Inhomogeneous nonlinear problems of ribbed-shell theory

1979 ◽  
Vol 15 (9) ◽  
pp. 808-813 ◽  
Author(s):  
G. D. Gavrilenko
Keyword(s):  
Shell Theory ◽  
Nonlinear Problems ◽  
Ribbed Shell

Nonlinear problems of shell theory and their solution methods (review)

1991 ◽  
Vol 27 (10) ◽  
pp. 929-947 ◽  
Author(s):  
Ya. M. Grigorenko ◽  
V. I. Gulyaev
Keyword(s):  
Shell Theory ◽  
Nonlinear Problems ◽  
Solution Methods

On the error of a ribbed shell theory based on the Kirchhoff ? Love hypotheses

1970 ◽  
Vol 6 (6) ◽  
pp. 609-615
Author(s):  
A. P. Varvak ◽  
V. A. Zarutskii
Keyword(s):  
Shell Theory ◽  
Ribbed Shell

Analysis of Spectral Characteristics of Sound Waves Scattered from a Cracked Cylindrical Elastic Shell Filled with a Viscous Fluid

2013 ◽  
Vol 38 (3) ◽  
pp. 335-350 ◽  
Author(s):  
Olexa Piddubniak ◽  
Nadia Piddubniak
Keyword(s):  
Viscous Fluid ◽  
Shell Theory ◽  
Elastic Shell ◽  
Spectral Characteristics ◽  
Steel Shell ◽  
Sound Waves ◽  
Thin Cylindrical Shell ◽  
Shell Surface ◽  
Theory Approximation ◽  
Directivity Patterns

Abstract The scattering of plane steady-state sound waves from a viscous fluid-filled thin cylindrical shell weak- ened by a long linear slit and submerged in an ideal fluid is studied. For the description of vibrations of elastic objects the Kirchhoff-Love shell-theory approximation is used. An exact solution of this problem is obtained in the form of series with cylindrical harmonics. The numerical analysis is carried out for a steel shell filled with oil and immersed in seawater. The modules and phases of the scattering amplitudes versus the dimensionless wavenumber of the incident sound wave as well as directivity patterns of the scattered field are investigated taking into consideration the orientation of the slit on the elastic shell surface. The plots obtained show a considerable influence of the slit and viscous fluid filler on the diffraction process.

A Comprehensive Investigation of Ensembles of Gaussian-Based and Gradient-Based Transformed Reliability Analyses: When and How to Use Them

2016 ◽  
Author(s):  
Po Ting Lin ◽  
Wei-Hao Lu ◽  
Shu-Ping Lin
Keyword(s):  
Design Optimization ◽  
Design Space ◽  
Optimization Problems ◽  
Nonlinear Problems ◽  
Kernel Functions ◽  
Sensitivity Analyses ◽  
Normal Space ◽  
Highly Nonlinear ◽  
Standard Normal ◽  
Gradient Based

In the past few years, researchers have begun to investigate the existence of arbitrary uncertainties in the design optimization problems. Most traditional reliability-based design optimization (RBDO) methods transform the design space to the standard normal space for reliability analysis but may not work well when the random variables are arbitrarily distributed. It is because that the transformation to the standard normal space cannot be determined or the distribution type is unknown. The methods of Ensemble of Gaussian-based Reliability Analyses (EoGRA) and Ensemble of Gradient-based Transformed Reliability Analyses (EGTRA) have been developed to estimate the joint probability density function using the ensemble of kernel functions. EoGRA performs a series of Gaussian-based kernel reliability analyses and merged them together to compute the reliability of the design point. EGTRA transforms the design space to the single-variate design space toward the constraint gradient, where the kernel reliability analyses become much less costly. In this paper, a series of comprehensive investigations were performed to study the similarities and differences between EoGRA and EGTRA. The results showed that EGTRA performs accurate and effective reliability analyses for both linear and nonlinear problems. When the constraints are highly nonlinear, EGTRA may have little problem but still can be effective in terms of starting from deterministic optimal points. On the other hands, the sensitivity analyses of EoGRA may be ineffective when the random distribution is completely inside the feasible space or infeasible space. However, EoGRA can find acceptable design points when starting from deterministic optimal points. Moreover, EoGRA is capable of delivering estimated failure probability of each constraint during the optimization processes, which may be convenient for some applications.

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