Experience with approximate reliability-based optimization methods

2004 ◽  
Vol 26 (1-2) ◽  
pp. 152-159 ◽  
Author(s):  
R.J. Yang ◽  
L. Gu
Keyword(s):  
Optimization Methods ◽  
Reliability Based Optimization

Reliability Based Design Optimization for Selective Excitation of the Vibration Modes of a Cantilever Spring

2012 ◽  
Vol 498 ◽  
pp. 102-114
Author(s):  
Khalil El-Hami ◽  
Abdelkhalak El Hami
Keyword(s):  
Cantilever Beam ◽  
Optimization Problems ◽  
Optimization Methods ◽  
Design Parameters ◽  
Optimization Strategy ◽  
High Dimensions ◽  
Engineering Structures ◽  
Reliability Based Design ◽  
Piezoelectric Polymer ◽  
Reliability Based Optimization

This paper is devoted to procedures for the reliability-based optimization methods of engineering structures combining measurement and sensitivity technique, for the purpose of the better sensitivity in force-gradient detection. In the experiment part of this study, the mica muscovite cantilever beam clamped-free is used. The excitation of a cantilever beam with several small sheets of piezoelectric polymer adequately glued to it selects one high-frequency vibration mode of the cantilever. The proposed strategy is design into a framework that allows the solution of optimization problems involving a several number of design parameters that characterizes the systems, including dimensional tolerance, material properties, boundary conditions, loads, and model predictions, considered to be uncertainties or variables. The proposed methodology directly supports quality engineering aspects enabling to specify the manufacturing tolerances normally required to achieve desired product reliability. Within this context, the robust design obtained is optimal over the range of variable conditions because it considers uncertainties during the optimization process. The large number of exact evaluations of problem, combined with the typically high dimensions of FE models of industrial structures, makes reliability-based optimization procedures very costly, sometimes unfeasible. Those difficulties motivate the study reported in this paper, in which a strategy is proposed consisting in the use of reliability-based optimization strategy combined with measurement and sensitivity technique specially adapted to the structures of industrial interested.

scholarly journals Reliability-based design optimization of offshore wind turbine support structures using analytical sensitivities and factorized uncertainty modeling

2020 ◽  
Vol 5 (1) ◽  
pp. 171-198 ◽  
Author(s):  
Lars Einar S. Stieng ◽  
Michael Muskulus
Keyword(s):  
Design Optimization ◽  
Optimization Methods ◽  
Offshore Wind ◽  
Offshore Wind Turbine ◽  
High Fidelity ◽  
Reliability Based Design Optimization ◽  
Support Structure ◽  
Reliability Based Design ◽  
Gradient Based ◽  
Reliability Based Optimization

Abstract. The need for cost-effective support structure designs for offshore wind turbines has led to continued interest in the development of design optimization methods. So far, almost no studies have considered the effect of uncertainty, and hence probabilistic constraints, on the support structure design optimization problem. In this work, we present a general methodology that implements recent developments in gradient-based design optimization, in particular the use of analytical gradients, within the context of reliability-based design optimization methods. Gradient-based optimization is typically more efficient and has more well-defined convergence properties than gradient-free methods, making this the preferred paradigm for reliability-based optimization where possible. By an assumed factorization of the uncertain response into a design-independent, probabilistic part and a design-dependent but completely deterministic part, it is possible to computationally decouple the reliability analysis from the design optimization. Furthermore, this decoupling makes no further assumption about the functional nature of the stochastic response, meaning that high-fidelity surrogate modeling through Gaussian process regression of the probabilistic part can be performed while using analytical gradient-based methods for the design optimization. We apply this methodology to several different cases based around a uniform cantilever beam and the OC3 Monopile and different loading and constraint scenarios. The results demonstrate the viability of the approach in terms of obtaining reliable, optimal support structure designs and furthermore show that in practice only a limited amount of additional computational effort is required compared to deterministic design optimization. While there are some limitations in the applied cases, and some further refinement might be necessary for applications to high-fidelity design scenarios, the demonstrated capabilities of the proposed methodology show that efficient reliability-based optimization for offshore wind turbine support structures is feasible.

Stochastic Approximation Methods in Optimal Structural Design: The Use of Intermediate Variables

1996 ◽  
Author(s):  
Kurt Marti
Keyword(s):  
Stochastic Approximation ◽  
Structural Design ◽  
Optimization Problem ◽  
Optimization Methods ◽  
Approximation Methods ◽  
Optimal Structural Design ◽  
Stochastic Optimization Problem ◽  
Reliability Based Optimization ◽  
Intermediate Variables ◽  
Intervening Variables

Abstract Reliability-based optimization methods in optimal structural design use mostly the following basic design criteria: I) Minimal weight (volume or costs) and II) high or reliability of the structure. Since, in practice, several parameters of the structure, e.g. elastic moduli, tolerances of structural dimensions, loads, are not given, fixed quantities, but random variables having a certain probability distribution P, a stochastic optimization problem will result from criteria (I), (II), which can be represented min F(x) with F(x) := Ef(ω, x). (1) x∇D Stochastic approximation methods are considered for solving (1): The gradient estimators are obtained by the response surface methodology (RSM) where especially the improvement of the estimators by using so-called “intermediate” or “intervening” variables is considered.

Optimization Methods in Finance

2018 ◽  
Author(s):  
Gérard Cornuéjols ◽  
Javier Peña ◽  
Reha Tütüncü
Keyword(s):  
Optimization Methods

Optimization Methods in Finance

2006 ◽  
Author(s):  
Gerard Cornuejols ◽  
Reha Tutuncu
Keyword(s):  
Optimization Methods
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