Journal ArticleDOI
Artificial neural network based inverse design: Airfoils and wings
Gang Sun,Sun Yanjie,Shuyue Wang +2 more
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TLDR
In this paper, an applicable airfoil/wing inverse design method with the help of Artificial Neural Network and an aircraft/wing database is presented. But the proposed method is not suitable for aircraft and turbomachinery designers.About:
This article is published in Aerospace Science and Technology.The article was published on 2015-04-01. It has received 65 citations till now. The article focuses on the topics: Airfoil & Wing.read more
Citations
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Journal ArticleDOI
Inverse Design of Airfoil Using a Deep Convolutional Neural Network
TL;DR: This paper proposes an approach to perform the inverse design of airfoils using deep convolutional neural networks (CNNs) that are based on the solution of differential equa...
Journal ArticleDOI
A review of the artificial neural network surrogate modeling in aerodynamic design
Gang Sun,Shuyue Wang +1 more
TL;DR: New frontiers of modern artificial neural network surrogate modeling are reviewed with regard to exploiting the hidden information for bringing new perspectives to optimization by exploring new data form and patterns.
Journal ArticleDOI
Fast pressure distribution prediction of airfoils using deep learning
TL;DR: This paper presents a data-driven approach for predicting the pressure distribution over airfoils based on Convolutional Neural Network (CNN), and utilizes a universal and flexible parametrization method called Signed Distance Function to improve the performances of CNN.
Journal ArticleDOI
Multi-kernel neural networks for nonlinear unsteady aerodynamic reduced-order modeling
Jiaqing Kou,Weiwei Zhang +1 more
TL;DR: Results indicate that the proposed multi-kernel neural networks outperform the single-kernel RBF neural networks in modeling noise-free and noisy aerodynamics at a constant Mach number, as well as predicting the aerodynamic loads with varying Mach numbers.
Journal ArticleDOI
Neural Networks-Based Aerodynamic Data Modeling: A Comprehensive Review
TL;DR: This paper analyzes the shortcomings of computational fluid dynamics (CFD) and traditional reduced-order models (ROMs) and identifies three important trends for future studies in aerodynamic data modeling.
References
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Journal ArticleDOI
A general regression neural network
TL;DR: The general regression neural network (GRNN) is a one-pass learning algorithm with a highly parallel structure that provides smooth transitions from one observed value to another.
Proceedings ArticleDOI
Multi-column deep neural networks for image classification
TL;DR: In this paper, a biologically plausible, wide and deep artificial neural network architectures was proposed to match human performance on tasks such as the recognition of handwritten digits or traffic signs, achieving near-human performance.
Journal ArticleDOI
Wing Design by Numerical Optimization
TL;DR: In this article, a study was conducted to assess the feasibility of performing computerized wing design by numerical optimization, which combined a full potential, inviscid aerodynamics code with a conjugate gradient optimization algorithm.
Journal ArticleDOI
Optimum aerodynamic design using the Navier-Stokes equations
TL;DR: In this paper, the authors describe the formulation of optimization techniques based on control theory for aerodynamic shape design in viscous compressible flow, modeled by the Navier-Stokes equations.
Book ChapterDOI
Parametric Airfoils and Wings
TL;DR: In this article, the authors used explicit mathematical functions for 2D curve definition for airfoil design and 3D wing definition for high lift systems by modelled track gear geometries, translation and rotation in 3D space.
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Neural networks based airfoil generation for a given Cp using Bezier–PARSEC parameterization
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