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The challenges of flex-fuel vehicle (FFV) emissions measurements have recently come to the forefront for the emissions testing community. The proliferation of ethanol blended gasoline in fractions as high as 85% has placed a new challenge in the path of accurate measures of NMHC and NMOG emissions. Test methods need modification to cope with excess amounts of water in the exhaust, assure transfer and capture of oxygenated compounds to integrated measurement systems (impinger and cartridge measurements) and provide modal emission rates of oxygenated species. Current test methods fall short of addressing these challenges. This presentation will discuss the challenges to FFV testing, modifications made to Ford Motor Company’s Vehicle Emissions Research Laboratory test cells, and demonstrate the improvements in recovery of oxygenated species from the vehicle exhaust system for both regulatory measurements and development measurements.

Today's automotive and electronics technologies are evolving so rapidly that educators and industry are both challenged to re-educate the technological workforce in the new area before they are replaced with yet another generation. In early November 2009 Ford's Product Development senior management formally approved a proposal by the University of Detroit Mercy to transform 125 of Ford's “IC Engine Automotive Engineers” into “Advanced Electric Vehicle Automotive Engineers.” Two months later, the first course of the Advanced Electric Vehicle Program began in Dearborn. UDM's response to Ford's needs (and those of other OEM's and suppliers) was not only at the rate of “academic light speed,” but it involved direct collaboration of Ford's electric vehicle leaders and subject matter experts and the UDM AEV Program faculty.

Geometric Dimensioning and Tolerancing is used to describe the allowed feature variations regarding the product design. Tolerance specification is important in many stages of all phases on product development. The product development engineering need to define the symbols to use on the Feature Control Frame of every component. Since the component function has an increment on its complexity year over year, it is not trivial to define those symbols anymore. The determination of dimensional tolerance shall be preceded by careful specification of the types of tolerance and symbols that will be applied in controlled features. Poor tolerance specifications can increase the production cost, require late product changes or lead to legal issues.

Because of rising complexity during the automotive product development process, the number of disciplines to be concerned has been significantly increased. Multidisciplinary design optimization (MDO) methodology, which provides an opportunity to integrate each discipline and conduct compromise searching process, is investigated and introduced to achieve the best compromise solution for the automotive industry. To make a better application of MDO, the suitable coupling strategy of different disciplines and efficient optimization techniques for automotive design are studied in this article. Firstly, considering the characteristics of automotive load cases which include many shared variables but rare coupling variables, a multilevel MDO coupling strategy based on enhanced collaborative optimization (ECO) is studied to improve the computational efficiency of MDO problems.

In this paper, a systems engineering approach is explored to evaluate the effect of design parameters that contribute to the performance of the embedded Automatic Speech Recognition (ASR) engine in a vehicle. This includes vehicle designs that influence the presence of environmental and HVAC noise, microphone placement strategy, seat position, and cabin material and geometry. Interactions can be analyzed between these factors and dominant influencers identified. Relationships can then be established between ASR engine performance and attribute performance metrics that quantify the link between the two. This helps aid proper target setting and hardware selection to meet the customer satisfaction goals for both teams.

Among the many sources of uncertainty in an unsteady computational fluid dynamics (CFD) simulation, the statistical uncertainty in the mean value of a fluctuating quantity (for example, the drag coefficient) is of practical importance for vehicle design and development. This uncertainty can be reduced by extending the simulation run length, however, this increases the computational cost and leads to longer turnaround times. Moreover, it is desirable to be able to run an unsteady CFD simulation for the minimum amount of time necessary to reach an acceptable amount of uncertainty in the quantity of interest. This work assesses several methods for calculating the uncertainty in the mean of an unsteady signal. Simulated noise is used to validate the methods, and evaluation is carried out using signals from CFD simulations of realistic vehicle geometries. Calculating the uncertainty in the difference between two signals is also discussed.

In recent years, the design of automotive components and assemblies have resulted in an over-reliance on advanced CAE tools especially the Finite Element Analysis. An emphasis on cost reduction and commonization of components in automotive industry has made it necessary to use the CAE tools in innovative ways. Use of FEA as a effective product development tool can be greatly enhanced if it provides a high degree of correlation with physical tests, thereby greatly limiting the investment in expensive prototypes and testing. This paper will discuss a robustness based methodology to realize effective correlation of finite element models with actual physical tests on automotive seat structure assembly, at a component, sub-system, and systems level. Based on a parameter design approach, the various factors that affect the degree of correlation between CAE models and physical tests will be described.

Knowledge mapping is a first and mandatory step in creation of system architecture. This paper considers the conceptual modeling of automotive systems, and discusses the creation of a knowledge-based model with respect to the Object Process Methodology an approach used in designing intelligent systems by depicting them using object models and process models. With this knowledge, systems engineer should consider what a product is comprised of (its structure), how it operates (its dynamics), and how it interacts with the environment. As systems have become more complex, a prevalent problem in systems development has been the number of accruing errors. A clearly defined and consistent mapping of knowledge regarding structure, operation and interaction is necessary to construct an effective and useful system. An interactive, iterative and consistent method is needed to cope with this complex and circular problem.

A hybrid electric vehicle (HEV) system model, which directly simulates vehicle drive cycles with interactions among driver, environment, vehicle hardware and vehicle controls, is a critical CAE tool used through out the product development process to project HEV fuel economy (FE) capabilities. The accuracy of the model is essential and directly influences the HEV hardware designs and technology decisions. This ultimately impacts HEV product content and cost. Therefore, improving HEV system model accuracy and establishing high-level model-test correlation are imperative. This paper presents a Parameter Diagram (P-Diagram) based model-test correlation framework which covers all areas contributing to potential model simulation vs. vehicle test differences. The paper describes each area in detail and the methods of characterizing the influences as well as the correlation metrics.

Automotive Product Development organisations are challenged with ever increasing levels of systems complexity driven by the introduction of new technologies to address environmental concerns and enhance customer satisfaction within a highly competitive and cost conscious market. The technical difficulty associated with the engineering of complex automotive systems is compounded by the increase in sophistication of the control systems needed to manage the integration of technology packages. Most automotive systems have an electro-mechanical structure with control and software features embedded within the system. The conventional methods for design analysis and synthesis are engineering discipline focused (mechanical, electrical, electronic, control, software).

Under various real-world scenarios, vehicles can become disabled and require towing. OEMs allow a few options for vehicle wrecker towing that include wheel lift tow using a stinger or towing on a flatbed. These methods entail multiple loading events that need to be assessed for damage to the towed vehicle. OEMs have several testing and evaluation methods in place for those scenarios with majority requiring physical vehicle prototypes. Recent focus to reduce product development time and cost has replaced the need for prototype testing with analytical verification methods. In this paper, the CAE method involving multibody dynamic simulation (MBDS) as well as finite element analysis (FEA) of vehicle flatbed operation, winching onto a flatbed, and stinger-pull towing are discussed.

A new methodology to predict vehicle interior wind noise using CFD results has been developed. The CFD simulation replaces wind tunnel testing for providing flow field information around vehicle greenhouse. A loadcase model based on the CFD results is used to excite an SEA vehicle model. This new approach has been demonstrated on a production vehicle with success for the frequency range of 250-10K Hz. The CAE prediction of interior wind noise agrees within 0.2 sones from wind tunnel testing. The model has been used to evaluate wind noise performance with different door glass design parameters. A glass thickness change from 3.8 mm to 4.8 mm results in 1.1 sones improvement, which agrees well to 1.4 sones improvement from testing. Laminated glass with about 3 times higher damping results in 2.5 sones improvement. This methodology using CFD results can be used in the early stage of product development to impact designs.

Residual stress can affect directly the quality of products as result of manufacturing process, for example, the vehicular assembly of sheet metal parts, where the welding of thin plates is applied. One method that can prevent it is the use of CAE which helps to understand the mechanism of welding effects using finite element methods. Using this tool is possible to evaluate the impact of the welding process in order to reduce time and costs during the product development. It helps also to foresee future durability/customer usage problems during the product life.

We present results which verify the design parameters and suggest performance capabilities/limitations of the Mars Gravity Biosatellite's proposed atmospherics control subassembly. Using a combination of benchtop prototype testing and analytic techniques, we derive control requirements for ammonia. Further, we demonstrate the dehumidification performance of our proposed partial gravity condensing heat exchanger. Ammonia production is of particular concern in rodent habitats. The contaminant is released following chemical degradation of liquid waste products. The rate of production is linked to humidity levels and to the design of habitat modules in terms of bedding substrate, air flow rates, choice of structural materials, and other complex factors. Ammonia buildup can rapidly lead to rodent health concerns and can negatively impact scientific return.

This paper describes the process of incorporation of 25% post-industrial recycled sheet molded composite (SMC) material in the 1998 Continental Hood inner. 1998 Continental Hood assembly consists of traditional SMC outer and this recycled hood inner along with three small steel reinforcements. BUDD Plastics collects SMC scraps from their manufacturing plants. The scrap is then processed and made into fillers for production of SMC. Strength of SMC comes from glass fibers and fillers are added to produce the final mix of raw materials. This recycled material is approximately 10% lighter and less stiff than the conventional virgin SMC. This presented unique challenges to the product development team to incorporate this material into a production vehicle in order to obtain the desired goal of reducing land fill and improving the environment.

Cross-section properties and joint stiffness properties of the body structure define its characteristic behavior. During the transitional product development process, body structure joints are optimized on an individual basis to reduce cost and weight. The objective of this paper is to present a methodology to analyze the entire body structure design by optimizing each body joint for stiffness and cost. This methodology utilizes joint sensitivity data from FEA, section properties, and cost/weight data. When the joint stiffness status does not meet the target during the design process, the methodology is an effective tool in making decisions regarding the gage increase/decrease for each part constituting body structure joints. Additionally, the methodology has been applied to body structure joints and door upper frame separately.

A correlation of aerodynamic wind tunnels was initiated between Chrysler, Ford and General Motors under the umbrella of the United States Council for Automotive Research (USCAR). The wind tunnels used in this correlation were the open jet tunnel at Chrysler's Aero Acoustic Wind Tunnel (AAWT), the open jet tunnel at the Jacobs Drivability Test Facility (DTF) that Ford uses, and the closed jet tunnel at General Motors Aerodynamics Laboratory (GMAL). Initially, existing non-competitive aerodynamic data was compared to determine the feasibility of facility correlation. Once feasibility was established, a series of standardized tests with six vehicles were conducted at the three wind tunnels. The size and body styles of the six vehicles were selected to cover the spectrum of production vehicles produced by the three companies. All vehicles were tested at EPA loading conditions. Despite the significant differences between the three facilities, the correlation results were very good.

Computer Aided Engineering (CAE) has become a vital tool for product development in automotive industry. Various computer models for occupant restraint systems are developed. The models simulate the vehicle interior, restraint system, and occupants in different crash scenarios. In order to improve the efficiency during the product development process, the model quality and its predictive capabilities must be ensured. In this research, an objective model validation metric is developed to evaluate the model validity and its predictive capabilities when multiple occupant injury responses are simultaneously compared with test curves. This validation metric is based on the probabilistic principal component analysis method and Bayesian statistics approach for multivariate model assessment. It first quantifies the uncertainties in both test and simulation results, extracts key features, and then evaluates the model quality.

During the Product Development Process, the experimental engineers try to acquire the most reliable data from Proving Grounds early on the development process, aiming to support CAE model correlation and in this way ensuring that the vehicle is capable of withstanding customer loads. Those data, from Proving Grounds, are correlated to the most severe customer's usage and public road conditions. The proposal of this paper is to analyze how tire pressure affects structural durability, since safety, performance and fuel economy were already discussed on other opportunities. Tire pressure is important because it's one variable where the customer can monitor and act and because TPMS (Tire Pressure Measurement System) is not available on most vehicles sold in the Brazilian Market,

Product platform strategies have advantages and disadvantages. Key automotive platform issues include economies of scale and potential loss of brand differentiation. Platform strategies can be defined for components/systems, manufacturing, product development teams, and company capability/knowledge. Concepts and tools that support platform definition and commonality include function structures, modularity matrices, product architecting and decomposition, and optimization methods. Managerial concerns include organizational dynamics, strategy, technology and cost, and support systems. These issues are reviewed and discussed. The paper concludes with a summary, observations, and some questions about platform strategies.