An effective optimisation method for multifactor and reliability-related structural design problems

This thesis first presents a systematic design procedure which satisfies the required strength and stiffness, and structural mass for conceptual engineering structural designs. The procedure employs a multi-objective and multi-disciplinary (MO–MD) optimisation method (multifactor optimisation of structure techniques, MOST) which is coupled with finite element analysis (FEA) as an analysis tool for seeking the optimum design. The effectiveness of the MOST technique is demonstrated in two case studies.Next, a reliability-related multi-factor optimisation method is proposed and developed, representing a combination of MOST (as a method of multi-factor optimisation) and the reliability-loading case index (RLI) (as a method of calculating the reliability index). The RLI is developed based on a well-known reliability method: the first-order reliability method (FORM). The effectiveness and robustness of the proposed methodology are demonstrated in two case studies in which the method is used to simultaneously consider multi-objective, multi-disciplinary, and multi-loading-case problems. The optimised designs meet the targeted performance criteria under various loading conditions.The results show that the attributes of the proposed optimisation methods can be used to address engineering design problems which require simultaneous consideration of multi-disciplinary problems. An important contribution of this study is the development of a conceptual MO–MD design optimisation method, in which multi-factor structural and reliability design problems can be simultaneously considered.

Name dc:type.qualificationname

PhD

Level dc:type.qualificationlevel

Doctoral

Grantor dc:publisher.institution

University of Hull

Year dc:date.issued

2011

Author dc:creator

• Thein, Chung Ket

Advisor dc:contributor.advisor

• Liu, Jing-Sheng

• Engineering

Language dc:language

en

Identifier

oai:hull-repository.worktribe.com:4211936

OAI identifier oai:identifier

oai:hull-repository.worktribe.com:4211936