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    Shape Optimization of a Multi-Element Foil Using an Evolutionary Algorithm

    Source: Journal of Fluids Engineering:;2010:;volume( 132 ):;issue: 005::page 51401
    Author:
    Yu-Tai Lee
    ,
    Vineet Ahuja
    ,
    Michael Ebert
    ,
    Ashvin Hosangadi
    DOI: 10.1115/1.4001343
    Publisher: The American Society of Mechanical Engineers (ASME)
    Abstract: A movable flap with a NACA foil cross section serves as a common control surface for underwater marine vehicles. To augment the functionality of the control surface, a tab assisted control (TAC) surface was experimentally tested to improve its performance especially at large angles of operation. The advantage of the TAC foil could be further enhanced with shape memory alloy (SMA) actuators to control the rear portion of the control surface to form a flexible tab (or FlexTAC) surface. Hybrid unstructured Reynolds averaged Navier–Stokes (RANS) based computational fluid dynamics (CFD) calculations were used to understand the flow physics associated with the multi-element FlexTAC foil with a stabilizer, a flap, and a flexible tab. The prediction results were also compared with the measured data obtained from both the TAC and the FlexTAC experiments. The simulations help explain subtle differences in performance of the multi-element airfoil concepts. The RANS solutions also predict the forces and moments on the surface of the hydrofoil with reasonable accuracy and the RANS procedure is found to be critical for use in a design optimization framework because of the importance of flow separation/turbulent effects in the gap region between the stabilizer and the flap. A systematic optimization study was also carried out with a genetic algorithm (GA) based design optimization procedure. This procedure searches the complex design landscape in an efficient and parallel manner. The fitness evaluations in the optimization procedure were performed with the RANS based CFD simulations. The mesh regeneration was carried out in an automated manner through a scripting process within the grid generator. The optimization calculation is performed simultaneously on both the stabilizer and the nonflexible portion of the flap. Shape changes to the trailing edge of the stabilizer strongly influence the secondary flow patterns that set up in the gap region between the stabilizer and the flap. They were found to have a profound influence on force and moment characteristics of the multi-element airfoil. A new control surface (OptimTAC) was constructed as a result of the design optimization calculation and was shown to have improved lift, drag, and torque characteristics over the original FlexTAC airfoil at high flap angles.
    keyword(s): Torque , Computational fluid dynamics , Design , Optimization , Shapes , Flow (Dynamics) , Force , Evolutionary algorithms AND Engineering simulation ,
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      Shape Optimization of a Multi-Element Foil Using an Evolutionary Algorithm

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    http://yetl.yabesh.ir/yetl1/handle/yetl/143502
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    contributor authorYu-Tai Lee
    contributor authorVineet Ahuja
    contributor authorMichael Ebert
    contributor authorAshvin Hosangadi
    date accessioned2017-05-09T00:38:17Z
    date available2017-05-09T00:38:17Z
    date copyrightMay, 2010
    date issued2010
    identifier issn0098-2202
    identifier otherJFEGA4-27418#051401_1.pdf
    identifier urihttp://yetl.yabesh.ir/yetl/handle/yetl/143502
    description abstractA movable flap with a NACA foil cross section serves as a common control surface for underwater marine vehicles. To augment the functionality of the control surface, a tab assisted control (TAC) surface was experimentally tested to improve its performance especially at large angles of operation. The advantage of the TAC foil could be further enhanced with shape memory alloy (SMA) actuators to control the rear portion of the control surface to form a flexible tab (or FlexTAC) surface. Hybrid unstructured Reynolds averaged Navier–Stokes (RANS) based computational fluid dynamics (CFD) calculations were used to understand the flow physics associated with the multi-element FlexTAC foil with a stabilizer, a flap, and a flexible tab. The prediction results were also compared with the measured data obtained from both the TAC and the FlexTAC experiments. The simulations help explain subtle differences in performance of the multi-element airfoil concepts. The RANS solutions also predict the forces and moments on the surface of the hydrofoil with reasonable accuracy and the RANS procedure is found to be critical for use in a design optimization framework because of the importance of flow separation/turbulent effects in the gap region between the stabilizer and the flap. A systematic optimization study was also carried out with a genetic algorithm (GA) based design optimization procedure. This procedure searches the complex design landscape in an efficient and parallel manner. The fitness evaluations in the optimization procedure were performed with the RANS based CFD simulations. The mesh regeneration was carried out in an automated manner through a scripting process within the grid generator. The optimization calculation is performed simultaneously on both the stabilizer and the nonflexible portion of the flap. Shape changes to the trailing edge of the stabilizer strongly influence the secondary flow patterns that set up in the gap region between the stabilizer and the flap. They were found to have a profound influence on force and moment characteristics of the multi-element airfoil. A new control surface (OptimTAC) was constructed as a result of the design optimization calculation and was shown to have improved lift, drag, and torque characteristics over the original FlexTAC airfoil at high flap angles.
    publisherThe American Society of Mechanical Engineers (ASME)
    titleShape Optimization of a Multi-Element Foil Using an Evolutionary Algorithm
    typeJournal Paper
    journal volume132
    journal issue5
    journal titleJournal of Fluids Engineering
    identifier doi10.1115/1.4001343
    journal fristpage51401
    identifier eissn1528-901X
    keywordsTorque
    keywordsComputational fluid dynamics
    keywordsDesign
    keywordsOptimization
    keywordsShapes
    keywordsFlow (Dynamics)
    keywordsForce
    keywordsEvolutionary algorithms AND Engineering simulation
    treeJournal of Fluids Engineering:;2010:;volume( 132 ):;issue: 005
    contenttypeFulltext
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