Metamodel Based Multidisciplinary Design Optimization Of Automotive Structures
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Metamodel-Based Multidisciplinary Design Optimization of Automotive Structures
Author: Ann-Britt Ryberg
language: en
Publisher: Linköping University Electronic Press
Release Date: 2017-09-14
Multidisciplinary design optimization (MDO) can be used in computer aided engineering (CAE) to efficiently improve and balance performance of automotive structures. However, large-scale MDO is not yet generally integrated within automotive product development due to several challenges, of which excessive computing times is the most important one. In this thesis, a metamodel-based MDO process that fits normal company organizations and CAE-based development processes is presented. The introduction of global metamodels offers means to increase computational efficiency and distribute work without implementing complicated multi-level MDO methods. The presented MDO process is proven to be efficient for thickness optimization studies with the objective to minimize mass. It can also be used for spot weld optimization if the models are prepared correctly. A comparison of different methods reveals that topology optimization, which requires less model preparation and computational effort, is an alternative if load cases involving simulations of linear systems are judged to be of major importance. A technical challenge when performing metamodel-based design optimization is lack of accuracy for metamodels representing complex responses including discontinuities, which are common in for example crashworthiness applications. The decision boundary from a support vector machine (SVM) can be used to identify the border between different types of deformation behaviour. In this thesis, this information is used to improve the accuracy of feedforward neural network metamodels. Three different approaches are tested; to split the design space and fit separate metamodels for the different regions, to add estimated guiding samples to the fitting set along the boundary before a global metamodel is fitted, and to use a special SVM-based sequential sampling method. Substantial improvements in accuracy are observed, and it is found that implementing SVM-based sequential sampling and estimated guiding samples can result in successful optimization studies for cases where more conventional methods fail.
Developments in Reliability Engineering
Modern systems have become increasingly complex to design and build, while the demand for reliability and cost-effective enhancement continues. Robust international competition has further intensified the need for all designers, managers, practitioners, scientists, and engineers to ensure a level of reliability of their products and processes before release at the lowest cost.Developments in Reliability Engineering equips its audience with the necessary information to keep up with the latest original research and state-of-the-art advances in reliability engineering. The volume offers an excursus from historical theories and methods to the present-world practical utility of these concepts with worked-out examples. - Guides readers through reliability topics from an historical perspective to new research results, advancements, and latest developments - Draws on the authors' experience of reliability analysis in a range of industries and disciplines, showing the need for reliability from the product design stage right through to aftercare - Provides methods throughout, making this title a good source of actionable information
Bridge Optimization
Author: Yun Lai Zhou
language: en
Publisher: BoD – Books on Demand
Release Date: 2020-02-05
This is a collection of several applications for condition monitoring and damage identification in bridge structures. Bridge structural condition monitoring is essential since it can provide early warning of potential defects in bridges, which may induce catastrophic accidents and result in huge economic loss. Such bridge condition monitoring relies on sensing techniques, especially advanced sensing techniques that can provide detailed information on bridge structures. Additionally, postprocessing systems can interpret the captured data and warn of any potential faults. This book will give students a thorough understanding of bridge condition monitoring.