Ongoing PhD Projects
Jens Kristian Mikkelsen Gudichsen (PhD Candidate)
“Structural Dynamics of Robotic Cartesian Manipulators”
This project develops computationally efficient methods for modelling and identifying the configuration-dependent dynamics of large-scale robotic manipulators, with a focus on structural vibrations. It introduces:
- Physics-based modelling using dynamic substructuring for time-varying interfaces.
- Optimized experiment design and adaptive Bayesian identification to reduce measurement effort.
- Global identification schemes for accurate dynamic prediction under varying configurations.
The research advances strategies for precision, stability, and adaptability in next-generation automation systems.
This PhD project is performed in collaboration with SDU’s Centre for Large Structure Production (LSP).
Vijayasankar Irissappane (PhD Candidate)
“Digital twins of axial piston pumps for machine learning based condition monitoring”
Modern engineering systems are increasingly complex, making purely analytical models insufficient. Advances in sensing and data acquisition now enable powerful data-driven approaches. This project integrates physics-based models with machine learning to create Hybrid Digital Twins—virtual models that capture and propagate uncertainties from manufacturing, environment, and modelling. These twins will simulate diverse damage scenarios, generate synthetic datasets, and support advanced algorithms for Structural Health Monitoring (SHM) and Condition Monitoring (CM), improving reliability and predictive capabilities across large populations of structures.
This is an Industrial PhD Project performed in collaboration with Danfoss High Pressure Pumps and financed by Innovation Fund Denmark.
Casper Aaskov Drangsfeldt (PhD Candidate)
“A probabilistic approach for structural health monitoring of ship gearboxes under time-variant operating conditions”
This project focuses on improving the reliability and reducing maintenance costs of Crew Transfer Vessels (CTVs) used in offshore wind operations. Heavy usage and harsh conditions accelerate wear on engines, propulsion systems, and structural components. Using operational data, expert knowledge, and machine learning, the research aims to:
- Map CTV workflows and identify critical operating modes linked to deterioration.
- Develop models to predict component health under varying conditions.
- Propose operational strategies to extend vessel lifetime and optimize maintenance.
The project is conducted in collaboration with industry partners in the maritime sector and is part of the SDU Maritime Research Platform.
Jack Rahbek (PhD Candidate)
“Frequency-Based Fatigue Life Evaluation and Accelerated Testing of Agricultural Mower Structure”
Agricultural machines like mowers experience severe vibrations and cyclic loads during operation and transport, leading to fatigue failures. This project aims to develop efficient and accurate fatigue life prediction methods by combining frequency-domain fatigue evaluation with virtual sensing techniques. The research addresses challenges such as multiaxial and non-proportional stress states, sensor placement limitations, and harsh operating conditions. By leveraging dynamic model updating and accelerometer-based data, the project seeks to reduce large-scale testing and enable faster, more reliable design decisions for agricultural machinery.
This is an Industrial PhD project in collaboration with Kverneland Group Kerteminde A/S and funded by Innovation Fund Denmark
Ongoing Postdoctoral Projects
Hossein Soleimani (Postdoctoral researcher)
“Spectral Fatigue Analysis Using Stochastic Substructuring”
This project develops computationally efficient methods for fatigue life prediction in complex welded and manufactured structures under stochastic loading conditions. The approach combines:
- Spectral fatigue analysis to estimate stress spectra and fatigue damage under random excitations.
- Stochastic dynamic substructuring, enabling local-to-global modelling of large structures while accounting for uncertainties in materials, interfaces (welds, 3D-printed joints), and loading.
- Integration of forward physical modelling (stochastic FEM) and inverse identification techniques for design validation.
Applications include ship structures, offshore components, and advanced manufacturing systems, aiming to improve reliability and reduce computational cost in fatigue assessment.
This postdoctoral project is made in collaboration with SDU’s Centre for Large Structure Production and financed by IFD’s Grand Solutions grant “LSP Ship Factory”.
Visiting researchers
- Paola Michelle Guevara Alvarez (Visiting PhD Candidate)
- Josep Font Moré (Visiting PhD Candidate – Graduated Spring 2025)
Alumni
- Silas Sverre Christensen, “Vibration and strain monitoring of an offshore structure” (PhD Project, Completed 2020), “Investigation into offshore wind turbine vibrations” (Industrial Postdoc project, Completed 2022)
- Mikkel Løvenskjold Larsen, “A Novel Node Design using High Strength Steel for Jacket Structures” (Completed 2022)
- Nimai Domenico Bibbo, “Analytical Fatigue Life Assessment of a Full-Scale Wind Turbine Test Bench” (Finalized 2022)
- Jonas Gad Kjeld, “Methodology for determination of vibration damping of an offshore wind turbine supporting structure”, (Finalized 2021)
- Karsten K. Vesterholm, “Robust Identification of Modal Parameters of Nonlinear and Time Variant Systems” (Finalized 2021)
- Goran Jelicic, “System identification of aeroelastic parameter-varying systems using real-time operational modal analysis”, (Completed 2022)
- Tobias Pawlowitz, “Fatigue Strength of Welded Thin Steel Structures” (Completed 2021)