Results 1 to 10 of about 69,093 (209)

Machine learning in aerodynamic shape optimization

Progress in Aerospace Sciences, 2022
Machine learning (ML) has been increasingly used to aid aerodynamic shape optimization (ASO), thanks to the availability of aerodynamic data and continued developments in deep learning. We review the applications of ML in ASO to date and provide a perspective on the state-of-the-art and future directions. We first introduce conventional ASO and current
Jichao Li, Xiaosong Du, Joaquim R R A Martins   +2 more
exaly   +3 more sources

Aerodynamic design optimization: Challenges and perspectives

Computers and Fluids, 2022
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Joaquim R R A Martins
exaly   +2 more sources

A Review of Intelligent Airfoil Aerodynamic Optimization Methods Based on Data-Driven Advanced Models

Mathematics
With the rapid development of artificial intelligence technology, data-driven advanced models have provided new ideas and means for airfoil aerodynamic optimization.
Liyue Wang, Haochen Zhang, Cong Wang, Jun Tao, Xinyue Lan, Gang Sun, Jinzhang Feng   +6 more
doaj   +3 more sources

Aerodynamic Benefits by Optimizing Cycling Posture [PDF]

Applied Sciences, 2022
An approach to aerodynamically optimizing cycling posture and reducing drag in an Ironman (IM) event was elaborated. Therefore, four commonly used positions in cycling were investigated and simulated for a flow velocity of 10 m/s and yaw angles of 0–20° using OpenFoam-based Nabla Flow CFD simulation software software.
Alois Schaffarczyk, Silas Koehn, Luca Oggiano, Kai Schaffarczyk   +3 more
openaire   +3 more sources

Noise prediction research of a scaled turboprop aircraft

Xibei Gongye Daxue Xuebao, 2021
Aerodynamic noise level has become an important performance index of civil aircraft, and it is drawing more and more attention. Most airframe noise research based on CFD method is aimed at aircraft individual parts at present, while lack of noise ...
Song Minhua, Song Wenping, Wang Yue, Han Zhonghua, Zhang Yanjun, Lei Wutao   +5 more
doaj   +1 more source

Airfoil Robust Optimization Based on Convolutional Neural Network and Polynomial Chaos Method

Hangkong gongcheng jinzhan, 2021
In conventional airfoil optimization design method, the aerodynamic performance of the optimal airfoil can deteriorate at the non-design point, so it is necessary to study the airfoil robust optimization method.An airfoil robustness design method based ...
GAO Yuan, YAN Yan, ZHOU Lei, LI Dian, HAO Haibing   +4 more
doaj   +1 more source

Two-stage aerodynamic optimization method based on early termination of CFD convergence and variable-fidelity model

Xibei Gongye Daxue Xuebao, 2021
Efficient aerodynamic design optimization method is of great value for improving the aerodynamic performance of little UAV's airfoil. Using engineering or semi-engineering estimation method to analyze aerodynamic forces in solving aerodynamic ...

doaj   +1 more source

High-dimensional aerodynamic data modeling using a machine learning method based on a convolutional neural network

Advances in Aerodynamics, 2022
Modeling high-dimensional aerodynamic data presents a significant challenge in aero-loads prediction, aerodynamic shape optimization, flight control, and simulation.
Bo-Wen Zan, Zhong-Hua Han, Chen-Zhou Xu, Ming-Qi Liu, Wen-Zheng Wang   +4 more
doaj   +1 more source

Multi-objective aerodynamic optimization of flying-wing configuration based on adjoint method

Xibei Gongye Daxue Xuebao, 2021
In order to solve the multi-objective multi-constraint design in aerodynamic design of flying wing, the aerodynamic optimization design based on the adjoint method is studied. In terms of the principle of the adjoint equation, the boundary conditions and

doaj   +1 more source

Quantitative Weight and Two-Particle Search Algorithm to Optimize Aero-Stealth Performance of a Backward Inclined Vertical Tail

Aerospace, 2023
To study the influence of the tilt-back design of a vertical tail on its aerodynamic stealth characteristics, an optimization method based on a quantitative weight coefficient and a two-particle search algorithm is presented.
Zeyang Zhou, Jun Huang
doaj   +1 more source