Design optimization of stiffened panels using finite element integrated force method. (15th March 2018)

• Record Type: Journal Article
• Authors: Singh, A.; Yang, F.; Sedaghati, R.
• Is Part Of: Engineering structures. Volume 159(2018)
• Page Start: 99
• Page End: 109
• Publication Date: 2018-03-15
• Subjects: Stiffened panel -- Force method -- Finite element analysis -- Stability analysis; Structural engineering -- Periodicals; Structural analysis (Engineering) -- Periodicals; Construction, Technique de la -- Périodiques; Génie parasismique -- Périodiques; Pression du vent -- Périodiques; Earthquake engineering; Structural engineering; Wind-pressure; Periodicals; 624.105
• DOI: 10.1016/j.engstruct.2017.12.040
• Languages: English
• ISSNs: 0141-0296

Abstract

Highlights: Developing an integrated force method (IFM) for stiffened panels' buckling analysis. Developing analytical sensitivity formulations for stiffened panels using IFM. Formulating a design optimization procedure for stiffened panels using IFM. Abstract: The objective of the present paper is to develop an efficient and accurate design optimization procedure to minimize the mass of the stiffened panels subjected to uniform compression loading, while guarding against the buckling failure. A Finite Element (FE) model based on the Integrated Force Method (IFM) is developed to perform the buckling analysis of stiffened panels. It has been shown that the finite element analysis based on the force methodology is able to predict the critical buckling load accurately and efficiently. The Sequential Quadratic Programming (SQP) is then applied to the established IFM model to minimize the mass of stiffened panels while guarding against buckling failure. An efficient analytical formulation to perform the sensitivity analysis is formulated using the developed finite element force method, and then utilized in the SQP formulation as the gradient information. Illustrated examples have been presented to verify the validities of the proposed methodologies. It has been shown that comparing to the numerical sensitivity analysis, the design optimization using the developed analytical sensitivity formulation is very efficient and accurate.