Extending Quadcopter Drone Flight Time and Range with CFD Simulation

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There are two main categories of aerial drones: rotorcraft capable of vertical take-off and landing (VTOLs) and fixed-wing vehicles. Rotorcraft drones offer essential advantages over fixed-wing systems, as they can hover (maintain a constant altitude) and are generally easier to control and operate. However, multi-copters also have inherent shortcomings, the most important being their limited flight time and range. Even modern and innovative electric drones have a limited flight time of around 20-30 minutes, depending on flight conditions. Very few in the high-end class reach flight times of close to one hour.

The application of computational fluid dynamics (CFD) can help significantly improve the efficiency of drones and extend their flight time and range. In this article, we demonstrate how, through an example: the aerodynamic simulation and optimization of an industrial drone in hover mode, the most energy-intensive mode of this type of drone.

Quadcopter Geometry

We selected one of the most widely used rotorcraft drone configurations today: the quadcopter. Drone manufacturers for the private consumer sector (amateur video shooting, racing drones, drones for kids, etc.) predominantly rely on this type of configuration.

Drone CAD files were provided by Mr. Monasor and Dr. Weerasinghe from the University of the West of England.

The propeller blade was modeled with Fidelity’s parametric modelers, taking into account the required thrust. Multiple sections were extracted from the original geometry and stacked together to build the 3D blade. An appropriate twist distribution was provided to ensure the parametrized blade resembled the original geometry as closely as possible.

The setup benefited from the symmetry of this drone geometry: only one-fourth of the drone needed to be included in the computational domain, hence only one arm.

The chosen domain definition represented a practical case, corresponding to “free air” simulation at a hovering altitude high enough to neglect any ground effect.

Structured and Unstructured Meshing Combined

Due to the complexity of the drone domain, an unstructured mesh was generated with Fidelity Automesh, which automatically refines the mesh near high curvature areas and edges, thus minimizing user interaction and engineering time. This leads to a high-quality mesh sufficiently robust to be used for optimization.

For the propeller blade, a multiblock structured mesh was generated using Fidelity’s wizard-type approach, which makes it very easy and fast to generate high-quality structured meshes with multiple grid levels. A variable tip gap was applied, and a matching periodic connection between two periodic faces was automatically computed. Combined with the fact that we only needed to mesh one blade, this matching connection led to a two-fold cell count reduction and faster simulation speed.

Within Fidelity Automesh, structured and unstructured meshes can be combined and run in the same computation, so the user can take advantage of both the velocity of structured meshes and the robustness of unstructured ones without needing to tune any solver settings. This also reduces RAM and disk consumption.

Rotating block (in grey) and volume mesh around the quadcopter airframe

Steady and Unsteady CFD Simulation

Both steady and unsteady simulations can be run in Fidelity Flow. We set the propeller to rotate at 5,000 RPM, and the drone arm was stationary. To predict flow turbulence, we used the Spalart-Allmaras model. A mixing-plane interface was used for the steady simulation. For the unsteady non-linear harmonic (NLH) simulation, a specific treatment based on Fourier decomposition was applied that provides domain scaling with a computational cost similar to that of a mixing plane.

The NLH method provides unsteady flow results with considerably fewer constraints than the domain scaling and phase-lagged methods. For this project, one harmonic per domain was added to capture the unsteady perturbation in the domain.

The simulation results revealed strong unsteady features in the flow field. Pressure distribution on the airframe showed to be largely impacted by the instantaneous position of the propeller, and the velocity field around the drone was subject to strong periodic oscillations linked to rotor rotation.

  

Threshold field contour - left: averaged axial velocity with steady (mixing plane) approach and right: instantaneous axial velocity with unsteady (Nonlinear Harmonic Method) approach

A comparison of the results showed that while steady simulation can provide a good representation of the mean flow field, only NLH analysis provides accurate information about the unsteadiness of the flow field, offering valuable data in terms of unsteady flow physics, blade and airframe loading, as well as blade tip vortex and bluff body recirculation dynamics, at a cost comparable to a steady simulation.

Drone Design Optimization

Fidelity offers multiple possibilities for design parametrization and optimization. Available optimization methods range from single-objective optimization to multi-objective and robust design optimization (RDO) that takes into account operational and manufacturing uncertainties. A drone optimization process can benefit from all these methods. The final choice of the technique depends mainly on expected operation modes.

The below image shows an example of geometry parameterization. The propeller geometry was parameterized on CAD model level, and its angle of attack at three spanwise sections was taken as a design variable. Each geometry was automatically re-meshed in Fidelity Automesh. The drone arm was parameterized using three morphing vectors, which enabled optimization of the shape of the drone through morphing while also satisfying multiple constraints that were applied to ensure feasible designs.

Cadence’s optimization routines are based on gradient-free algorithms that are much more efficient than gradient-based optimization for complex multi-component systems such as drones. The employed optimization processes benefit from a tremendous speed-up thanks to built-in surrogate models or artificial neural networks. Underlying evolutionary and genetic algorithms ensure an optimum converged solution in terms of defined objectives, such as flight time maximization. The practical use of such algorithms confirms that they can lead to novel, innovative, and sometimes even unexpected optimum system designs.

Summary

The case study demonstrates the powerful capabilities of Fidelity for drone CFD simulation. Seamlessly combined structured and unstructured meshing, fast, high-accuracy unsteady simulations with NLH and fully automated optimization tools based on efficient evolutionary algorithms, parameterization, and morphing, ensure a fast and robust workflow and an optimum design result for the defined objectives: maximizing flight time and range.

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