SOURCE: AFI

The Technology Development Fund (TDF) under the Defence Research and Development Organisation (DRDO) has initiated the ‘Dare to Dream 5.0’ Challenge specifically for the Individual Category, calling for innovative solutions in the realm of rocket motor design using Machine Learning (ML) and Artificial Intelligence (AI). This challenge focuses on enhancing the design, health monitoring, and service life prediction of Solid Rocket Motors (SRMs), which are crucial for various missile systems.
Solid rocket motors are designed for long-term storage and are meant for one-time use, making the assessment of their longevity and reliability paramount. The propellant used in these motors is a particulate composite material that exhibits viscoelastic behavior, meaning its mechanical properties change with both time and temperature, particularly under the stresses of thermal cycling and operational chamber pressure.
The typical failure modes for SRMs include:
- Surface Cracks: Cracks that appear on the surface of the propellant grain.
- Particle Cracking: Fractures within the propellant’s composite particles.
- De-wetting: Loss of adhesion between propellant particles.
- Propellant Tearing due to Creep: Gradual deformation under sustained load.
- De-bonding at Propellant-Insulation Interface: Separation between propellant and the insulation layer.
- De-bonding at Insulation-Case Interface: Separation of the insulation from the motor case.
These challenges highlight the need for predictive models that can accurately forecast the safety margins of SRMs over time, an area where traditional methodologies have limitations.
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