Search Results

While CAE methods allow improving nominal product design using virtual prototypes, uncertainty and variability in properties and manufacturing processes lead to scatter in actual performances. Uncertainty must hence be incorporated in the CAE process to guarantee the robustness and reliability of the design. This paper presents an overview of uncertainty-based design in automotive and aerospace engineering. Fuzzy methods take uncertainty into account, whereas reliability analysis and a reliability-based design optimization framework can deal with variability. Key enabling technologies to alleviate the computational burden, such as workflow automation, substructuring and design of experiments, are discussed, and industrial applications are presented.

Acoustic performance of vehicle engines is a real challenge for powertrain design engineers. Quiet engines are required to reduce noise pollution and satisfy pass-by noise regulations, but also to improve the driving comfort. Simulation techniques such as the Boundary Element Method (BEM) have already been available for some time and allow predicting the vibro-acoustic response of engines. Although the accuracy of these simulation techniques has been proven, a challenge still remains in the required computation time. Given the large amount of speeds for a full engine run-up and the need to cover a large frequency range, computation times are significant, which limits the possibility to perform many design iterations to optimize the system. In 2001, Acoustic Transfer Vectors (ATV) [1] have been presented to adequately deal with multiple rpm. The ATV provide the acoustic response for unit surface velocities and are therefore independent from the engine's actual surface vibrations.

This paper discusses the requirement for CAE methods to properly take into account the variabilities and uncertainties that characterizes design input properties without leading to oversized structures. Optimizing the structural behaviour while taking into account expected variability and uncertainty in the structure and its model, requires the adoption of a reliability-based design optimization approach. This paper starts with an overview of the problem of simulation uncertainty. The key focus is then on the description of the most commonly used methods and enabling tools for reliability analysis and reliability based design optimization. The theory is illustrated by real automotive design problems.

A key element to bring research advances on intelligent materials to industrial use is that the product CAE models must support such solutions. This involves modeling capabilities for intelligent material systems, sensor and actuator components, control systems as well as their integration in system-level application designs. The final result will then be a multi-attribute optimization approach integrating noise and vibration performance with reliability, durability and cost aspects. As no single integrated solution will fulfill all requirements of the various material and control approaches, the focus of the research is on the use, combination and extension of existing codes and tools.

Vibro-acoustical optimization of vehicles is a complex task, due to the many interactions that exist between subcomponents and car body in a broad acoustical frequency range. The goal of this paper is to present a view on the different experimental methodologies for vibro-acoustical analysis, that approach the vehicle as a source, transfer and receiver system. This approach focuses on the use of transfer path and source identification techniques, both for structure-borne and air-borne contribution analysis, and on the use of modeling techniques as there are vibro-acoustical modal analysis, FRF based substructuring and experimental statistical energy analysis techniques. It is explained what the main focus is of each of the techniques, where they can be used in the vibro-acoustical optimization process and in which frequency ranges they are useful.

One of the scientific fields where, for already more than 20 years, system identification plays a crucial role is this of structural dynamics and vibro-acoustic system optimization. The experimental approach is based on the “Modal Analysis” concept. The present paper reviews the test procedure and system identification principles of this approach. The main focus though is on the real problems with which engineers, performing modal analysis on complex structures on a daily basis, are currently confronted. The added value of several new testing approaches (laser methods, smart transducers…) and identification algorithms (spatial domain, subspace, maximum likelihood,..) for solving these problems is shown. The discussed elements are illustrated with a number of industrial case studies.