Single-rotor UAV flow field simulation using generative adversarial networks

Abstract In recent years, with the large-scale application of unmanned aerial vehicles (UAV) in agricultural plant protection, various shortcomings of have been identified; for example, the rotor flow field of the UAV will cause drift of the droplets, resulting in waste and secondary disaster. Therefore, digital simulation has become a necessity. However, due to the complexity of the rotor flow field of the rotor UAV and the operating environment of the UAV, digital simulation is associated with a large workload and great computational costs. It is urgent to explore new models using deep learning to identify the law of the rotor flow field. To address this problem, deep learning, in combination with flow field methods, is used to explore new models in this paper. A generative adversarial network (GAN) prediction model is proposed in this paper. The GAN includes a generation network and a discrimination network. In this paper, the features of the flow field are learned by the generative network to identify deep features of the flow field. The discrimination network distinguishes between true and false pictures by extracting features during training to realize adversarial training. This model can predict the flow field by identifying features of the flow-field distribution in training samples to build a predictive model. The compression effects of the computational fluid dynamics (CFD) model and the GAN model are compared in this paper. The GAN model outperforms the CFD model in predicting the flow field and compressing the data.

[1]  F. Nicoud,et al.  Wall-Adapting Local Eddy-Viscosity models for simulations in complex geometries , 2007 .

[2]  Jette Reeg,et al.  Modelling direct and indirect effects of herbicides on non-target grassland communities , 2017 .

[3]  Yao Zhang,et al.  Application of Convolutional Neural Network to Predict Airfoil Lift Coefficient , 2017, ArXiv.

[4]  Ruddy Brionnaud,et al.  Direct Noise Computation with a Lattice-Boltzmann Method and Application to Industrial Test Cases , 2016 .

[5]  Nils Thürey,et al.  tempoGAN: A Temporally Coherent, Volumetric GAN for Super-resolution Fluid Flow , 2018, ACM Trans. Graph..

[6]  Tom White,et al.  Generative Adversarial Networks: An Overview , 2017, IEEE Signal Processing Magazine.

[7]  Marco Aurélio Gerosa,et al.  XFlow: An Extensible Tool for Empirical Analysis of Software Systems Evolution , 2011 .

[8]  Nataliia Kussul,et al.  Deep Learning Classification of Land Cover and Crop Types Using Remote Sensing Data , 2017, IEEE Geoscience and Remote Sensing Letters.

[9]  Zhang Ling,et al.  Numerical simulation and experimental verification on downwash air flow of six-rotor agricultural unmanned aerial vehicle in hover , 2017 .

[10]  Fei Zhou,et al.  MvSSIM: A quality assessment index for hyperspectral images , 2018, Neurocomputing.

[11]  Daniel Rueckert,et al.  Real-Time Single Image and Video Super-Resolution Using an Efficient Sub-Pixel Convolutional Neural Network , 2016, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[12]  J. Nathan Kutz,et al.  Deep learning in fluid dynamics , 2017, Journal of Fluid Mechanics.

[13]  Eero P. Simoncelli,et al.  Image quality assessment: from error visibility to structural similarity , 2004, IEEE Transactions on Image Processing.

[14]  Scott Thibault,et al.  CFD Simulation of a Quad-Rotor UAV with Rotors in Motion Explicitly Modeled Using an LBM Approach with Adaptive Refinement , 2017 .

[15]  He Xiongkui,et al.  Recent development of unmanned aerial vehicle for plant protection in East Asia , 2017 .

[16]  Mahmoud R. El-Sakka,et al.  Improved BM3D image denoising using SSIM-optimized Wiener filter , 2018, EURASIP J. Image Video Process..

[17]  Sebastian Bosse,et al.  A Haar wavelet-based perceptual similarity index for image quality assessment , 2016, Signal Process. Image Commun..

[18]  Xiuping Jia,et al.  Deep Feature Extraction and Classification of Hyperspectral Images Based on Convolutional Neural Networks , 2016, IEEE Transactions on Geoscience and Remote Sensing.

[19]  Collin James Preftakes.  Exposure and risk to non-target receptors for agricultural spray drift of formulation types and adjuvants , 2017 .

[20]  Andreas Kamilaris,et al.  Deep learning in agriculture: A survey , 2018, Comput. Electron. Agric..

[21]  Alexei A. Efros,et al.  Image-to-Image Translation with Conditional Adversarial Networks , 2016, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[22]  Rob Fergus,et al.  Visualizing and Understanding Convolutional Networks , 2013, ECCV.

[23]  Konstantinos P. Ferentinos,et al.  Deep learning models for plant disease detection and diagnosis , 2018, Comput. Electron. Agric..

[24]  Hui Li,et al.  Prediction model of velocity field around circular cylinder over various Reynolds numbers by fusion convolutional neural networks based on pressure on the cylinder , 2018 .

[25]  D. Pandya,et al.  A computational fluid dynamics based artificial neural network model to predict solid particle erosion , 2017 .

[26]  Hemerson Pistori,et al.  Weed detection in soybean crops using ConvNets , 2017, Comput. Electron. Agric..

[27]  Y. Men,et al.  Research on CO dispersion of a vehicular exhaust plume using Lattice Boltzmann Method and Large Eddy Simulation , 2017 .