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In recent truck applications, single-piece large-diameter propshafts, in lieu of two-piece propshafts, have become more prevalent to reduce cost and mass. These large-diameter props, however, amplify driveline radiated noise. The challenge presented is to optimize prop shaft modal tuning to achieve acceptable radiated noise levels. Historically, CAE methods and capabilities have not been able to accurately predict propshaft airborne noise making it impossible to cascade subsystem noise requirements needed to achieve desired vehicle level performance. As a result, late and costly changes can be needed to make a given vehicle commercially acceptable for N&V performance prior to launch. This paper will cover the development of a two-step CAE method to predict modal characteristics and airborne noise sensitivities of large-diameter single piece aluminum propshafts fitted with different liner treatments.

This paper describes the validation of RAMSIS, a 3-D CAD human model for ergonomic vehicle evaluation. At GM NAO, the model’s capability to correctly predict position and posture in vehicle CAD environments was tested. H- and Eye point locations between RAMSIS manikins and their human counterparts were compared. At GM/SAAB the model’s postural discomfort predictability was evaluated. Changes in postural discomfort predictions of the RAMSIS manikins were compared to that of the human subjects when they evaluated two different driving buck conditions. We concluded that RAMSIS has good position, posture and postural discomfort prediction capabilities and is a useful CAD ergonomic evaluation and design tool for vehicle interiors.

Standard CFD methods require a mesh that fits the boundaries of the computational domain. For a complex geometry the generation of such a grid is time-consuming and often requires modifications to the model geometry. This paper evaluates the Immersed Boundary (IB) approach which does not require a boundary-conforming mesh and thus would speed up the process of the grid generation. In the IB approach the CAD surfaces (in Stereo Lithography -STL- format) are used directly and this eliminates the surface meshing phase and also mitigates the process of the CAD cleanup. A volume mesh, consisting of regular, locally refined, hexahedrals is generated in the computational domain, including inside the body. The cells are then classified as fluid, solid and interface cells using a simple ray-tracing scheme. Interface cells, correspond to regions that are partially fluid and are intersected by the boundary surfaces.

This paper describes the design, synthesis-analysis and development of the unique vehicle structure architecture for the fifth generation Chevrolet Corvette, ‘C5’, which starts in the 1997 model year. The innovative structural layout of the ‘C5’ enables torsional rigidity in an open roof vehicle which exceeds that of all current production open roof vehicles by a wide margin. The first structural mode of the ‘C5’ in open roof configuration approaches typical values measured in similar size fixed roof vehicles. Extensive use of CAE and a systems methodology of benchmarking and requirements rolldown were employed to develop the ‘C5’ vehicle architecture. Simple computer models coupled with numerical optimization were used early in the design process to evaluate every design concept and alternative iteration for mass and structural efficiency.

This paper describes the validation of RAMSIS, a 3-D CAD human model for ergonomic vehicle evaluation at General Motors (GM). The model’s capability to correctly predict position and posture in vehicle CAD environments was tested. H- and Eye point locations between RAMSIS manikins and their human counterparts were compared. We concluded that RAMSIS has good position and posture prediction capabilities and is a useful CAD ergonomic evaluation and design tool for vehicle interiors.

Robust optimization usually requires numerous functional evaluations, which is not feasible when the functional evaluation is time-consuming. Examples in automobile industry include crash worthiness/safety and fatigue life simulations. In practice, a response surface model (RSM) is often used as a surrogate to the CAE model, so that robust optimization can be carried out. However, if the error in the RSM is significant, the solution based on the RSM can be invalid. This paper proposes a method of finding multiple candidate solutions, all of which have similar predicted performances. This approach is effective in finding the close-to-optimum solutions when the model has error, and providing design alternatives. Examples are provided to illustrate the method.

Since 1997, General Motors Body Manufacturing Engineering - Die Engineering Services (BME-DES) has been working jointly with our software vendor to develop and implement a parallel version of stamping simulation software for mass production analysis applications. The evolution of this technology and the insight gained through the implementation of DMP/MPP technology as well as performance benchmarks are discussed in this publication.

Ride comfort is a critical factor to customer perception of vehicle quality as it is related to vehicle experience when driving. It adds value to the product and, consequently, to vehicle brand. It has become a demand not only for passenger unibody vehicles but also to larger segments including mid-size trucks. Ride quality is usually quantified as harshness which is a measure of how the vehicle transmits the road irregularities to the customer at the tactile points such as the steering wheel and seats. Improving harshness requires tuning of different parts including tires, chassis frame/subframe and suspension mounts and bushings. This paper describes the methodology to enhance the harshness performance for a mid-size truck using a full vehicle CAE model. The influence of stiffnesses of body mounts and control arms bushings to harshness response is investigated through sensitivity analysis and the optimal configuration is found.

Free form fabrication, or rapid prototyping as it is commonly known, is the creation of a physical entity, directly from numerical description, using an additive process. The mathematical data used is typically in the form of a 3D CAD file, but it may also be obtained from a reverse engineering process. This paper presents a review of three of the leading FFF (free form fabrication) systems which are commercially available. Time constraints will allow us to describe only three of these products. Although this does not do justice to a technology where there are more than 30 different systems in various stages of development, these examples represent the vast majority of machines which are in the marketplace today.

As the computer efficiency and capability increase, so as the Computer Aided Engineering (CAE) technologies improve. Recently Robust Design or Reliability Based Design Optimization (RBDO) technologies have been utilized in all sorts of industries including automotive. The process generally involves identifying key input design variables and key performance output variables, determining a sampling plan for CAE simulations, building a response surface model (RSM), analyzing the results, and finding the optimized design that meets the reliability criteria. Yet little was addressed on the robustness of a CAE design model in the process. A systematic method of defining Robustness in a CAE design model was developed. How robust a CAE model is and how far away an optimized design is from the More Robust Region (MRR) are addressed in this paper.

Recent advancements in simulation software and computational hardware make it realizable to simulate a full vehicle system comprised of multiple sub-models developed in different modeling languages. The so-called, co-simulation allows one to develop a control strategy and evaluate various aspects of a vehicle system, such as fuel efficiency and vehicle drivability, in a cost-effective manner. In order to study the feasibility of the synchronized parallel processing in co-simulation this paper presents two co-simulation frameworks for a complete vehicle system with multiple heterogeneous subsystem models. In the first approach, subsystem models are co-simulated in a serial configuration, and the same sub-models are co-simulated in a parallel configuration in the second approach.

When using a math model to estimate the failure rate of a product, or the mean and standard deviation of performance characteristics of the product, one important issue is the accuracy of the estimates. All math models have error. This error will be transmitted to the error in the estimates of failure rate, mean, and standard deviation. This paper presents a method to calculate the bounds on the transmitted error, which can then be used to 1) obtain confidence bounds on estimates of mean, standard deviation, and failure rate; and 2) establish accuracy requirements on math models.

As one of many pack-level battery simulation approaches developed within the General Motors-led Computer-Aided Engineering of Automotive Batteries (CAEBAT) Phase 1 project, the system approach treats the entire battery pack as a dynamic system which includes multiple engineering disciplines for simulation. It is the most efficient approach of all the CAEBAT battery pack-level approaches in terms of computational time and resources. This paper reports the application of the system approach for a 24-cell liquid-cooled prototype battery pack. It also summarizes the verification of the approach by comparing the simulation results with the measurement data. The results using the system approach are found to have a very good agreement with the measurements.

An integrated stochastic design framework that facilitates practical applications involving time-consuming CAE simulations is described. The probabilistic performance measure that addresses stochastic uncertainties in CAE modeling and simulations is used to support design decision-making. Two enabling metamodeling methods using cross-validated radial basis functions (CVRBF) and a corresponding uniform sampling method are introduced to approximate highly nonlinear CAE model input/output relationships. A vehicle restraint system example is used to demonstrate the effectiveness of the proposed framework and enabling techniques.

The Alliance of Automotive Manufacturers (Alliance) has produced a document in which Principle 1.4 gives criteria and methods for calculating downvision angles to navigation and telematics displays in vehicles. This paper describes the details of the criteria and methods for determining compliance. Visual displays placed high in the vehicle instrument panel help drivers to use their peripheral vision to monitor the roadway for major developments, even during brief glances to the display. The Alliance has developed two criteria to define the maximum allowable downward viewing angle for displayed information in North American vehicles. One criterion is for use in two-dimensional Computer Aided Design (CAD) analyses, and one is for use in three-dimensional CAD analyses. Alliance Principle 1.4 is consistent with known driver performance research data, and known facts about the peripheral sensitivity of the human visual system.

Fast-running metamodels (surrogates or response surfaces) that approximate multivariate input/output relationships of time-consuming CAE simulations facilitate effective design trade-offs and optimizations in the vehicle development process. While the cross-validated nonparametric metamodeling methods are capable of capturing the highly nonlinear input/output relationships, it is crucial to ensure the adequacy of the metamodel error estimates. Moreover, in order to circumvent the so-called curse-of-dimensionality in constructing any nonlinear multivariate metamodels from a realistic number of expensive simulations, it is necessary to reliably eliminate insignificant inputs and consequently reduce the metamodel prediction error by focusing on major contributors. This paper presents a robust data-adaptive nonparametric metamodeling procedure that combines a convergent variable screening process with a robust 2-level error assessment strategy to achieve better metamodel accuracy.