scholarly journals Multiple Aerodynamic Coefficient Prediction of Airfoils Using a Convolutional Neural Network

Symmetry ◽  
2020 ◽  
Vol 12 (4) ◽  
pp. 544
Author(s):  
Hai Chen ◽  
Lei He ◽  
Weiqi Qian ◽  
Song Wang
Keyword(s):  
Neural Network ◽  
Prediction Model ◽  
Convolutional Neural Network ◽  
Model Simulation ◽  
Prediction Method ◽  
Aircraft Design ◽  
Aerodynamic Characteristics ◽  
Aerodynamic Coefficients ◽  
Pitch Moment ◽  
Drag And Lift

Both symmetric and asymmetric airfoils are widely used in aircraft design and manufacture, and they have different aerodynamic characteristics. In order to improve flight performance and ensure flight safety, the aerodynamic coefficients of these airfoils must be obtained. Various methods are used to generate aerodynamic coefficients. The prediction model is a promising method that can effectively reduce cost and time. In this paper, a graphical prediction method for multiple aerodynamic coefficients of airfoils based on a convolutional neural network (CNN) is proposed. First, a transformed airfoil image (TAI) was constructed by using the flow-condition convolution with the airfoil image. Next, TAI was combined with the original airfoil image to form a composite airfoil image (CAI) that is used as the input of the CNN prediction model. Then, the structure and parameters of the prediction model were designed according to CAI features. Finally, a sample set that was generated on the basis of the deformation of symmetrical airfoil NACA 0012 was used to train and test the prediction model. Simulation results showed that the proposed method based on CNN could simultaneously predict the pitch-moment, drag, and lift coefficients, and prediction accuracy was high.

scholarly journals Application of Improved Naive Bayesian-CNN Classification Algorithm in Sandstorm Prediction in Inner Mongolia

2019 ◽  
Vol 2019 ◽  
pp. 1-13
Author(s):  
Li Tiancheng ◽  
Ren Qing-dao-er-ji ◽  
Qiu Ying
Keyword(s):  
Neural Network ◽  
Prediction Model ◽  
Convolutional Neural Network ◽  
Inner Mongolia ◽  
Prediction Method ◽  
Classification Algorithm ◽  
Bayesian Algorithm ◽  
Naive Bayesian ◽  
Model Based ◽  
Naïve Bayesian

Hazards of sandstorm are increasingly recognized and valued by the general public, scientific researchers, and even government decision-making bodies. This paper proposed an efficient sandstorm prediction method that considered both the effect of atmospheric movement and ground factors on sandstorm occurrence, called improved naive Bayesian-CNN classification algorithm (INB-CNN classification algorithm). Firstly, we established a sandstorm prediction model based on the convolutional neural network algorithm, which considered atmospheric movement factors. Convolutional neural network (CNN) is a deep neural network with convolution structure, which can automatically learn features from massive data. Then, we established a sandstorm prediction model based on the Naive Bayesian algorithm, which considered ground factors. Finally, we established a sandstorm prediction model based on the improved naive Bayesian-CNN classification algorithm. Experimental results showed that the prediction accuracy of the sandstorm prediction model based on INB-CNN classification algorithm is higher than that of others and the model can better reflect the law of sandstorm occurrence. This paper used two algorithms, naive Bayesian algorithm and CNN algorithm, to identify and diagnose the strength of sandstorm in Inner Mongolia and found that combining the two algorithms, INB-CNN classification algorithm had the greatest success in predicting the occurrence of sandstorms.

scholarly journals A phase-error prediction method for coherent beam combining via convolutional neural network

Optik ◽  
2021 ◽  
pp. 167827
Author(s):  
Haolong Jia ◽  
Jing Zuo ◽  
Qiliang Bao ◽  
Chao Geng ◽  
Xinyang Li ◽  
...  
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Phase Error ◽  
Prediction Method ◽  
Coherent Beam ◽  
Error Prediction ◽  
Coherent Beam Combining ◽  
Beam Combining

scholarly journals Forecast Model of TV Show Rating Based on Convolutional Neural Network

Complexity ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Lingfeng Wang
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Prediction Model ◽  
Convolutional Neural Network ◽  
Network Structure ◽  
Rating Data ◽  
Tv Program ◽  
Neural Network Structure ◽  
Rating Prediction ◽  
Tv Show

The TV show rating analysis and prediction system can collect and transmit information more quickly and quickly upload the information to the database. The convolutional neural network is a multilayer neural network structure that simulates the operating mechanism of biological vision systems. It is a neural network composed of multiple convolutional layers and downsampling layers sequentially connected. It can obtain useful feature descriptions from original data and is an effective method to extract features from data. At present, convolutional neural networks have become a research hotspot in speech recognition, image recognition and classification, natural language processing, and other fields and have been widely and successfully applied in these fields. Therefore, this paper introduces the convolutional neural network structure to predict the TV program rating data. First, it briefly introduces artificial neural networks and deep learning methods and focuses on the algorithm principles of convolutional neural networks and support vector machines. Then, we improve the convolutional neural network to fit the TV program rating data and finally apply the two prediction models to the TV program rating data prediction. We improve the convolutional neural network TV program rating prediction model and combine the advantages of the convolutional neural network to extract effective features and good classification and prediction capabilities to improve the prediction accuracy. Through simulation comparison, we verify the feasibility and effectiveness of the TV program rating prediction model given in this article.

scholarly journals Automatic Design System with Generative Adversarial Network and Convolutional Neural Network for Optimization Design of Interior Permanent Magnet Synchronous Motor

2021 ◽  
Author(s):  
Yuki Shimizu ◽  
Shigeo Morimoto ◽  
Masayuki Sanada ◽  
Yukinori Inoue
Keyword(s):  
Neural Network ◽  
Prediction Model ◽  
Convolutional Neural Network ◽  
Permanent Magnet ◽  
Optimization Design ◽  
Design System ◽  
Automatic Design ◽  
Wide Range ◽  
Interior Permanent Magnet ◽  
Long Time

The optimal design of interior permanent magnet synchronous motors requires a long time because finite element analysis (FEA) is performed repeatedly. To solve this problem, many researchers have used artificial intelligence to construct a prediction model that can replace FEA. However, because the training data are generated by FEA, it takes a very long time to obtain a sufficient amount of data, making it impossible to train a large-scale prediction model. Here, we propose a method for generating a large amount of data from a small number of FEA results using machine learning. An automatic design system with a deep generative model and a convolutional neural network is then constructed. With its sufficient data, the proposed system can handle three topologies and three motor parameters in a wide range of current vector regions. The proposed system was applied to multi-objective optimization design, with the optimization completed in 13-15 seconds.

scholarly journals Automatic Design System with Generative Adversarial Network and Convolutional Neural Network for Optimization Design of Interior Permanent Magnet Synchronous Motor

2021 ◽  
Author(s):  
Yuki Shimizu ◽  
Shigeo Morimoto ◽  
Masayuki Sanada ◽  
Yukinori Inoue
Keyword(s):  
Neural Network ◽  
Prediction Model ◽  
Convolutional Neural Network ◽  
Permanent Magnet ◽  
Optimization Design ◽  
Design System ◽  
Automatic Design ◽  
Wide Range ◽  
Interior Permanent Magnet ◽  
Long Time

The optimal design of interior permanent magnet synchronous motors requires a long time because finite element analysis (FEA) is performed repeatedly. To solve this problem, many researchers have used artificial intelligence to construct a prediction model that can replace FEA. However, because the training data are generated by FEA, it takes a very long time to obtain a sufficient amount of data, making it impossible to train a large-scale prediction model. Here, we propose a method for generating a large amount of data from a small number of FEA results using machine learning. An automatic design system with a deep generative model and a convolutional neural network is then constructed. With its sufficient data, the proposed system can handle three topologies and three motor parameters in a wide range of current vector regions. The proposed system was applied to multi-objective optimization design, with the optimization completed in 13-15 seconds.

Sign in / Sign up

Export Citation Format

Share Document