AI-Driven Fatigue and Crack Growth Prediction for Aircraft Structures under Variable Loading

Problem addressed:
Aircraft structures such as wings and fuselage panels are subjected to complex variable-amplitude loading during flight operations, including take-off, turbulence, and landing cycles. These conditions lead to fatigue damage accumulation and crack propagation, which are difficult to predict accurately using traditional approaches. Existing methods often rely on conservative assumptions or computationally expensive simulations, limiting their applicability for real-time decision-making and maintenance optimization.

Proposed solution:
This project aims to develop a hybrid computational framework combining:

• Fracture mechanics-based crack growth modeling (Paris law and extensions)

• High-fidelity numerical methods (FEM/BEM) for stress field evaluation

• Physics-informed neural networks (PINNs)

• Surrogate models for fast and scalable prediction

The objective is to enable accurate and efficient prediction of fatigue life and remaining useful life (RUL) under realistic flight load spectra.

Key applications:

• Structural Health Monitoring (SHM) systems

• Digital twins for aircraft structures

• Predictive and condition-based maintenance

• Life extension of critical structural components

Expected impact (industrial and economic):

• Reduction in maintenance and inspection costs

• Improved safety and reliability of aircraft operations

• Optimization of maintenance scheduling

• Support for next-generation intelligent aerospace systems

Collaboration sought:

• European research groups in computational mechanics, AI, and aerospace engineering

• Industrial partners (aircraft manufacturers, MRO companies, aviation technology providers)

• MSCA Postdoctoral candidates interested in applied structural mechanics and AI

What I offer:

• Expertise in computational mechanics, structural optimization, and fracture modeling

• Experience in advanced materials and nonlinear structural behavior

• Integration of physics-based models with AI for engineering applications