Effects of Rotor-Rotor Interaction on the Wake Structure and Thrust Generation of a Quadrotor Unmanned Aerial Vehicle

Lee, Seungcheol; Chae, Seokbong; Woo, Seong Yong; Jang, Jaesung; Kim, Jooha

doi:10.1109/access.2021.3088150

Cited by 20 publications

(25 citation statements)

References 55 publications

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“…Based on Figure 5 and Equation ( 4), it can be caused by the thrust drop. From an aerodynamic perspective, the generated rotor tip-vortices are induced by each other and are impinged by the propellers, which causes very complicated blade-vortex interaction and vortex-vortex interaction [14][15][16][17]. For i = 0.56 at Re = 0.89 × 10 5 , the FM (or hover efficiency) has a remarkable improvement of 12.14%.…”

Section: Resultsmentioning

confidence: 99%

Aerodynamic Performance of a Coaxial Hex-Rotor MAV in Hover

2021

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Micro aerial vehicles (MAVs) usually suffer from several challenges, not least of which are unsatisfactory hover efficiency and limited fly time. This paper discusses the aerodynamic characteristics of a novel Hex-rotor MAV with a coaxial rotor capable of providing higher thrust in a compact structure. To extend the endurance during hover, flow field analysis and aerodynamic performance optimization are conducted by both experiments and numerical simulations with different rotor spacing ratios (i = 0.56, 0.59, 0.63, 0.67, 0.71, 0.77, 0.83, 0.91). The measured parameters are thrust, power, and hover efficiency during the experiments. Retip ranged from 0.7 × 105 to 1.3 × 105 is also studied by Spalart–Allmaras simulations. The test results show that the MAV has the optimum aerodynamic performance at i = 0.56 with Retip = 0.85 × 105. Compared to the MAV with i = 0.98 for Retip = 0.85 × 105, thrust is increased by 5.18% with a reduced power of 3.8%, and hover efficiency is also improved by 12.14%. The simulated results indicate a weakness in inter-rotor interference with the increased rotor spacing. Additionally, the enlarged pressure difference, reduced turbulence, and weakened vortices are responsible for the aerodynamic improvement. This provides an alternative method for increasing the MAV fly time and offers inspiration for future structural design.

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Section: Resultsmentioning

confidence: 99%

Aerodynamic Performance of a Coaxial Hex-Rotor MAV in Hover

2021

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“…For the data set-up, 1170 pairs of particle image sequences, flow fields calculated from them using a conventional PIV algorithm (CC) and thrust forces corresponding to each flow field were prepared experimentally using a drone rotor. A detailed description of the experimental set-up is provided in Lee et al (2021). A particle image sequence consisted of two-channel images of 2048 × 2048 pixels, covering an area of 207 × 207 mm 2 , captured by a charge-coupled device camera with a time interval of 20 μs.…”

Section: Data Preparationmentioning

confidence: 99%

“…Image sharpening for the pixel-wise change in intensity was applied for image preprocessing, and polynomial subpixel interpolation was used to reduce errors and improve spatial accuracy. This method was applied in our previous studies (Chae et al 2019;Lee et al 2021) and has been proven to be accurate. The flow fields obtained using the above method were compared with those calculated using a commercial program (Dantec DynamicStudio) utilizing the same particle images for further validation.…”

Section: Data Preparationmentioning

confidence: 99%

“…2019; Lee et al. 2021) and has been proven to be accurate. The flow fields obtained using the above method were compared with those calculated using a commercial program (Dantec DynamicStudio) utilizing the same particle images for further validation.…”

Section: Data Preparationmentioning

confidence: 99%

“…A detailed description of the experimental set-up is provided in Lee et al. (2021). A particle image sequence consisted of two-channel images of 2048 × 2048 pixels, covering an area of 207 × 207 mm 2 , captured by a charge-coupled device camera with a time interval of 20 μs.…”

Section: Data Preparationmentioning

confidence: 99%

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Accurate prediction of the particle image velocimetry flow field and rotor thrust using deep learning

Lee

Son

et al. 2022

J. Fluid Mech.

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With particle image velocimetry (PIV), cross-correlation and optical flow methods have been mainly adopted to obtain the velocity field from particle images. In this study, a novel artificial intelligence (AI) architecture is proposed to predict an accurate flow field and drone rotor thrust from high-resolution particle images. As the ground truth, the flow fields past a high-speed drone rotor obtained from a fast Fourier transform-based cross-correlation algorithm were used along with the thrusts measured by a load cell. Two deep-learning models were developed, and for instantaneous flow-field prediction, a generative adversarial network (GAN) was employed for the first time. It is a spectral-norm-based residual conditional GAN translator that provides a stable adversarial training and high-quality flow generation. Its prediction accuracy is 97.21 % (coefficient of determination, or R2). Subsequently, a deep convolutional neural network was trained to predict the instantaneous rotor thrust from the flow field, and the model is the first AI architecture to predict the thrust. Based on an input of the generated flow field, the network had an R2 accuracy of 94.57 %. To understand the prediction pathways, the internal part of the model was investigated using a class activation map. The results showed that the model recognized the area receiving kinetic energy from the rotor and successfully made a prediction. The proposed architecture is accurate and nearly 600 times faster than the cross-correlation PIV method for real-world complex turbulent flows. In this study, the rotor thrust was calculated directly from the flow field using deep learning for the first time.

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