Airfoil shape parameterization for optimum Navier–Stokes design with genetic algorithm

Shahrokhi, Ava; Jahangirian, A.

doi:10.1016/j.ast.2007.04.004

Cited by 86 publications

(44 citation statements)

References 7 publications

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“…Though often overly simplistic in representation (conventionally two-or three-dimensional plots), fitness landscapes have become crucial in terms of our understanding of how an evolving population will behave relative to a static fitness function. The fitness function itself describes a property which will dictate selection; this could plausibly be enzyme activity or specificity in the case of protein evolution, or a measure of drag in the design of airfoils in a 'real life' evolutionary optimization problem (Shahrokhi & Jahangirian 2007). Commonly, the evolving population is envisaged as a single hill-climber; an algorithm which crosses the landscape by accepting only genetic modifications (single-point mutations) that result in an improvement in fitness (Kauffman & Levin 1987).…”

Section: Introductionmentioning

confidence: 99%

Analysis of a complete DNA–protein affinity landscape

Rowe

Platt

Wedge

et al. 2009

J. R. Soc. Interface.

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Properties of biological fitness landscapes are of interest to a wide sector of the life sciences, from ecology to genetics to synthetic biology. For biomolecular fitness landscapes, the information we currently possess comes primarily from two sources: sparse samples obtained from directed evolution experiments; and more fine-grained but less authentic information from 'in silico' models (such as NK-landscapes). Here we present the entire protein-binding profile of all variants of a nucleic acid oligomer 10 bases in length, which we have obtained experimentally by a series of highly parallel on-chip assays. The resulting complete landscape of sequence-binding pairs, comprising more than one million binding measurements in duplicate, has been analysed statistically using a number of metrics commonly applied to synthetic landscapes. These metrics show that the landscape is rugged, with many local optima, and that this arises from a combination of experimental variation and the natural structural properties of the oligonucleotides.

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Section: Introductionmentioning

confidence: 99%

Analysis of a complete DNA–protein affinity landscape

Rowe

Platt

Wedge

et al. 2009

J. R. Soc. Interface.

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“…The manual effort required in such design studies can be considerably reduced by employing modern design automation and optimization techniques and there are, of course, numerous examples of such techniques being applied throughout the literature. Aerofoil sections [1,2], compressor blades [3], wings [4], aircraft [5], combustors [6][7][8] and whole engines [9,10], for example, have all been the subject of automated design optimizations in recent years. However, the majority of engineering design optimization examples within the literature include a fundamental limitation which can limit the benefits that such automation can bring to real world problems.…”

Section: Introductionmentioning

confidence: 99%

Isothermal Combustor Prediffuser and Fuel Injector Feed Arm Design Optimization Using the prometheus Design System

Zhang

Toal

Keane

et al. 2015

Journal of Engineering for Gas Turbines and Power

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The prometheus combustor design system aims to reduce the complexity of evaluating combustor designs by automatically defining preprocessing, simulation, and postprocessing tasks based on the automatic identification of combustor features within the computer-aided design (CAD) environment. This system enables best practice to be codified and topological changes to a combustor's design to be more easily considered within an automated design process. The following paper presents the prometheus combustor design system and its application to the multiobjective isothermal optimization of a combustor prediffuser and the multifidelity isothermal optimization of a fuel injector feed arm in combination with a surrogate modeling strategy accelerated via a high-performance graphical processing unit (GPU).

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“…Actually, the boundary values of the input parameters have widespread orders. For example, the domain of changes for the rst PARSEC parameter (r LE ) is [0.006-0.0115] where the domain of the fth parameter ( TE ) is [0][1][2][3][4][5][6][7][8][9][10]. This causes the e ects of the higherorder parameters to be much more than those of the lower-order parameters during the training process.…”

Section: Pre-processing the Training Datamentioning

confidence: 99%

“…The boundaries are introduced in Section 6 for airfoil shape optimization. More information about PARSEC parameterization can be found in reference [6]. Thus, the genetic algorithm starts with a collection of chromosomes and generates new chromosomes from previously generated members using GA operators.…”

Section: Aerodynamic Optimization With Gamentioning

confidence: 99%

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An Optimum Neural Network for Evolutionary Aerodynamic Shape Design

2017

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Abstract. Two new techniques are proposed to enhance the estimation abilities of the conventional Neural Network (NN) method in its application to the tness function estimation of aerodynamic shape optimization with the Genetic Algorithm (GA). The rst technique is pre-processing the training data in order to increase the training accuracy of the Multi-Layer Perceptron (MLP) approach. The second technique is a new structure for the network to improve its quality through a modi ed growing and pruning method. Using the proposed techniques, one can obtain the best estimations from the NN with less computational time. The new methods are applied for optimum design of a transonic airfoil and the results are compared with those obtained from the accurate Computational Fluid Dynamics (CFD) tness evaluator and with the conventional MLP NN approach. The numerical experiments show that using the new method can reduce the computational time signi cantly while achieving improved accuracy.

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Airfoil shape parameterization for optimum Navier–Stokes design with genetic algorithm

Cited by 86 publications

References 7 publications

Analysis of a complete DNA–protein affinity landscape

Analysis of a complete DNA–protein affinity landscape

Isothermal Combustor Prediffuser and Fuel Injector Feed Arm Design Optimization Using the prometheus Design System

An Optimum Neural Network for Evolutionary Aerodynamic Shape Design

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