Variable-Fidelity Aerodynamic Shape Optimization

Leifsson, Leifur; Koziel, Slawomir

doi:10.1007/978-3-642-20859-1_9

Cited by 17 publications

(6 citation statements)

References 68 publications

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“…Because we seek to demonstrate that the presented framework is applicable to a wide variety of engineering design problems, we use MATLAB's generalized pattern search (GPS) algorithm with positive 2N basis and mesh tolerance of 0.01. Pattern search methods do not suffer from some of the problems associated with gradient-based finite differencing, such as potential oversensitivity or insensitivity to design variable variation, and can therefore be more readily used for many design applications [42,63], including problems of high dimensionality. Although this approach may not be the most efficient, it is reliable and also has rigorous local convergence properties [64].…”

Section: Simulation Setup and Solution Strategiesmentioning

confidence: 99%

A framework for parametric design optimization using isogeometric analysis

Herrema

Wiese

Darling

et al. 2017

Computer Methods in Applied Mechanics and Engineering

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Isogeometric analysis (IGA) fundamentally seeks to bridge the gap between engineering design and high-fidelity computational analysis by using spline functions as finite element bases. However, additional computational design paradigms must be taken into consideration to ensure that designers can take full advantage of IGA, especially within the context of design optimization. In this work, we propose a novel approach that employs IGA methodologies while still rigorously abiding by the paradigms of advanced design parameterization, analysis model validity, and interactivity. The entire design lifecycle utilizes a consistent geometry description and is contained within a single platform. Because of this unified workflow, iterative design optimization can be naturally integrated. The proposed methodology is demonstrated through an IGA-based parametric design optimization framework implemented using the Grasshopper algorithmic modeling interface for Rhinoceros 3D. The framework is capable of performing IGA-based design optimization of realistic engineering structures that are practically constructed through the use of complex geometric operations. We demonstrate the framework's effectiveness on both an internally pressurized tube and a wind turbine blade, highlighting its applicability across a spectrum of design complexity. In addition to inherently featuring the advantageous characteristics of IGA, the seamless nature of the workflow instantiated in this framework diminishes the obstacles traditionally encountered when performing finite-element-analysis-based design optimization.

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Section: Simulation Setup and Solution Strategiesmentioning

confidence: 99%

A framework for parametric design optimization using isogeometric analysis

Herrema

Wiese

Darling

et al. 2017

Computer Methods in Applied Mechanics and Engineering

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“…Simulation-driven optimization, in which function evaluations involve numerical simulations, plays an important role in industrial design, such as aerodynamic design optimization [1]. Such numerical simulations are usually computationally expensive, yet the computational complexity of the simulations can be tuned.…”

Section: Introductionmentioning

confidence: 99%

A Generic Test Suite for Evolutionary Multifidelity Optimization

Wang

Jin

Doherty

2018

IEEE Trans. Evol. Computat.

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Many real-world optimization problems involve computationally intensive numerical simulations to accurately evaluate the quality of solutions. Usually the fidelity of the simulations can be controlled using certain parameters and there is a trade-off between simulation fidelity and computational cost, i.e., the higher the fidelity, the more complex the simulation will be. To reduce the computational time in simulation-driven optimization, it is a common practice to use multiple fidelity levels in search for the optimal solution. So far, not much work has been done in evolutionary optimization that considers multiple fidelity levels in fitness evaluations. In this work, we aim to develop test suites that are able to capture some important characteristics in real-world multi-fidelity optimization, thereby offering a useful benchmark for developing evolutionary algorithms for multi-fidelity optimization. To demonstrate the usefulness of the proposed test suite, three strategies for adapting the fidelity level of the test problems during optimization are suggested and embedded in a particle swarm optimization algorithm. Our simulation results indicate that the use of changing fidelity is able to enhance the performance and reduce the computational cost of the particle swarm optimization, which is desired in solving expensive optimization problems.

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“…Our low-fidelity models are obtained through variable-resolution computational fluid dynamic simulations 4 . Physics-based surrogates exhibit excellent generalization capability which results in potentially dramatic reduction of the airfoil/wing design optimization cost 5,6,7 .…”

Section: Introductionmentioning

confidence: 99%

Aerodynamic Design Optimization: Physics-based Surrogate Approaches for Airfoil and Wing Design

Leifsson

Kozieł

Tesfahunegn

et al. 2014

52nd Aerospace Sciences Meeting

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The aerodynamic optimization community has recently started an effort to develop benchmark problems suitable for exercising aerodynamic optimization methods in a constrained design space. In the first round, four problems have been developed, two involving two-dimensional airfoils and the other two three-dimensional wings. In this paper, we address the two-dimensional problems which involve optimization of the NACA 0012 in inviscid transonic flow, as well as optimization of the RAE 2822 in viscous transonic flow. We solve the problems using a computationally efficient physics-based surrogate approach exploiting space mapping. Our results indicate that by shifting the computational burden to fast low-fidelity models, significant performance improvements can be achieved at the cost of a few evaluations of the expensive computational fluid dynamic models. In our approach, a commercial package FLUENT is used as the high-fidelity fluid flow solver with a hyperbolic C-mesh, whereas the versatile viscous-inviscid solver MSES is utilized as the low-fidelity model. The PARSEC parameterization method is used to describe the airfoil shapes with up to 10 design variables.

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Variable-Fidelity Aerodynamic Shape Optimization

Cited by 17 publications

References 68 publications

A framework for parametric design optimization using isogeometric analysis

A framework for parametric design optimization using isogeometric analysis

A Generic Test Suite for Evolutionary Multifidelity Optimization

Aerodynamic Design Optimization: Physics-based Surrogate Approaches for Airfoil and Wing Design

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