Urban Wake-Field Generation Using Large-Eddy Simulation for Application to Quadrotor Flight

Sutherland, M.; Etele, Jason; Fusina, Giovanni

doi:10.2514/1.c033624

Cited by 8 publications

(5 citation statements)

References 27 publications

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“…To facilitate simulating longer flights and feedback-controlled UAS motion, a common approach is to simulate the urban wind field first (without the UAS) and then store the pre-computed wind field in memory to be queried at runtime as a UAS vehicle moves through the simulated environment. In the latter approach, local changes in wind velocity affect aerodynamic and thrust forces according to first-principles models [18][19][20]. CFD-based wind vehicle simulators can also capture the effect of wind velocity gradients over the span of the vehicle by using the velocitypoint method [21].…”

Section: Related Workmentioning

confidence: 99%

Quadrotor Flight Simulation in a CFD-generated Urban Wind Field

Kakavitsas,

Willis,

Jacobik

et al. 2024

Preprint

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This paper presents a software pipeline that enables simulating a quadrotor’s flight in realistic urban wind fields where complex wind phenomena are common and have significant impact on vehicle dynamics. The pipeline integrates the OpenStreetMap database for obtaining real-world building geometry, the OpenFOAM computational fluid dynamics (CFD) solver for computing a three-dimensional, steady, wind field, the Gazebo robotics simulation environment, and the PX4 software-in-the-loop autopilot. A 3D wind plugin is developed to interpolate a pre-computed CFD wind field at runtime during the simulation. The approach is demonstrated by comparing the flight performance of a quadrotor flying over a university campus environment with and without the wind field.

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Section: Related Workmentioning

confidence: 99%

Quadrotor Flight Simulation in a CFD-generated Urban Wind Field

Kakavitsas,

Willis,

Jacobik

et al. 2024

Preprint

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“…Meanwhile, with the use of Unmanned Aerial Systems (UAS) becoming more prevalent around the world, wind field within urban environments becomes more significant and several studies have examined its impact on UAS flight performance. Sutherland et al [10] studied the UAS flight controller ability to maintain the UAS within its assigned position or path amidst transient wind flow. From the obtained time-averaged solution of CFD simulation [11], Watkins et al [12] focused on the change in flow velocity magnitude and angles due to the shear flow over a building's roof.…”

Section: Introductionmentioning

confidence: 99%

Simulation of Wind Field in a Building Complex for Evaluation of the Wind Effect Along UAS Flight Path

Nathanael

Wang

Low

2023

AIAA AVIATION 2023 Forum

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With the use of Unmanned Aerial Systems (UAS) becoming more common, wind field within urban environments becomes significant, and its impact on UAS flight should be examined to ensure UAS operations can be implemented and managed safely with little to no risk of damages and injuries to the inhabitants and infrastructures. This study presents the wind field within the Biopolis building complex in Singapore from steady-state RANS simulation using OpenFOAM v1912. The wind simulation was done using various inlet wind speeds and directions based on the measurements from a weather station near the building complex. After obtaining the velocity field for the whole area, the wind effect on UAS along a given flight path was inferred from the local wind velocity magnitude and direction along the path. When the general wind heading was aligned with the flight path, the local wind and its impact on the UAS along its path had more gradual and less extreme changes than in other cases where the UAS had to fly by crossing through the wake flow of the buildings. Additionally, for flow cases with the same inlet wind direction but different reference speeds, similarity in their results was observed when the wind velocity magnitude was normalized with respect to the velocity magnitude at the inlet boundary. Contours of the wind Turbulence Kinetic Energy (TKE) from the simulation results show that the areas with higher TKE are located around buildings at the most upstream against the wind, and these areas increase in size and TKE levels with higher wind speed.

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“…The first data point of the set identified in Table 3.16 and Figure 3.5, that the UAT was immersed in, was Point 7 with wind direction WSW as was mentioned in Table 3. 16 and shown in Figure 3. 5.…”

Section: Results For Point 7 Wswmentioning

confidence: 99%

“…Understanding a UAT's stability performance in the context of its unique operating environment is a key aspect of determining the required standards to certify so that the risk to public safety is minimized. To better understand these wind environments, several contributions have been documented around the development of simulated urban airflow environments such as Orr et Al [15], Sutherland et Al [16],…”

Section: The Urban Airflow Environmentmentioning

confidence: 99%

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Analysis of Urban Air Taxi Inner Loop Controller Architectures Subject to Noisy Inertial Measurement Unit Input while Immersed in Empirically-Developed Urban Airflow Disturbances

Maksoud

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Interest in designing and operating commercial Urban Air Taxis (UAT) around the world has grown exponentially over the last decade. One of the many challenges that are unique to UATs is the urban airflow environment in which they are envisaged to operate in. The presence of high-rise buildings creates a highly turbulent urban airflow environment which requires UATs operating within it to be equipped with highly robust inner-loop controllers to maintain steady level flight. This study investigates the effect of Angular Random Walk (ARW) noise in four grades of Inertial Measurement Units (IMU) on the inner loop flight controller's ability to maintain steady level flight. Three types of control schema are examined, namely classical Proportional Integral Derivative (PID) control, Active Disturbance Rejection Control (ADRC), and Linear Active Disturbance Rejection Control (LADRC). Each control schema has a different method of overcoming ARW noise found in the IMU readings which impacts its performance. A total of three metrics are used to compare the performance of the different controllers: 1) command tracking performance; 2) control surface deflections; and 3) passenger comfort. The results of this study show promising potential for using ADRC-based control architecture in UAT inner-loop control applications by exhibting superior command tracking performance, lower control surface deflections, and increased passenger comfort compared to PID. It also provides insights to regulatory authorites and UAT manufacturers of the importance of accounting for IMU sensor noise and recommended IMU grades to be used in UAT applications. Finally, iv I would like to thank my family for their patience and support while I completed this work. To my parents who constantly supported my endeavor to complete this work, my wife who took on the extra load to free up my time, my brother who would make the 5-hour drive to Ottawa to help out with chores when deadlines were approaching, thank you for all you have done for me. To my four young children who never got bored of asking me whether I was done so I could play, I can now say that my work here is done and I now have time to play. It is my hope that my endeavor in completing this work serves as a source of motivation to you in the future that with dedication, determination and the right support you can achieve what may at first glance appear to be too difficult and out of reach. A special thank you to my co-supervisors Prof. Khouli and Prof. Atia for your constant support, motivation and patience when things weren't going in the right direction. Thank you to Dr. Wall and the extended research team at the NRC, RWDI and RMC for the opportunity to present my progress and the usefull feedback you provided throughout this work.

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Urban Wake-Field Generation Using Large-Eddy Simulation for Application to Quadrotor Flight

Cited by 8 publications

References 27 publications

Quadrotor Flight Simulation in a CFD-generated Urban Wind Field

Quadrotor Flight Simulation in a CFD-generated Urban Wind Field

Simulation of Wind Field in a Building Complex for Evaluation of the Wind Effect Along UAS Flight Path

Analysis of Urban Air Taxi Inner Loop Controller Architectures Subject to Noisy Inertial Measurement Unit Input while Immersed in Empirically-Developed Urban Airflow Disturbances

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