An Optimization Framework Based on Kriging Method with Additive Bridge Function for Variable-Fidelity Problem

2015 ◽  
Author(s):  
Peng Wang ◽  
Yang Li ◽  
Chengshan Li
Keyword(s):  
Kriging Method ◽  
Bridge Function ◽  
Optimization Framework ◽  
Variable Fidelity

scholarly journals Multi-Fidelity Optimization of a Composite Airliner Wing Subject to Structural and Aeroelastic Constraints

Aerospace ◽  
2021 ◽  
Vol 8 (12) ◽  
pp. 398
Author(s):  
Angelos Kafkas ◽  
Spyridon Kilimtzidis ◽  
Athanasios Kotzakolios ◽  
Vassilis Kostopoulos ◽  
George Lampeas
Keyword(s):  
Low Cost ◽  
Computational Cost ◽  
Design Stage ◽  
Computational Time ◽  
High Fidelity ◽  
Full Potential ◽  
Optimized Design ◽  
Structural Representation ◽  
Optimization Framework ◽  
Variable Fidelity

Efficient optimization is a prerequisite to realize the full potential of an aeronautical structure. The success of an optimization framework is predominately influenced by the ability to capture all relevant physics. Furthermore, high computational efficiency allows a greater number of runs during the design optimization process to support decision-making. The efficiency can be improved by the selection of highly optimized algorithms and by reducing the dimensionality of the optimization problem by formulating it using a finite number of significant parameters. A plethora of variable-fidelity tools, dictated by each design stage, are commonly used, ranging from costly high-fidelity to low-cost, low-fidelity methods. Unfortunately, despite rapid solution times, an optimization framework utilizing low-fidelity tools does not necessarily capture the physical problem accurately. At the same time, high-fidelity solution methods incur a very high computational cost. Aiming to bridge the gap and combine the best of both worlds, a multi-fidelity optimization framework was constructed in this research paper. In our approach, the low-fidelity modules and especially the equivalent-plate methodology structural representation, capable of drastically reducing the associated computational time, form the backbone of the optimization framework and a MIDACO optimizer is tasked with providing an initial optimized design. The higher fidelity modules are then employed to explore possible further gains in performance. The developed framework was applied to a benchmark airliner wing. As demonstrated, reasonable mass reduction was obtained for a current state of the art configuration.

Variable-fidelity shape optimization of dual-rotor wind turbines

2018 ◽  
Vol 35 (7) ◽  
pp. 2514-2542
Author(s):  
Andrew Thelen ◽  
Leifur Leifsson ◽  
Anupam Sharma ◽  
Slawomir Koziel
Keyword(s):  
Design Optimization ◽  
Wind Turbines ◽  
Computational Cost ◽  
Optimization Techniques ◽  
Fast Variable ◽  
Efficient Design ◽  
Content Type ◽  
Optimization Framework ◽  
Derivative Free ◽  
Variable Fidelity

Purpose Dual-rotor wind turbines (DRWTs) are a novel type of wind turbines that can capture more power than their single-rotor counterparts. Because their surrounding flow fields are complex, evaluating a DRWT design requires accurate predictive simulations, which incur high computational costs. Currently, there does not exist a design optimization framework for DRWTs. Since the design optimization of DRWTs requires numerous model evaluations, the purpose of this paper is to identify computationally efficient design approaches. Design/methodology/approach Several algorithms are compared for the design optimization of DRWTs. The algorithms vary widely in approaches and include a direct derivative-free method, as well as three surrogate-based optimization methods, two approximation-based approaches and one variable-fidelity approach with coarse discretization low-fidelity models. Findings The proposed variable-fidelity method required significantly lower computational cost than the derivative-free and approximation-based methods. Large computational savings come from using the time-consuming high-fidelity simulations sparingly and performing the majority of the design space search using the fast variable-fidelity models. Originality/value Due the complex simulations and the large number of designable parameters, the design of DRWTs require the use of numerical optimization algorithms. This work presents a novel and efficient design optimization framework for DRWTs using computationally intensive simulations and variable-fidelity optimization techniques.

A New Variable-Fidelity Optimization Framework Based on Model Fusion and Objective-Oriented Sequential Sampling

2008 ◽  
Vol 130 (11) ◽  
Author(s):  
Ying Xiong ◽  
Wei Chen ◽  
Kwok-Leung Tsui
Keyword(s):  
Computational Models ◽  
Sequential Sampling ◽  
Surrogate Models ◽  
High Fidelity ◽  
Design Decision ◽  
Model Fusion ◽  
Design Objective ◽  
Optimization Framework ◽  
Variable Fidelity ◽  
High Fidelity Simulations

Computational models with variable fidelity have been widely used in engineering design. To alleviate the computational burden, surrogate models are used for optimization without directly invoking expensive high-fidelity simulations. In this work, a model fusion technique based on the Bayesian–Gaussian process modeling is employed to construct cheap surrogate models to integrate information from both low-fidelity and high-fidelity models, while the interpolation uncertainty of the surrogate model due to the lack of sufficient high-fidelity simulations is quantified. In contrast to space filling, the sequential sampling of a high-fidelity simulation model in our proposed framework is objective-oriented, aiming for improving a design objective. Strategy based on periodical switching criteria is studied, which is shown to be effective in guiding the sequential sampling of a high-fidelity model toward improving a design objective as well as reducing the interpolation uncertainty. A design confidence metric is proposed as the stopping criterion to facilitate design decision making against the interpolation uncertainty. Examples are provided to illustrate the key ideas and features of model fusion, sequential sampling, and design confidence—the three key elements in the proposed variable-fidelity optimization framework.

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