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In applications of the Energy Finite Element Analysis (EFEA) there is an increasing need for developing comprehensive models with a large number of elements which include both structural and interior fluid elements, while certain parts of the structure are considered to be exposed to an external fluid loading. In order to accommodate efficient computations when using simulation models with a large number of elements, joints, and domains, a substructuring computational capability has been developed. The new algorithm is based on dividing the EFEA model into substructures with internal and interface degrees of freedom. The system of equations for each substructure is assembled and solved separately and the information is condensed to the interface degrees of freedom. The condensed systems of equations from each substructure are assembled in a reduced global system of equations. Once the global system of equations has been solved the solution for each substructure is pursued.

In order to develop confidence in numerical models which are used for automotive crash simulations, results are compared with test data. Modeling assumptions are made when constructing a simulation model for a complex system, such as a vehicle. Through a thorough understanding of the modeling assumptions an appropriate set of variables can be selected and adjusted in order to improve correlation with test data. Such a process can lead to better modeling practices when constructing a simulation model. Comparisons between the time history of acceleration responses from test and simulations are the most challenging. Computing accelerations correctly is more difficult compared to computing displacements, velocities, or intrusion levels due to the second order differentiation with time. In this paper a methodology for enabling the update of a simulation model for improved correlation is presented.

The Energy Boundary Element Analysis (EBEA) has been utilized in the past for computing the exterior acoustic field at high frequencies (above ∼400Hz) around vehicle structures and numerical results have been compared successfully to test data [1, 2 and 3]. The Energy Finite Element Analysis (EFEA) has been developed for computing the structural vibration of complex structures at high frequencies and validations have been presented in previous publications [4, 5]. In this paper the EBEA is utilized for computing the acoustic field around a vehicle structure due to external acoustic noise sources. The computed exterior acoustic field comprises the excitation for the EFEA analysis. Appropriate loading functions have been developed for representing the exterior acoustic loading in the EFEA simulations, and a formulation has been developed for considering the acoustic treatment applied on the interior side of structural panels.

This paper highlights the application of design optimization in reducing product manufacturing cost without compromising product performance. By using a topology optimization method, the manufacturing cost of a clam shell has been reduced by approximately one-third, while maintaining the NVH performance of the steering column that is connected to the instrument panel (IP) through the clam shell. Two different optimization approaches and two different topological weld deployments are investigated. It is found that a fully-deployed seam weld approach with automatic optimization provides the best design results.

A formulation that accounts for manufacturing variability in the analysis of structural/acoustic systems is presented. The methodology incorporates the concept of fast probability integration with finite element (FEA) and boundary element analysis (BEA) for producing the probabilistic acoustic response of a structural/acoustic system. The advanced mean value method is used for integrating the system probability density function. FEA and BEA are combined for producing the acoustic response that constitutes the performance function. The probabilistic acoustic response is calculated in terms of a cumulative distribution function. The new methodology is used to illustrate the difference between the results from a probabilistic analysis that accounts for manufacturing uncertainty, and an equivalent deterministic simulation through applications. The probabilistic computations are validated by comparison to Monte Carlo simulations.

The Indirect Boundary Element Analysis is employed for developing a computational pass-by noise simulation capability. An inverse analysis algorithm is developed in order to generate the definition of the main noise sources in the numerical model. The individual source models are combined for developing a system model for pass-by noise simulation. The developed numerical techniques are validated through comparison between numerical results and test data for component level and system level analyses. Specifically, the source definition capability is validated by comparing the actual and the computationally reconstructed acoustic field for an engine intake manifold. The overall pass-by noise simulation capability is validated by computing the maximum overall sound pressure level for a vehicle under two separate driving conditions.

The Energy Finite Element Analysis(EFEA) is a recent development for high frequency vibro-acoustic analysis, and constitutes an evolution in the area of high frequency computations. The EFEA is a wave based approach, while the SEA is a modal based approach. In this paper the similarities in the theoretical development of the two methods are outlined. The main scope of this paper is to establish the validity of the EFEA by analyzing several complex structural-acoustic systems. The EFEA solutions are compared successfully to SEA results and to solutions obtained from extremely dense conventional FEA models.

Visteon has developed a CAE procedure to qualify instrument panel (IP) products under the vehicle key life test environments, by employing a set of CAE simulation and durability techniques. The virtual key life test method simulates the same structural configuration and the proving ground road loads as in the physical test. A representative dynamic road load profile model is constructed based on the vehicle proving ground field data. The dynamic stress simulation is realized by employing the finite element transient analysis. The durability evaluation is based on the dynamic stress results and the material fatigue properties of each component. The procedure has helped the IP engineering team to identify and correct potential durability problems at earlier design stage without a prototype. It has shown that the CAE virtual key life test procedure provides a way to speed up IP product development, to minimize prototypes and costs.

GENPAD® is a knowledge-based, three-dimensional modeling computer tool developed by Visteon to create occupant-friendly interiors. GENPAD quickly and easily produces zones to evaluate ergonomic aspects of vehicle interiors such as reach, clearance, vision, and reflection. These zones are produced from automated design studies based on experience and engineering standards accepted by the automotive industry. Without GENPAD, a single study requires an experienced engineer 4-6 hours to complete. Multiple studies require several engineers weeks to perform. The methods used are also error-prone due to complex instructions. To overcome these challenges, GENPAD provides over 50 ergonomic packaging studies that produce accurate results in minutes, not weeks, every time.

Occupant knee impact simulations are performed in the automotive industry as an integrated design process during the course of instrument panel (IP) development. All major automakers have different categories of dynamic testing methods as part of their design process in validating their designs against the FMVSS 208 requirement. This has given rise to a corresponding number of knee impact simulations performed at various stages of product development. This paper investigates the advantages and disadvantages of various types of these knee impact simulations. Only the knee load requirement portion of the FMVSS208 is considered in this paper.

A full-system, end-to-end blast modeling and simulation of vehicle underbody buried blast events typically includes detailed modeling of soil, high explosive (HE) charge and air. The complex computations involved in these simulations take days to just capture the initial 50-millisecond blast-off phase, and in some cases, even weeks. The single most intricate step in the buried blast event simulation is in the modeling of the explosive loading on the underbody structure from the blast products; it is also one of the most computationally expensive steps of the simulation. Therefore, there is significant interest in the modeling and simulation community to develop various methodologies for fast running tools to run full simulation events in quicker turnarounds of time.

The ability to quickly design new vehicles with optimal crashworthiness has long been a goal of automotive manufacturers and Tier 1 suppliers alike. This paper takes steps towards that goal by automating manual design iterations. The crashworthiness of an instrument panel was optimized using LS-OPT. In one design experiment, optimizing the gauges of non-styled parts in the instrument panel reduced the simulated force in a Bendix test setup by around 30%. In a second design experiment, optimizing the shape of non-styled parts in the instrument panel with a parametric preprocessor enhanced the simulated crashworthiness by around 20%. In a third design experiment, the design space was increased and an additional 7% improvement in simulated crashworthiness was found. The designs were generated several times faster and were less expensive to evaluate than with previous manual methods.

A myriad of topology optimization tools exist today in the market that use automated under-the-hood structural simulations. All the user needs is to provide is the current shape of the part, or the maximum space that the part is allowed to occupy, and the maximum loads that it will experience. Though this technology has existed for over 25 years, recent advances in Additive Manufacturing (AM) have now enabled fabrication of hitherto-infeasible parts, both quickly and inexpensively. A quick cursory literature search on successful implementation of topology optimization reveals that a majority of the attention has been focused on structural components and assemblies subjected to known service load(s) [1,2,3]. Therein lies one of the disadvantages experienced in the state-of-the-art today, especially for the military industry.