Autonomous Propulsion Design Optimization

This AI solution uses advanced machine learning and reinforcement learning to co-design and optimize propulsion systems for autonomous aerospace and defense platforms, from unmanned aircraft to multi-phase spacecraft trajectories. By rapidly exploring design spaces, mission profiles, and control strategies in simulation, it accelerates joint development programs, improves fuel efficiency and mission endurance, and reduces the cost and risk of propulsion R&D.

The Problem

“Autonomous co-design of propulsion + mission + control in simulation”

Organizations face these key challenges:

Design iterations take weeks/months due to CFD/FEA bottlenecks and manual parameter sweeps

Propulsion sizing and control tuning are done separately, causing late-stage integration failures

Hard to find feasible designs across many constraints (thermal, structural, fuel, acoustics, safety)

R&D cost/risk is high because only a small fraction of the design space is explored

Autonomous Propulsion Design Optimization

The Problem

Impact When Solved

The Shift

Technologies

Key Players

Real-World Use Cases

GE Aerospace & Shield AI Autonomous X-Bat Vehicle Propulsion Collaboration

GE Aerospace and Shield AI X-BAT Propulsion Collaboration

Multi-Phase Spacecraft Trajectory Optimization via Transformer-Based Reinforcement Learning