The computational fluid dynamic modeling of downwash flow field for a six-rotor UAV

Zheng, Yongjun; Yang, Shaoqiong; Liu, Xingxing; Wang, Jie; Norton, Tomás; Chen, Jian; Yu, Tan

doi:10.15302/j-fase-2018216

Cited by 37 publications

(40 citation statements)

References 12 publications

(12 reference statements)

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“…In terms of China Agricultural University, the team containing Zheng and Yang simulated the downwash flowing field of a type of six-rotor UAV, JF 01-10. The features and characteristics of the downwash field were obtained [59] , but the computation cost was high and further validation is needed to examine the models. Then, they conducted a qualitative test to try to verify the distribution of an eight-rotor downwash.…”

Section: Examination By Using Cfdmentioning

confidence: 99%

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Research status and trends of downwash airflow of spray UAVs in agriculture

Yang

Zheng

Liu

2019

International Journal of Precision Agricultural Aviation

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Downwash airflow of spray UAVs in agriculture has been gradually focused on and a few studies have already been involved in it. Thus, this paper firstly reviewed the current research of downwash airflow of UAVs for agricultural spray. It is found that Chinese scholars have published the majority of the studies in this area. Furthermore, two aspects of this kind of research, one, the work related to the caused results of UAV downwash acting on spray, the other, that regarding downwash itself for investigation, were analysed. It is illustrated that the present research usually uses water-sensitive paper, electronic devices, like wind-speed arrays and PIV, and CFD simulation. However, the existed electronic devices have some limitations, such as lacking of the ability of showing and demonstration of downwash in big enough areas in detail during recording. Moreover, the studies directly related to downwash have been just launched around the world and the examination for multi-rotor UAVs is even seriously not enough. Therefore, in terms of research trends, it is significant to develop specialised sensors for downwash measurement. The general and reliable CFD models of a class of UAVs are also needed. In addition, the combination of UAV downwash with real applications and actual production is important for the clarification of the details in different situations.

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Section: Examination By Using Cfdmentioning

confidence: 99%

“…The downwash of a single-rotor UAV [33] b. The downwash of a six-rotor UAV [59] Figure 9 The examples of the CFD models of several types of UAVs 3 Research trends of downwash airflow of the UAVs for spray…”

Section: Examination By Using Cfdmentioning

confidence: 99%

Research status and trends of downwash airflow of spray UAVs in agriculture

Yang

Zheng

Liu

2019

International Journal of Precision Agricultural Aviation

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“…In regards to the choice of sUAS flight platform, and optimal integration of sensor to the platform, the effects of the flow field of the sUAS should be further considered. In particular, the effect of the downwash caused by the rotating rotor blades [35,36] can distort the shape (concentration field) of the methane plume, which could then complicate the use of algorithms to infer source mass flow based on the measured flow field. Indeed, future research will focus on inferring mass-flow (and source location) from the sUAS methane concentration maps and wind data [15,[37][38][39].…”

Section: Discussionmentioning

confidence: 99%

Cavity Ring-Down Methane Sensor for Small Unmanned Aerial Systems

Martı́nez

Miller

Yalin

2020

Sensors

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We present the development, integration, and testing of an open-path cavity ring-down spectroscopy (CRDS) methane sensor for deployment on small unmanned aerial systems (sUAS). The open-path configuration used here (without pump or flow-cell) enables a low mass (4 kg) and low power (12 W) instrument that can be readily integrated to sUAS, defined here as having all-up mass of <25 kg. The instrument uses a compact telecom style laser at 1651 nm (near-infrared) and a linear 2-mirror high-finesse cavity. We show test results of flying the sensor on a DJI Matrice 600 hexacopter sUAS. The high sensitivity of the CRDS method allows sensitive methane detection with a precision of ~10–30 ppb demonstrated for actual flight conditions. A controlled release setup, where known mass flows are delivered, was used to simulate point-source methane emissions. Examples of methane plume detection from flight tests suggest that isolated plumes from sources with a mass flow as low as ~0.005 g/s can be detected. The sUAS sensor should have utility for emissions monitoring and quantification from natural gas infrastructure. To the best of our knowledge, it is also the first CRDS sensor directly deployed onboard an sUAS.

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“…ANSYS was used to develop the CFD models of both the real-used UAV and the scaled-down drone. In terms of the real-used UAV CFD models, Figure 6 shows the CFD results of the real-used UAV by the previous study [23] . Mesh in Workbench was used for meshing and Fluent in Workbench was used as the solver.…”

Section: Cfd Models By Using Ansysmentioning

confidence: 99%

Model migration for CFD and verification of a six-rotor UAV downwash

Yang

Tang

Zheng

et al. 2020

International Journal of Agricultural and Biological Engineering

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Currently, Computational Fluid Dynamics (CFD) has been used to investigate agricultural UAV downwash. However, the validations of CFD models are difficult to deal with. Current verification methods are to use either water-sensitive papers or wind-speed arrays, which could get wind distribution or speed only. In this study, model migration was used to develop and verify downwash CFD models. The basic idea is to try to use the results of a scaled-down drone to represent that of a real-used UAV. The CFD models of both a real-used six-rotor UAV, JF01-10, and a 1:10 scaled-down small drone were developed by ANSYS. Then, the scaled-down drone was utilized to conduct trials by particle image velocimetry (PIV), so that not only distribution and speed but also flowing direction of downwash could be obtained. Results indicated the relative error between the PIV tests and the CFD models of the small UAV was less than 12%, while that between the tests and the CFD models of JF01-10 was less than 34%. It could be indicated that model migration could reflect multiple downwash characteristics but should be optimized in some complex details. This study was a preliminary but fundamental attempt to investigate CFD modelling and validation of agricultural UAVs and provided a novel thinking of downwash verification.

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The computational fluid dynamic modeling of downwash flow field for a six-rotor UAV

Cited by 37 publications

References 12 publications

Research status and trends of downwash airflow of spray UAVs in agriculture

Research status and trends of downwash airflow of spray UAVs in agriculture

Cavity Ring-Down Methane Sensor for Small Unmanned Aerial Systems

Model migration for CFD and verification of a six-rotor UAV downwash

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