PhD scholarship in Diagnosis and Prognosis of Fatigue Damage in Steel Structures using Multi-Fidelity Models and Virtual Sensing - DTU Construct

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Testo originale dell'annuncio (in inglese)

The Section for Structures and Safety at DTU Civil and Mechanical Engineering Department is looking for an ambitious, motivated and skilled PhD student eager to dedicate three years to the diagnosis and prognosis of fatigue damage in steel structures, with emphasis on steel bridges. Over the past century, steel has been the backbone of structures and infrastructures. Much of this built stock is reaching or has exceeded its design lifetime while being exposed to harsher operational conditions than originally designed for. Fatigue, where repetitive loads initiate and propagate cracks, is a key challenge in this aging era, undermining the functionality, safety and longevity of critical steel infrastructure. There is an urgent need to advance monitoring, diagnosis and prognosis methods that can reliably assess fatigue damage and predict remaining lifetime, enabling proactive maintenance strategies that extend service life and reduce CO2 emissions. Through this PhD scholarship, you will contribute to the development of a multi-component framework for reliable and computationally efficient fatigue diagnosis and prognosis of steel structures. Building on the group's established expertise in virtual sensing and multi-fidelity modelling, your research will advance and integrate three core elements: (i) physics-based multi-fidelity structural models enabling high-resolution analysis at fatigue-prone hot-spots while maintaining computational efficiency through lower-fidelity simulation of non-critical regions, (ii) virtual sensing techniques for load and stress estimation from limited and optimally placed sensor data, and (iii) sophisticated fatigue analysis models combining advanced fracture mechanics with crack initiation and growth simulation. Additionally, you will explore fatigue mitigation strategies aimed at stress relaxation in fatigue-prone areas, minimizing material consumption and maximizing structural efficiency. The diagnostic and prognostic capabilities of the developed framework can be verified through simulated and/or laboratory-generated data. Responsibilities and qualifications Your primary responsibilities and tasks include: Development and advancement of multi-fidelity finite element models of steel structures with emphasis on fatigue hot-spot modelling, building on existing in-house methods Load and stress estimation using virtual sensing techniques (e.g., Kalman Filters) combined with multi-nature sensing data from optimally chosen locations Development and application of advanced fracture mechanics models for crack initiation, growth simulation and remaining fatigue life prediction Exploration of fatigue mitigation strategies for stress relaxation at fatigue-prone locations, with focus on material efficiency Integration of the above components into a coherent diagnostic and prognostic framework Preparation of scientific publications and effective presentation of research findings at international conferences and workshops Support of relevant teaching activities at DTU's Civil and Mechanical Engineering Department Co-supervision of BSc and MSc projects complementary to this PhD project Candidate Profile You must have a two-year master's degree (120 ECTS points) or a similar degree with an academic level equivalent to a two-year master's degree. You should be a motivated candidate with a background in Civil or Mechanical Engineering or other related engineering fields. Additionally, you should be equipped with the following qualifications and technical skills: Solid background in Structural Dynamics and Finite Element Modelling. Knowledge of or strong interest in Fracture Mechanics and fatigue analysis. Experience in coding (e.g., Python) and in the use of structural analysis software (e.g., Abaqus, OpenSees) is desirable. Familiarity with virtual sensing techniques, state estimation methods or Kalman Filters is advantageous. Ability to work independently and take initiative in planning and executing computational re