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3 edition of Two-dimensional high-lift aerodynamic optimization using neural networks found in the catalog.

Two-dimensional high-lift aerodynamic optimization using neural networks

Two-dimensional high-lift aerodynamic optimization using neural networks

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  • 17 Currently reading

Published by National Aeronautics and Space Administration, Ames Research Center, National Technical Information Service, distributor in Moffett Field, Calif, Springfield, Va .
Written in English

    Subjects:
  • Neural nets.,
  • Angle of attack.,
  • Navier-Stokes equation.,
  • Lift.,
  • Incompressible flow.,
  • Computers.,
  • Airfoils.,
  • Aerodynamic configurations.,
  • Aerodynamic coefficients.

  • Edition Notes

    Other titlesTwo dimensional high lift aerodynamic optimization using neural networks.
    StatementRoxana M. Greenman.
    Series[NASA technical memorandum] -- NASA/TM-1998-112233., NASA technical memorandum -- 112233.
    ContributionsAmes Research Center.
    The Physical Object
    FormatMicroform
    Pagination1 v.
    ID Numbers
    Open LibraryOL18132744M

    AIAA Aerodynamic Shape Optimization Using a Cartesian Adjoint Method and CAD Geometry AIAA Prediction of Vertical Tail Buffet Using Artificial Neural Networks. Levinski, O. / Hill, B. / American Institute of AIAA Effect of Acoustic Slat Modifications on Aerodynamic Properties of High-Lift System. Ortmann, J. The present study is concerned with experimental and computational investigation of a jet-controlled high-lift hydrofoil with a flap, i.e., BLC hydrofoil using a blown flap. The primary objective is to understand the lift increase phenomena by such a device, which can be implemented in many of already deployed surface ships and under-water.

    Mar 18,  · Optimization of the cooling requires an accurate prediction of aerodynamic losses and heat transfer on turbine blades. A new two-dimensional compressible, aerothermal boundary layer code has been developed. The formulation includes strong viscous-inviscid interaction, which enhances the stability properties of the niarbylbaycafe.com by: 1. On the pressure dependence of ignition and mixing in two-dimensional reactive shock-bubble interaction. Winter, M.; Breitsamter, C.: Reduced-Order Modeling of Unsteady Aerodynamic Loads Using Radial Basis Function Neural Networks. Aerodynamic Optimization of a Morphing Membrane Wing. ICAS Congress [28th.], , pp. more.

    Many complex aeronautical design problems can be formulated with efficient multi-objective evolutionary optimization methods and game strategies. This book describes the role of advanced innovative evolution tools in the solution, or the set of solutions of single or multi disciplinary optimization. SIAM Journal on Numerical Analysis. Article Tools. Add to my favorites. Download Citations. Reduced-Order Modeling of Subsurface Multi-phase Flow Models Using Deep Residual Recurrent Neural Networks. Transport in Porous Media Model order reduction for steady aerodynamics of high-lift configurations. CEAS Aeronautical Journal Cited by:


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Two-dimensional high-lift aerodynamic optimization using neural networks Download PDF EPUB FB2

NASA / TM- Two-Dimensional High-Lift Aerodynamic Optimization Using Neural Networks Roxana M. Greenman Ames Research Center, Moffett Field, California. Get this from a library. Two-dimensional high-lift aerodynamic optimization using neural networks.

[Roxana M Greenman; Ames Research Center.; United States. National Technical Information Service,; United States. National Aeronautics and Space Administration,]. The high-lift performance of a multi-element airfoil was optimized by using neural-net predictions that were trained using a computational data set.

The numerical data was generated using a two-dimensional, incompressible, Navier-Stokes algorithm with the Spalart-Allmaras turbulence niarbylbaycafe.com by: Two-dimensional Two-dimensional high-lift aerodynamic optimization using neural networks book Aerodynamic Optimization Using the Continuous Adjoint Method Sangho Kim,* Juan J.

Alonso* and Antony Jameson* Stanford University, Stanford, CA An adjoint-based Navier-Stokes design and optimization method for two-dimensional multi-element high-lift configurations is derived and presented.

The compressible. A 'read' is counted each time someone views a publication summary (such as the title, abstract, and list of authors), clicks on a figure, or views or downloads the full-text.

The design of the high-lift system is crucial for the economical success of a commercial airplane. The aim of this work is to find an optimized high-lift system with improved aerodynamic.

Aircraft engine preliminary design utilizing a cascade optimization strategy with neural network and regression approximations Surya Patnaik, Dale Hopkins and.

GLOBAL OPTIMIZATION OF TWO-DIMENSIONAL HIGH LIFT AERODYNAMIC SYSTEM build a relation between several input design pa-rameters and one or several responses [8], [9]. For the modeling part of the DOE a large number of fractional factorial designs have been developed, such as Box-Behnken design, Latin square design, Box-Wilson (Central Composite).

Application of Swarm Approach and Artificial Neural Networks for Airfoil Shape Optimization Manas S. Khurana 1, Hadi Winarto 2 and Arvind K.

Sinha 3 The Sir Lawrence Wackett Aerospace Centre – RMIT University, Melbourne, VIC, The Direct Numerical Optimization (DNO) approach for. Multi-Fidelity High-Lift Aerodynamic Optimization of Single-Element Airfoils two-dimensional high-lift system design [4,5]. The adjoint method is gradient-based, but it is very efficient since these studies the surrogates are constructed by approximating the high-fidelity model data using nonlinear regression, e.g., neural networks and.

Combined high-speed and high-lift wing aerodynamic optimization using a coupled VLMD RANS approach. a two-dimensional RANS flow solver extended for infinite swept flows multi-topology high-lift optimization can be performed by assigning different viscous sectional data corresponding to different high-lift configurations along the niarbylbaycafe.com by: 4.

Franke D.M. () Aerodynamic Optimization of a High-Lift System with Kinematic Constraints. In: Dillmann A., Heller G., Kreplin HP., Nitsche W., Peltzer I.

(eds) New Results in Numerical and Experimental Fluid Mechanics VIII. Notes on Numerical Fluid Mechanics and Multidisciplinary Design, vol Springer, Berlin, HeidelbergCited by: 1. Antony Jameson and Massimiliano Fatica, “Using Computational Fluid Dynamics for Aerodynamics”, Stanford University.

3 Boing using 22 M Cells on High lift Configuration. 2 18 Aerodynamic Optimization The use of computational simulation to scan many alternative designs has proved extremely valuable in practice, but it is also evident that.

High-lift configuration design is a highly multi-disciplinary process. For the present study, the problem is simplified to a two-dimensional aerodynamic optimization of a high-lift configura-tion.

This simplification allows for a higher-fidelity solver, providing an. Design of optimal aerodynamic shapes using stochastic optimization methods and computational intelligence.

Author links open be the optimizer GADO developed in the Rutgers University and used for several applications: the optimal design of two-dimensional supersonic missile Neural networks for optimization and signal processing, Wiley Cited by: Tang Z, Périaux J, Bugeda G and Oñate E Lift maximization with uncertainties for the optimization of high lift devices using multi-criterion evolutionary algorithms Proceedings of the Eleventh conference on Congress on Evolutionary Computation, ().

wing in ground effect considering aerodynamic characteristics and aerodynamic center of Pareto optima that include high-lift, high efficiency, and more stable airfoils on the edge OPTIMIZATION OF TWO-DIMENSIONAL WING IN GROUND EFFECT The fluid proper-ties are taken to be constant.

Nacelle design study of a supersonic experimental airplane using aerodynamic shape optimization. Makino, Z. Lei and; T. Iwamiya; Application of neural networks to store loads grid-survey.

F /digital wind tunnel testing of active low momentum flow control on single and multi-component airfoil systems at high lift. Lewington, D. Jan 19,  · AbstractThis study illustrates an deep learning approach supported by a metaheuristic design targeting the foremost features and parameters of artificial neural network (ANN) framework used in predicting relative exergy destruction ((frel,dest*) (f_{rel, dest}^*)) of a turboprop components.

The development of deep ANN comprising of three-hidden layers using data obtained considering multiple Author: Tolga Baklacioglu, Onder Turan, Hakan Aydin. Aug 04,  · Efficient aerodynamic shape optimization of transonic wings using a parallel infilling strategy and surrogate models Keane AJ () Design optimization of a two-dimensional subsonic engine air intake.

AIAA J Yamamoto K () Multi-objective aerodynamic exploration of elements’ setting for high-lift airfoil using kriging model. J Cited by:. Variable-Fidelity Kinematic Optimization of a Two-Dimensional Hovering Wing John Moore1, Bret Stanford2, Aaron McClung3, high lift and low power philosophies will conflict, complicating the constrained optimization process.

(the aerodynamic force along the Y-axis of Figure 1) is given on the left of Figure 3.A dimension reduction method called discrete empirical interpolation is proposed and shown to dramatically reduce the computational complexity of the popular proper orthogonal decomposition (POD) method for constructing reduced-order models for time dependent and/or parametrized nonlinear partial differential equations (PDEs).

In the presence of a general nonlinearity, the standard POD Cited by: Jun 28,  · AbstractThe flow field in a compressor cascade is very complex owing to the highly 3D, turbulent, and viscous properties. However, in the through-flow analysis method, the viscosity effects are taken into account using empirical models.

These models were based on experimental results for early blades. However, the blade types in modern compressors are quite different from those in older Author: Teng Fei, Lucheng Ji, Weilin Yi.