2008
DOI: 10.1016/j.cma.2008.07.009
|View full text |Cite
|
Sign up to set email alerts
|

A posteriori initial imperfection identification in shell buckling problems

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...
2
1
1
1

Citation Types

1
11
0

Year Published

2009
2009
2017
2017

Publication Types

Select...
6
2
1

Relationship

1
8

Authors

Journals

citations
Cited by 18 publications
(12 citation statements)
references
References 17 publications
1
11
0
Order By: Relevance
“…Stull et al [45] presented a method that employs sparse sensor telemetry, acquired during a safe service loading condition, for use in the solution of an inverse problem that characterizes the actual, and previously unknown, imperfection field in a shell structure. This a posteriori determination of the shell initial imperfection field is then used to make strength predictions regarding the shell structure in question.…”
Section: Imperfect Spherical Shellmentioning
confidence: 99%
“…Stull et al [45] presented a method that employs sparse sensor telemetry, acquired during a safe service loading condition, for use in the solution of an inverse problem that characterizes the actual, and previously unknown, imperfection field in a shell structure. This a posteriori determination of the shell initial imperfection field is then used to make strength predictions regarding the shell structure in question.…”
Section: Imperfect Spherical Shellmentioning
confidence: 99%
“…In this spirit, a ''divide-and-conquer'' scheme [50] is also adopted in the present work; a schematic of this process is given in Fig. 7.…”
Section: Genetic Algorithm For Functional Optimizationmentioning
confidence: 99%
“…(10). In addition to adopting a rather intuitive solution scheme, GAs are also well-suited to problems which exhibit complicated objective functional(s): a common issue when dealing with model-based SHM formulations [50]. Such objective functionals are often of a non-convex nature [48], thus eliminating the more efficient gradient-based solvers from consideration, due to their inability to negotiate solution spaces containing multiple local minima [51,52].…”
Section: Genetic Algorithm For Functional Optimizationmentioning
confidence: 99%
“…We require a way to parametrize forms of the hull damage. A natural approach for modeling such damage is via Gaussian Radial Basis Functions (RBFs) (Stull et al, 2008). The denting damage field is thus modeled as…”
Section: Acoustic Fsi Modeling With Damagementioning
confidence: 99%