结构健康监测博士职位

Thermoplastic composites are widely regarded as promising materials for the next generation of commercial aircraft, combining excellent mechanical performance with low weight. In addition, their melt-processable matrix enables automated, high-rate manufacturing of components that can subsequently be assembled into complex aerostructures using welding. This provides significant opportunities for more efficient and cost-effective aircraft manufacturing.

However, welded composite assemblies are challenging to inspect using conventional non-destructive inspection techniques. As a result, larger safety margins are often required in structural design, leading to heavier structures, while maintenance intervals may be more conservative than necessary. Developing reliable methods to continuously assess the structural condition therefore enable both lighter designs and more efficient maintenance strategies.

To address this challenge, the project aims to develop structural health monitoring technologies based on a digital twin . The digital twin will combine information from the physical structure with models and monitoring data to assess its current structural state and predict its remaining lifetime. This will enable the condition of welded structures to be monitored throughout their service life, allowing maintenance to be planned when it is actually needed rather than according to predetermined intervals. Ultimately, this approach aims to contribute to lighter, safer, and more sustainable aircraft structures .

The PhD positions

Experimental characterization and digital twin development A key challenge in developing a reliable digital twin is accurately characterizing the static and fatigue behaviour of welded thermoplastic composite joints. Since there are currently no well-established standards for fatigue testing of these joints, the project will involve developing experimental methods to reliably characterize their mechanical performance and damage evolution.

The experimental results will be used to develop constitutive models for the welded interface. In particular, these models should describe progressive interfacial damage development as a function of fatigue loading. The models will be implemented and validated in commercial finite element (FE) software. The resulting FE model will define the digital twin of the welded structure and provide the basis for the second PhD project, which will use the digital twin together with monitoring data to develop prognostic structural health monitoring strategies.

In this project you will: • Perform experimental characterization of the static and fatigue performance of welded thermoplastic composite structures. • Develop constitutive models that accurately describe the performance of the welded interface. • Implement the developed models in commercial FE simulation software for the development of a digital twin and validate their accuracy against experiments.

We are looking for a colleague who has experience in mechanical experimentation of the fatigue behavior of composite materials and/or polymers, and is able to develop and implement constitutive models in FE software.

Development of a Structural Health Monitoring system The ability to estimate the current state of the welded thermoplastic composite joint and the development of this state over time, is of decisive importance for lifetime performance modelling. The key challenges are the robust integration of a sensor system in the structure and the analysis of measured signals, which are typically strongly affected by environmental and operational conditions. This project aims to tackle these challenges by using piezo-electric and/or optical fiber based sensor system, combined with physics informed data analysis method, exploiting the digital twin model developed by the first PhD project.

In this project you will: • Implement an effective sensor integration method for welded thermoplastic composite structures, using piezo-electric sensors, fiber optical sensor, or a combination of both. • Perform dynamic experiments of pristine and (gradually) damaged structures to collect data for the state estimation methods. • Develop signal processing methods to estimate, enriched by physics-based information, the current state of the welded structure and its development under fatigue loading.

We are looking for a colleague who has experience in sensor integration and dynamic experimentation, knowledge of piezo-electric or optical fiber based measurements, and proficiency in signal processing methods enriched with physics-based information.