Reliability-Based Multifidelity Optimization Using Adaptive Hybrid Learning

2020 ◽  
Vol 6 (2) ◽  
Author(s):  
Mingyang Li ◽  
Zequn Wang
Keyword(s):  
Surrogate Model ◽  
Optimal Solution ◽  
Hybrid Learning ◽  
Data Sources ◽  
Optimal Designs ◽  
Sensitivity Analyses ◽  
High Fidelity ◽  
Reliability Based Design Optimization ◽  
Reliability Based Design ◽  
Multifidelity Optimization

Abstract Most of the existing reliability-based design optimization (RBDO) are not capable of analyzing data from multifidelity sources to improve the confidence of optimal solution while maintaining computational efficiency. In this paper, we propose a novel reliability-based multifidelity optimization (RBMO) framework that adaptively integrates both low- and high-fidelity data for achieving reliable optimal designs. The Gaussian process (GP) modeling technique is first utilized to build a hybrid surrogate model by fusing data sources with different fidelity levels. To reduce the number of low- and high-fidelity data, an adaptive hybrid learning (AHL) algorithm is then developed to efficiently update the hybrid model. The updated hybrid surrogate model is used for reliability and sensitivity analyses in solving an RBDO problem, which provides a pseudo-optimal solution in the RBMO framework. An optimal solution that meets the reliability targets can be achieved by sequentially performing the adaptive hybrid learning at the iterative pseudo-optimal designs and solving RBDO problems. The effectiveness of the proposed framework is demonstrated through three case studies.

scholarly journals Reliability-Based Design Optimization for Crane Metallic Structure Using ACO and AFOSM Based on China Standards

2015 ◽  
Vol 2015 ◽  
pp. 1-12 ◽  
Author(s):  
Xiaoning Fan ◽  
Xiaoheng Bi
Keyword(s):  
Design Optimization ◽  
Optimal Solution ◽  
Probabilistic Constraints ◽  
Metallic Structure ◽  
Reliability Based Design Optimization ◽  
Metallic Structures ◽  
Practical Applications ◽  
Reliability Indices ◽  
Uncertain Variables ◽  
Reliability Based Design

The design optimization of crane metallic structures is of great significance in reducing their weight and cost. Although it is known that uncertainties in the loads, geometry, dimensions, and materials of crane metallic structures are inherent and inevitable and that deterministic structural optimization can lead to an unreliable structure in practical applications, little amount of research on these factors has been reported. This paper considers a sensitivity analysis of uncertain variables and constructs a reliability-based design optimization model of an overhead traveling crane metallic structure. An advanced first-order second-moment method is used to calculate the reliability indices of probabilistic constraints at each design point. An effective ant colony optimization with a mutation local search is developed to achieve the global optimal solution. By applying our reliability-based design optimization to a realistic crane structure, we demonstrate that, compared with the practical design and the deterministic design optimization, the proposed method could find the lighter structure weight while satisfying the deterministic and probabilistic stress, deflection, and stiffness constraints and is therefore both feasible and effective.

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.

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

2019 ◽  
Author(s):  
Lars Einar S. Stieng ◽  
Michael Muskulus
Keyword(s):  
Wind Turbine ◽  
Design Optimization ◽  
Optimization Methods ◽  
Offshore Wind ◽  
Offshore Wind Turbine ◽  
High Fidelity ◽  
Reliability Based Design Optimization ◽  
Support Structure ◽  
Reliability Based Design ◽  
Gradient Based

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. 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 constraints 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.

Two-Level Approximation Method for Reliability-Based Design Optimization

2004 ◽  
Author(s):  
Kunjal Oza ◽  
Hae Chang Gea
Keyword(s):  
Design Optimization ◽  
Approximation Method ◽  
Order Approximation ◽  
Optimal Solution ◽  
Design Tool ◽  
Reliability Based Design Optimization ◽  
Reliability Based Design ◽  
Dominant Design ◽  
Practical Engineering ◽  
The Cost

In order to model uncertainties and achieve the required reliability, Reliability Based Design Optimization (RBDO) has evolved as a dominant design tool. Many methods have been introduced in solving the RBDO problem. However, the computational expense associated with the probabilistic constraint evaluation still limits the applicability of the RBDO to practical engineering problems. In this paper, a Two-Level Approximation method (TLA) is proposed. At the first level, a reduced second order approximation is used for better optimization solution; at the second level a linear approximation is used for faster reliability assessment. The optimal solution is obtained interatively. The proposed method is tested on certain numerical examples, and results obtained are compared to evaluate the cost-effectiveness.

Sensitivity Analyses of FORM-Based and DRM-Based Performance Measure Approach for Reliability-Based Design Optimization

2008 ◽  
Author(s):  
Ikjin Lee ◽  
Kyung K. Choi ◽  
Liu Du ◽  
David Gorsich
Keyword(s):  
Design Optimization ◽  
Performance Measure ◽  
Second Order ◽  
Probabilistic Constraints ◽  
Sensitivity Analyses ◽  
Probabilistic Constraint ◽  
Reliability Based Design Optimization ◽  
Performance Measure Approach ◽  
Reliability Based Design ◽  
Reliability Method

In a gradient-based design optimization, it is necessary to know sensitivities of the constraint with respect to the design variables. In a reliability-based design optimization (RBDO), the constraint is evaluated at the most probable point (MPP) and called the probabilistic constraint, thus it requires the sensitivities of the probabilistic constraints at MPP. This paper presents the rigorous analytic derivation of the sensitivities of the probabilistic constraint at MPP for both First Order Reliability Method (FORM)-based Performance Measure Approach (PMA) and Dimension Reduction Method (DRM)-based PMA. Numerical examples are used to demonstrate that the analytic sensitivities agree very well with the sensitivities obtained from the finite difference method (FDM). However, since the sensitivity calculation at the true DRM-based MPP requires the second-order derivatives and additional MPP search, the sensitivity derivation at the approximated DRM-based MPP, which does not require the second-order derivatives and additional MPP search to find the DRM-based MPP, is proposed in this paper. A convergence study illustrates that the sensitivity at the approximated DRM-based MPP converges to the sensitivity at the true DRM-based MPP as the design approaches the optimum design. Hence, the sensitivity at the approximated DRM-based MPP is proposed to be used for the DRM-based RBDO to enhance the efficiency of the optimization.

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