Author: United States. National Aeronautics and Space AdministrationPublisher:
ISBN:
Category :
Languages : en
Pages :
Book Description
TWO-DIMENSIONAL HIGH-LIFT AERODYNAMIC OPTIMIZATION USING NEURAL NETWORKS... NASA/TM-1998-112233... OCT. 27, 1998
Author: United States. National Aeronautics and Space AdministrationPublisher:
ISBN:
Category :
Languages : en
Pages :
Book Description
Two-Dimensional High-Lift Aerodynamic Optimization Using Neural Networks
Author: National Aeronautics and Space Administration (NASA)Publisher: Createspace Independent Publishing Platform
ISBN: 9781723469992
Category :
Languages : en
Pages : 142
Book Description
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 model. Because it is difficult to predict maximum lift for high-lift systems, an empirically-based maximum lift criteria was used in this study to determine both the maximum lift and the angle at which it occurs. The 'pressure difference rule, ' which states that the maximum lift condition corresponds to a certain pressure difference between the peak suction pressure and the pressure at the trailing edge of the element, was applied and verified with experimental observations for this configuration. Multiple input, single output networks were trained using the NASA Ames variation of the Levenberg-Marquardt algorithm for each of the aerodynamic coefficients (lift, drag and moment). The artificial neural networks were integrated with a gradient-based optimizer. Using independent numerical simulations and experimental data for this high-lift configuration, it was shown that this design process successfully optimized flap deflection, gap, overlap, and angle of attack to maximize lift. Once the neural nets were trained and integrated with the optimizer, minimal additional computer resources were required to perform optimization runs with different initial conditions and parameters. Applying the neural networks within the high-lift rigging optimization process reduced the amount of computational time and resources by 44% compared with traditional gradient-based optimization procedures for multiple optimization runs. Greenman, Roxana M. Ames Research Center NEURAL NETS; ANGLE OF ATTACK; NAVIER-STOKES EQUATION; LIFT; INCOMPRESSIBLE FLOW; COMPUTERS; AIRFOILS; AERODYNAMIC CONFIGURATIONS; AERODYNAMIC COEFFICIENTS; TURBULENCE MODELS; TRAILING EDGES; SUCTION; GRADIENTS; DRAG; FLAPPING; DEFLECTION; ALGO..
Two-dimensional High-lift Aerodynamic Optimization Using Neural Networks
Author: Roxana M. GreenmanJournal of Aircraft
Author: Two-dimensional, High-lift Aerodynamic Optimization Using the Continuous Adjoint Method
Author: Sangho Kim37th AIAA Aerospace Sciences Meeting and Exhibit
Author: Numerical Simulation of the Aerodynamics of High-Lift Configurations
Author: Omar Darío López MejiaPublisher: Springer
ISBN: 331962136X
Category : Technology & Engineering
Languages : en
Pages : 118
Book Description
This book deals with numerical simulations and computations of the turbulent flow around high-lift configurations commonly used in aircraft. It is devoted to the Computational Fluids Dynamics (CFD) method using full Navier-Stokes solvers typically used in the simulation of high-lift configuration. With the increase of computational resources in the aeronautical industry, the computation of complex flows such as the aerodynamics of high-lift configurations has become an active field not only in academic but also in industrial environments. The scope of the book includes applications and topics of interest related to the simulation of high-lift configurations such as: lift and drag prediction, unsteady aerodynamics, low Reynolds effects, high performance computing, turbulence modelling, flow feature visualization, among others. This book gives a description of the state-of-the-art of computational models for simulation of high-lift configurations. It also shows and discusses numerical results and validation of these computational models. Finally, this book is a good reference for graduate students and researchers interested in the field of simulation of high-lift configurations.
Analysis of the Hessian for Aerodynamic Optimization
Author: National Aeronautics and Space Administration (NASA)Publisher: Createspace Independent Publishing Platform
ISBN: 9781722089351
Category :
Languages : en
Pages : 24
Book Description
In this paper we analyze inviscid aerodynamic shape optimization problems governed by the full potential and the Euler equations in two and three dimensions. The analysis indicates that minimization of pressure dependent cost functions results in Hessians whose eigenvalue distributions are identical for the full potential and the Euler equations. However the optimization problems in two and three dimensions are inherently different. While the two dimensional optimization problems are well-posed the three dimensional ones are ill-posed. Oscillations in the shape up to the smallest scale allowed by the design space can develop in the direction perpendicular to the flow, implying that a regularization is required. A natural choice of such a regularization is derived. The analysis also gives an estimate of the Hessian's condition number which implies that the problems at hand are ill-conditioned. Infinite dimensional approximations for the Hessians are constructed and preconditioners for gradient based methods are derived from these approximate Hessians. Arian, Eyal and Ta'asan, Shlomo Ames Research Center NAS1-19480...
Application of Artificial Neural Networks to the Design Optimization of Aerospace Structural Components
Author: National Aeronautics and Space Administration (NASA)Publisher: Createspace Independent Publishing Platform
ISBN: 9781725149809
Category :
Languages : en
Pages : 30
Book Description
The application of artificial neural networks to capture structural design expertise is demonstrated. The principal advantage of a trained neural network is that it requires trivial computational effort to produce an acceptable new design. For the class of problems addressed, the development of a conventional expert system would be extremely difficult. In the present effort, a structural optimization code with multiple nonlinear programming algorithms and an artificial neural network code NETS were used. A set of optimum designs for a ring and two aircraft wings for static and dynamic constraints were generated by using the optimization codes. The optimum design data were processed to obtain input and output pairs, which were used to develop a trained artificial neural network with the code NETS. Optimum designs for new design conditions were predicted by using the trained network. Neural net prediction of optimum designs was found to be satisfactory for most of the output design parameters. However, results from the present study indicate that caution must be exercised to ensure that all design variables are within selected error bounds. Berke, Laszlo and Patnaik, Surya N. and Murthy, Pappu L. N. Glenn Research Center NASA-TM-4389, E-6994-1, NAS 1.15:4389 RTOP 505-63-5B...
Neural Network Prediction of New Aircraft Design Coefficients
Author: National Aeronautics and Space Adm NasaPublisher: Independently Published
ISBN: 9781792937910
Category : Science
Languages : en
Pages : 36
Book Description
This paper discusses a neural network tool for more effective aircraft design evaluations during wind tunnel tests. Using a hybrid neural network optimization method, we have produced fast and reliable predictions of aerodynamical coefficients, found optimal flap settings, and flap schedules. For validation, the tool was tested on a 55% scale model of the USAF/NASA Subsonic High Alpha Research Concept aircraft (SHARC). Four different networks were trained to predict coefficients of lift, drag, moment of inertia, and lift drag ratio (C(sub L), C(sub D), C(sub M), and L/D) from angle of attack and flap settings. The latter network was then used to determine an overall optimal flap setting and for finding optimal flap schedules. Norgaard, Magnus and Jorgensen, Charles C. and Ross, James C. Ames Research Center NASA-TM-112197, A-976719, NAS 1.15:112197 RTOP 519-30-12...