A brief literature survey is done to highlight the major existing methods and tools, their function, and their shortcomings. The article goes on to describe the general approach of the method before describing a computer aided tool that has been developed to implement the method. The article concludes with a walk through of a generic use case that describes where such a method would be useful and how such a tool would be implemented.

Recently Michelin has been developing a new airless, integrated tire and wheel combination called the Tweel® tire. The Tweel tire aims at performance levels beyond those possible with conventional pneumatic technology because of its shear band design, added suspension, and potentially decreased rolling resistance. In this paper, we will focus on the environmental impact of the Tweel tire during its life-cycle from manufacturing, through use and disposal. Since the Tweel tire is currently still in the research phase and is not manufactured and used on a large scale, there are uncertainties with respect to end-of-life scenarios and rolling resistance estimates that will affect the LCA. Nevertheless, some preliminary conclusions of the Tweel tire's environmental performance in comparison to a conventional radial tire can be drawn.

Although the design phase can account for a sizable amount of the resources consumed during the product realization process, the time and costs associated with the design process are often neglected when making design decisions. To investigate this issue, we define a process-centric decision model in which the design-phase consumption of resources, such as time and money, is explicitly modeled. While it is clear that the utility of a design is almost always directly impacted by the monetary costs of the design process, our decision model also accounts for the fact that the profit earned by a product depends strongly on its launch date. The decision model allows us thus to consider the trade-off between the time necessary for analysis and the improvement in product quality that results from the analysis. The decision model is sufficiently generic that almost any set of beliefs about the alternatives or analyses, as well as any utility-based preference structure can be modeled.

An operational scheme with fuel-lean and exhaust gas dilution in spark-ignited engines increases thermal efficiency and decreases NOx emission, while these operations inherently induce combustion instability and thus large cycle-to-cycle variation in engine. In order to stabilize combustion variations, the development of an advanced ignition system is becoming critical. To quantify the impact of spark-ignition discharge, ignitability tests were conducted in an optically accessible combustion vessel to characterize the flame kernel development of lean methane-air mixture with CO₂ simulating exhaust diluent. A shrouded fan was used to generate turbulence in the vicinity of J-gap spark plug and a Variable Output Ignition System (VOIS) capable of producing a varied set of spark discharge patterns was developed and used as an ignition source. The main feature of the VOIS is to vary the secondary current during glow discharge including naturally decaying and truncated with multiple strikes.

Computer programs and models are playing an increasing role in simulating vehicle crashworthiness, dynamic, and fuel efficiency. To maximize the effectiveness of these models, the validity and predictive capabilities of these models need to be assessed quantitatively. For a successful implementation of Computer Aided Engineering (CAE) models as an integrated part of the current vehicle development process, it is necessary to develop objective validation metric that has the desirable metric properties to quantify the discrepancy between multiple tests and simulation results. However, most of the outputs of dynamic systems are multiple functional responses, such as time history series. This calls for the development of an objective metric that can evaluate the differences of the multiple time histories as well as the key features under uncertainty.

A copula-based approach for model bias characterization was previously proposed [18] aiming at improving prediction accuracy compared to other model characterization approaches such as regression and Gaussian Process. This paper proposes an adaptive copula-based approach for model bias identification to enhance the available methodology. The main idea is to use cluster analysis to preprocess data, then apply the copula-based approach using information from each cluster. The final prediction accumulates predictions obtained from each cluster. Two case studies will be used to demonstrate the superiority of the adaptive copula-based approach over its predecessor.

To date, model validation metric is prominently designed for non-dynamic model responses. Though metrics for dynamic responses are also available, they are specifically designed for the vehicle impact application and uncertainties are not considered in the metric. This paper proposes the validation metric for general dynamic system responses under uncertainty. The metric makes use of the popular U-pooling approach and extends it for dynamic responses. Furthermore, shape deviation metric was proposed to be included in the validation metric with the capability of considering multiple dynamic test data. One vehicle impact model is presented to demonstrate the proposed validation metric.

Simulation based design optimization has become the common practice in automotive product development. Increasing computer models are developed to simulate various dynamic systems. Before applying these models for product development, model validation needs to be conducted to assess their validity. In model validation, for the purpose of obtaining results successfully, it is vital to select or develop appropriate metrics for specific applications. For dynamic systems, one of the key obstacles of model validation is that most of the responses are functional, such as time history curves. This calls for the development of a metric that can evaluate the differences in terms of phase shift, magnitude and shape, which requires information from both time and frequency domain. And by representing time histories in frequency domain, more intuitive information can be obtained, such as magnitude-frequency and phase-frequency characteristics.

We analyze the frequency response of structural dynamic systems with uncertainties in load and material properties. We introduce uncertainties in the system as interval numbers, and use Interval Finite Element Method (IFEM). Overestimation due to dependency is reduced using a new decomposition for the stiffness and mass matrices, as well as for the nodal equivalent load. In addition, primary and derived quantities are simultaneously obtained by means of Lagrangian multipliers that are introduced in the total energy of the system. The obtained interval equations are solved by means of a new variant of the iterative enclosure method resulting in guaranteed enclosures of relevant quantities. Several numerical examples show the accuracy and efficiency of the new formulation.

We present a new interval-based formulation for the static analysis of plane stress/strain problems with uncertain parameters in load, material and geometry. We exploit the Interval Finite Element Method (IFEM) to model uncertainties in the system. Overestimation due to dependency among interval variables is reduced using a new decomposition strategy for the structural stiffness matrix and the nodal equivalent load vector. Primary and derived quantities follow from minimization of the total energy and they are solved simultaneously and with the same accuracy by means of Lagrangian multipliers. Two different element assembly strategies are introduced in the formulation: one is Element-by-Element, and the other resembles conventional assembly. In addition, we implement a new variant of the interval iterative enclosure method to obtain outer and inner solutions. Numerical examples show that the proposed interval approach guarantees to enclose the exact system response.

Design optimization methods are commonly used for weight reduction subjecting to multiple constraints in automotive industry. One of the major challenges remained is to deal with a large number of design variables for large-scale design optimization problems effectively. In this paper, a new approach based on fuzzy rough set is proposed to address this issue. The concept of rough set theory is to deal with redundant information and seek for a reduced design variable set. The proposed method first exploits fuzzy rough set to screen out the insignificant or redundant design variables with regard to the output functions, then uses the reduced design variable set for design optimization. A vehicle body structure is used to demonstrate the effectiveness of the proposed method and compare with a traditional weighted sensitivity based main effect approach.

One of the major challenges in multiobjective, multidisciplinary design optimization (MDO) is the long computational time required in evaluating the new designs' performances. To shorten the cycle time of product design, a data mining-based strategy is developed to improve the efficiency of heuristic optimization algorithms. Based on the historical information of the optimization process, clustering and classification techniques are employed to identify and eliminate the low quality and repetitive designs before operating the time-consuming design evaluations. The proposed method improves design performances within the same computation budget. Two case studies, one mathematical benchmark problem and one vehicle side impact design problem, are conducted as demonstration.

Ultrasonic fatigue tests (testing frequency around 20 kHz) have been conducted on four different cast aluminum alloys each with a distinct composition, heat treatment, and microstructure. Tests were performed in dry air, laboratory air and submerged in water. For some alloys, the ultrasonic fatigue lives were dramatically affected by the environment humidity. The effects of different factors like material composition, yield strength, secondary dendrite arm spacing and porosity were investigated; it was concluded that the material strength may be the key factor influencing the environmental humidity effect in ultrasonic fatigue testing. Further investigation on the effect of chemical composition, especially copper content, is needed.

This work contributes to the understanding of physical mechanisms that control flashback, or more appropriately combustion recession, in diesel sprays. A large dataset, comprising many fuels, injection pressures, ambient temperatures, ambient oxygen concentrations, ambient densities, and nozzle diameters is used to explore experimental trends for the behavior of combustion recession. Then, a reduced-order model, capable of modeling non-reacting and reacting conditions, is used to help interpret the experimental trends. Finally, the reduced-order model is used to predict how a controlled ramp-down rate-of-injection can enhance the likelihood of combustion recession for conditions that would not normally exhibit combustion recession. In general, fuel, ambient conditions, and the end-of-injection transient determine the success or failure of combustion recession.

End-of-injection transients have recently been shown to be important for combustion and emissions outcomes in diesel engines. The objective of this work is to develop an understanding of the coupling between end-of-injection transients and the propensity for second-stage ignition in mixtures upstream of the lifted diesel flame, or combustion recession. An injection system capable of varying the end-of-injection transient was developed to study single fuel sprays in a newly commissioned optically-accessible spray chamber under a range of ambient conditions. Simultaneous high-speed optical diagnostics, namely schlieren, OH* chemiluminescence, and broadband luminosity, were used to characterize the spatial and temporal development of combustion recession after the end of injection.

In early phases of conceptual design stages for developing a new car in the modern automobile industry, the lack of systematic methodology to efficiently converge to an agreement between the aesthetics and aerodynamic performance tremendously increases budget and time. During these procedures, one of the most important tasks is to create geometric information which is versatilely morphable upon the demands of both of stylists and engineers. In this perspective, this paper proposes a Spline-based Modeling Algorithm (SMA) to implement into performing aerodynamic design optimization research based on CFD analysis. Once a 3-perspective schematic of a car is given, SMA regresses the backbone boundary lines by using optimum polynomial interpolation methods with the best goodness of fit, eventually reconstructing the 3D shape by linearly interpolating from the extracted boundaries minimizing loss of important geometric features.

Since, ice accretion can significantly degrade the performance and the stability of an airborne vehicle, it is imperative to be able to model it accurately. While ice accretion studies have been performed on airplane wings and helicopter blades in abundance, there are few that attempt to model the process on more complex geometries such as fuselages. This paper proposes a methodology that extends an existing in-house Extended Messinger solver to complex geometries by introducing the capability to work with unstructured grids and carry out spatial surface streamwise marching. For the work presented here commercial solvers such as STAR-CCM+ and ANSYS Fluent are used for the flow field and droplet dispersed phase computations. The ice accretion is carried out using an in-house icing solver called GT-ICE. The predictions by GT-ICE are compared to available experimental data, or to predictions by other solvers such as LEWICE and STAR-CCM+.

Two-scale command shaping is a recently proposed feedforward control method aimed at mitigating undesirable vibrations in nonlinear systems. The TSCS strategy uses a scale separation to cancel oscillations arising from nonlinear behavior of the system, and command shaping of the remaining linear problem. One promising application of TSCS is in reducing engine restart and shutdown vibrations found in conventional and in hybrid electric vehicle powertrains equipped with start-stop features. The efficacy of the TSCS during internal combustion engine restart has been demonstrated theoretically and experimentally in the authors’ prior works. The present article presents simulation results and describes the verified experimental apparatus used to study TSCS as applied to the ICE shutdown case. The apparatus represents a typical HEV powertrain and consists of a 1.03 L three-cylinder diesel ICE coupled to a permanent magnet alternating current electric machine through a spur gear coupling.

Modeling and simulation are commonly used in all stages of the design process. This is particularly vital to the success of systems engineering projects where the system under consideration is complex and involves interactions between many interdisciplinary subsystems. In the refining stages of the design process (after concept selection), models and simulations can be used to refine and optimize a system with respect to the decision maker’s objectives. In this paper, a dynamic model of a hydraulic backhoe serves as a test-bed for a large-scale sensitivity analysis and subsequent optimization of the most significant design parameters. The model is optimized under uncertainty with respect to a multi-attribute utility function that includes fuel consumption, cost of the key components, and machine performance.

This study demonstrates the use of pressure sensing technology to predict the crash severity of frontal impacts. It presents an investigation of the pressure change in the front structural elements (bumper, crush cans, rails) during crash events. A series of subsystem tests were conducted in the laboratory that represent a typical frontal crash development series and provided empirical data to support the analysis of the concept. The pressure signal energy at different sensor mounting locations was studied and design concepts were developed for amplifying the pressure signal. In addition, a pressure signal processing methodology was developed that relies on the analysis of the air flow behavior by normalizing and integrating the pressure changes. The processed signal from the pressure sensor is combined with the restraint control module (RCM) signals to define the crash severity, discriminate between the frontal crash modes and deploy the required restraint devices.