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Energy efficiency of thermal power plants depends on the vital plant elements' reliability levels.
However, the scalability (responsible for hundreds of data plane elements), reliability (what if a controller in failure), security (executing the network functions in this virtualized multi-tenant environment) and consistency (ensure consistency between controllers) issues should be taken into account.
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Durability of natural-draught cooling towers is investigated using finite element reliability analysis.
The objective is to determine the suitability of the algorithms for application to linear and nonlinear finite element reliability problems.
The approach is applied to analytical and finite element reliability problems and proves efficient up to 100 basic random variables.
Since OpenSees is used for finite element reliability analysis, this application is readily extendable to nonlinear structures.
This method employs GA as an optimization technique in which reliability constraints of RBO problem are evaluated using finite element reliability analysis modules offered by OpenSees.
A reinforced concrete frame is modeled for finite element sensitivity analysis by (Alam and Dookie 2012) followed by finite element reliability analysis under both static and dynamic load cases.
To support finite element reliability applications, analytical response sensitivities are derived with respect to uncertain material properties, girder dimensions, reinforcing details, and moving loads by the direct differentiation method (DDM).
In solving this problem in the context of non-linear finite element reliability analysis, two serious impediments are encountered: (a) for certain material models, the constraint function may have a discontinuous gradient, leading to failure of the search algorithm to converge.
When a system is composed of highly reliable elements, exact reliability quantification may be problematic, because computer accuracy is limited.
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