Moshman Research Optimizes Semi-Autonomous Unmanned Aerial Fire Suppression System
Source: ANSYS Website
Moshman Research partner Avigation is developing a heavy-lift semi-autonomous unmanned aerial vehicle (UAV) for rapid wildfire response, the Hellbender Wildland Fire Response System. The system features a mountaintop-launched aerial platform design with a sheet spray misting system to enable rapid response and optimal suppression. Using simulation to optimize thrust efficiency of the fan blades, Moshman (in support of Avigation) hoped to maximize system operation time for better fire containment that leads to less destruction of forests, property damage, and loss of life.
For Moshman, the challenge was to perform a proof-of-concept shape optimization of a ducted fan blade primarily using computational fluid dynamics (CFD) to demonstrate improved performance at a relevant operating point. It was a task involving a large model that included tens of millions of elements. Even with a simplification of the full geometry, modeling was still done in three dimensions, adding another layer of complexity. This resulted in hours-long runtimes on local machines punctuated with stops and starts during analysis, which was detrimental to maintaining workflow. Additionally, the team needed to consider adjoint solve time, deformation settings, and degrading mesh quality over design iterations during shape morphing to fully optimize the efficiency and performance related to fan blade tip shape and curvature.