Construction of surrogate models for predicting the buckling strength of stiffened panels through DoE and RSM methods. Issue 4 (14th October 2021)

• Record Type: Journal Article
• Authors: Papanikolaou, Nikolaos; Anyfantis, Konstantinos
• Is Part Of: Engineering computations. Volume 39:Issue 4(2022)
• Page Start: 1374
• Page End: 1406
• Publication Date: 2021-10-14
• Subjects: Finite element analysis -- Surrogate modeling -- Buckling strength -- Ship structures -- Stiffened panels; Computer-aided engineering -- Periodicals; Computer graphics -- Periodicals; 620.00285
• DOI: 10.1108/EC-03-2021-0176
• Languages: English
• ISSNs: 0264-4401

Abstract

Abstract : Purpose: Experimental mid/large scale testing of ship-like stiffened panels in compression is a quite expensive exercise that is not standard. Numerical simulations are preferred instead. Because of being relatively inexpensive (cost and time wise), most authors perform an exhaustive design space exploration arriving at a significant number of runs. This work demonstrates that the buckling response with respect to the nondimensional slenderness ratios may well be fitted with nine runs per stiffener geometry. Design/methodology/approach: Efficient derivation of buckling strength formulas for stiffened panels through the employment of design of experiments (DoE) and response surface methodology (RSM) combined with numerical nonlinear experimentation over the entire range of practical geometries. Findings: The surrogate model developed for T-bar stiffeners predicts accurately enough the ultimate stress in the practical design area, while the surrogate models for angle bars and flat bars demonstrate difference between 10 and 30% from common structural rules (CSR). Originality/value: To the authors' best knowledge, the statistical-based formal and rigorous approach of DoE and RSM to obtaining buckling surfaces for stiffened panels is performed for the first time. The number of required observations per stiffener type has not been addressed yet as each work selects its own sampling scheme without formal reasoning. This work comes to frame the number of observations for efficient surrogate model building.