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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.

A key ingredient in designing products that are more robust is a thorough knowledge of the physics of the ideal function of those products and the physics of the failure modes of those products. We refer to the mathematical functions describing this physics as the transfer functions for that product. Dimensional analysis (DA) is a well known, but often overlooked, tool for reducing the number of experiments needed to characterize a physical system. In this paper, we demonstrate how the application of DA can be used to reduce the size of a DOE needed to estimate transfer functions experimentally. Furthermore, the transfer function generated using DOEs with DA tend to be more general than those generated using larger DOEs directly on the design parameters. With ever-increasing competitive pressure and reduced product development time, a tool such as DA, which can dramatically reduce experimental cost, is an incredibly valuable addition to an engineers toolbox.

The importance of the automotive test facility has increased significantly due in large part to continuous pressure on manufactures to shorten product development cycles. Test facilities are no longer used only for regulatory testing, or development testing in which the effects of small design changes (A-to-B testing) are determined; automotive manufacturers are beginning to use these facilities for final design validation, which has traditionally required on road testing. A host of resources have gone into the design and construction of facilities with the capability to simulate nearly any environment of practical importance to the automotive industry. As a result, there are now a number of test facilities, and specifically wind tunnels, in which engineers can test most aspects of a vehicle's performance in real-world environments.

Virtual Verification (VV) of engineering designs is a critical enabler in the Product Development (PD) process to reduce the time-to-market in a cost efficient manner. Reliance on cost effective VV methods have significantly increased with increased pressure to meet customer expectations for new products at reduced PD budgets. Computer Aided Engineering (CAE) is one such VV method that affords an engineer to make decisions about the ability of the designs to meet the design criteria even before a prototype is built. The first step of the CAE process is meshing which is a time consuming, manual and laborious process. Also mesh development time and accuracy significantly varies with the (1) component (trim body, engine, suspension, brakes, etc.), (2) features predominantly occurring in the component (welds, ribs, fillets, etc.), meshing guidelines based on which the model needs to be developed (durability, safety, NVH, etc.), and the expertise of the meshing engineer involved.

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,

Chrysler is a company run by automotive enthusiasts, and its motorsports programs are an integral part of the company's corporate, brand, and product development process. Chrysler's motorsports programs are executed from within its Platform Team system by the same engineers, using the same processes and facilities as production vehicle programs. This results in teaching and inspiring engineers, designers, and technicians, as well as providing genuine technical benefits to the company. This paper tells the “how” story of the design, build, and test of the Dodge Stratus Super Touring Car. Detailed results have been purposely omitted from the paper due to the competitive nature of motor racing.

A driving simulator development project at the Systems Engineering and Technical Process Center (SE/TP) is exploring the role of driving simulation in the vehicle design process. The simulator provides two vehicle mockup testing arenas that support a wide field of view, computer-generated image of the road scene which dynamically responds to driver commands as a function of programmable vehicle model parameters. Two unique aspects of the simulator are the fast 65 ms response time and low incidence rate of simulator induced syndrome (about 5%). Preliminary model validation results and data comparing driver performance in a vehicle vs. the simulator indicate accurate handling response dynamics within the on-center handling region (<0.3g lateral acceleration). Applications have included supporting the development of new steering system concepts, as well as evaluating the usability of vehicle controls and displays.

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).

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.

There are a multitude of dynamics faced by any industry. There is also a consistent search and development of technological platforms and services to address these changes. This necessitates a shared work philosophy which involves multiple stakeholders. Verification and validation are integral part of any development irrespective of product, process, or services. Also, every industry has a regulatory compliance to adhere too. But the extent of complexity and the level of dependencies or interactions between modules as well as stakeholders involved, creates slippage at some or other level. Nowadays the industries are also driven by reuse for cost effectiveness. Though it marks the significant improvement in the capability to compete, compatibility is a key measure to a successful product or service launch and sustainability.

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.

Simultaneous engineering methods are being used in automotive engineering. A brief explanation of the method and benefits are presented. Merging the methodology with new organizational structures provides greater advantages. A discussion of the benefits and future directions are presented.

There is a strong business case for automotive interfaces to undergo usability testing throughout their product development lifecycle. System Usability Scale (SUS) is a simple and standard measure of usability. To meet the timing needs for product development, usability testing needs to be performed in a quick, cost effective manner. Hence the required sample size of participants for a usability study is one of the critical factors. To determine an acceptable sample size, a Monte Carlo simulation using SUS scores from eleven different in-vehicle automotive interface usability studies was used to create 500,000 subsamples of different sample sizes. The percentage of subsamples with mean scores within the confidence interval of the population mean was calculated. At a subsample size of thirty-five, 95% of the subsamples have a mean SUS score within the 95% confidence interval of the population mean.

This paper describes the ride and handling development process used for the 1997 Corvette. Three levels of suspension are available for the 1997 Corvette: base (FE1), sport (FE3) and RTD or Real Time Damping (F45) suspensions. All suspensions will be discussed in this paper A review of the development and vehicle integration tradeoffs for each of the specific chassis components is included. Control arm bushings, springs, jounce bumpers, anti-roll bars and insulators, tires, shock mounts, shock absorber valving, real-time damping, steering development, alignment and measurements are discussed.

Driver assistance features are increasingly dependent upon system architectures that distribute and share responsibilities across various function-based ECUs to minimize cost and redundancy while maximizing engineering efficiency. Clear and accurate system requirements are critical to success, and a robust methodology for validating and testing requirements is essential. Distributed systems are highly sensitive to requirement ambiguity and inaccuracy as they are designed on the assumptions of predictable logical behavior of each functional component. Requirement ambiguity drives variance in implementations which results in system incompatibilities. Errors in requirements lead to faulty implementations that fail not just the component test but also hinder the testing of the entire system of components.

This paper focuses on the methodology development and application of reliability-based design optimization to a vehicle exhaust system under noise, vibration and harshness constraints with uncertainties. Reliability-based design optimization provides a systematic way for considering uncertainties in product development process. As traditional reliability analysis itself is a design optimization problem that requires many function evaluations, it often requires tremendous computational resources and efficient optimization methodologies. Multiple functional response constraints and large number of design variables add further complexity to the problem. This paper investigates an integrated approach by taking advantages of variable screening, design of experiments, response surface model, and reliability-based design optimization for problems with functional responses. A typical vehicle exhaust system is used as an example to demonstrate the methodology.

As automotive competition is becoming more and more global, automotive products (vehicles or systems or components) are often developed in one market and made and/or used in other markets. The operating conditions are often different from one market to another market. If the market unique operating conditions are not considered in its development and manufacture process, the product may not fully perform its intended function over useful life period, and may experience fewer failure modes in one market but more or different failure modes in other markets. This paper presents a Reliability Engineering approach, based on “Failure Mode Avoidance”, to the product development for global automotive markets. The approach includes three phases: Discover operating conditions (noise factors) and their potential failure modes Develop countermeasures to deal with the noise factors, and Verify the effectiveness of the countermeasures using Reliability Demonstration Matrix.

During the design conception of an automobile, typically low-fidelity physics-based simulations are coupled with engineering judgement to define key architectural components and subsystems which limits the capability to identify NVH issues arising from systems interaction. This translates to non-optimal designs because of unexplored design opportunities and therefore, lost business efficiencies. The sparse design information available during the design conception phase limits the development of representative higher fidelity physics-based simulations. To address that restriction on design optimization opportunities, this paper introduces an alternate approach to develop reduced order predictive models using regression techniques by harnessing historical measurement and simulation data. The concept is illustrated using two driveline NVH phenomenon: axle whine and take-off shudder.