ASSURE – Evaluating Airborne Collision Severity: A Review of the Task A43 Engine Ingestion Report

Why It Matters

Validating computational models against real‑world ingestion data gives manufacturers a cost‑effective way to certify engine resilience against the growing drone threat, influencing safety standards and regulatory oversight.

Key Takeaways

• Live ingestion test used CFM56‑7B engine and DJI Phantom 3 drone.
• Impact speed 92.6 m/s (180 knots) simulated severe take‑off collision.
• LS‑DYNA simulations matched physical damage, confirming severity level 3.
• Open representative fan model performed like proprietary CFM56‑7B engine.
• Validated model offers non‑proprietary tool for UAS safety testing.

Pulse Analysis

The surge in recreational and commercial drones has outpaced existing aviation safety frameworks, prompting regulators to seek data‑driven solutions for foreign‑object ingestion. Traditional certification tests focus on birds or ice, but modern UAS components—dense lithium‑polymer batteries and rigid motors—pose distinct impact dynamics. By executing a controlled ingestion of a DJI Phantom 3 into a CFM56‑7B fan, researchers captured high‑speed video, strain data, and post‑impact blade deformation, creating a rare empirical baseline for future risk assessments.

The core achievement of Task A43 lies in its rigorous validation of LS‑DYNA finite‑element simulations against the live test. The computational model reproduced blade‑impact locations, damage patterns, and resultant imbalance within the engine’s certification envelope, despite the physical test’s fireball that software cannot replicate. Moreover, the study demonstrated that an open representative fan assembly—devoid of proprietary geometry—behaves indistinguishably from the actual CFM56‑7B, offering the industry a universally accessible benchmark for simulation calibration.

For aircraft manufacturers, UAS developers, and regulators, this validated modeling approach streamlines safety testing, reduces reliance on costly full‑scale experiments, and accelerates certification timelines. It also equips the FAA and international bodies with a scientifically sound basis to update airspace rules and engine certification criteria as drone traffic intensifies. Ultimately, the open‑source model fosters collaborative risk mitigation, enabling stakeholders to anticipate and mitigate drone‑induced engine damage before it jeopardizes commercial operations.