Lead Researcher: Dr Mauro S. Innocente
Researcher: Paolo Grasso
1) Self-Organising Swarm of Massless Firefighting Drones
- Mass-less drones self-organised based on Particle Swarm algorithm adapted to individuals operating within physical dynamic environments of high severity and frequency of change.
- Realistic Fire Propagation Model, though only enthalpy balance and chemical species balance are considered at this stage for efficiency.
- Includes combustion, 2D diffusion, pseudo-3D radiation, pseudo-3D convection (though no conservation of momentum).
- A given ignition temperature triggers a fire, which consumes “forest fuel”.
- Combustion does not take place if temperature is below ignition or “forest fuel” has been depleted.
- Fire propagation model solved using centred finite differences in space and the general Runge-Kutta method in time.
- When a drone releases a water payload drop, the nodal temperature decreases reaching a value that results from a mass-based weighted average of the temperatures of the payload and of the node.
- Drone docking stations located at the top left corner in the form of a small grid (one specific location assigned to each drone with a 1-metre gap between them).
- Water payload source located at the bottom left corner in the form of a small grid (one specific location assigned to each drone with a 1-metre gap between them). Locations of docking stations and water payload sources coincide.
- Battery consumption measured by distance travelled.
- Excluding visualisations, this autonomous firefighting system simulation runs faster than real-time using a standard PC (including fire propagation).
Example of initial swarm of 100 drones