Characterisation and extension of a rigid body dynamics solver coupled with OpenFOAM for flight performance analysis of flapping-wing drones

TL;DR

This work addresses the challenge of high-fidelity CFD–rigid-body coupling for flapping-wing drones by extending OpenFOAM's articulated-body framework to support active joint actuation through an imposedMotion library and an extended ABA (eABA). The approach enables prescribing wing kinematics while solving body dynamics, using overset grids and LES to capture unsteady aerodynamics. Thorough verification is provided via a vacuum double-pendulum benchmark and comparisons with quasi-steady models, showing good agreement in forces, trajectories, and dynamics for both a single-wing and a body–wing drone; parametric analyses confirm grid- and time-step robustness, with overset updates identified as the main computational bottleneck. The framework promises a versatile, open-source platform for free-flight multibody CFD studies and can be extended to coupled multiphysics with structural solvers for broader aerospace and bio-inspired applications.

Abstract

The extraordinary aerial agility of hummingbirds and insects continues to inspire the design of flapping-wing drones. To replicate and analyze such flight, computational fluid dynamics (CFD) simulations that couple flow solvers with rigid body dynamics are essential. While OpenFOAM offers tools for these multiphysics simulations, two key limitations remain: (1) a lack of thorough verification and performance characterization, and (2) the reliance on torque-based control for wing motion, which is impractical for parametric studies and real-time control. The developments are tested with a four and a five degrees of freedom flapping-wing drone equipped with a rigid, semi-elliptical wing. Ascending flight motions are simulated using the overset method, a moving background grid, and an LES model. Parametric studies demonstrate the independence of the grid and integration schemes, while profiling analyses identify the overset method as the computational bottleneck. The drone trajectories are compared with those from a literature quasi-steady model, and the body-wing interaction is analyzed in detail.

Figures (19)

• Figure 1: Diagram representing the main operations performed by (1) the rigid body dynamic solver relying on the ABA algorithm, (2) the dynamic mesh method relying on the overset method, and (3) the flow solver using pimple loops.
• Figure 2: Schematic of the kinematic tree of a multibody system with four links and four joints, illustrating the articulated body concept.
• Figure 3: (a) Kinematic tree of the flapping-wing drone, showing its four degrees of freedom. (b) Top view and (c) side view of the wing, defining the flapping angle ($\phi$) and pitching angle ($\alpha$).
• Figure 4: Kinematic tree of the drone consisting of a spherical body and a wing with 5 degrees of freedom.
• Figure 5: (a) Background grid shown in transparent mode with the component grid of the spherical body and the wing. (b) A closer view of the component grids.