Doctoral School

National Technical University of Athens (Athens, GR)


National Technical University of Athens (Athens, GR)
TechneValue (Bäch, CH)


Christos Karliampas

Early Stage Researcher 06

My name is Christos and I am a 28 years old mechanical engineer from Greece. I obtained my MSc degree in aeronautical engineering from Politecnico di Milano on April 2021, but soon I decided to make a leap to apply my knowledge in more humanistic projects and MeDiTATe training network constituted a great opportunity to accomplish it. During my studies I believe I acquired a holistic view of modeling techniques required to address problems in flexible dynamic structures, specifically the need for stochastic response analysis. Thus, collaborating with specialized clinical institutions, I want to contribute to the challenging attempt to correlate data from medical imaging with the hemodynamic CFD sensitivity analysis and hypothesize on the progress of an aneurysm. I hope to facilitate them with user-friendly tools and put my effort so in future personalized medical trials will become a routine procedure to save patients life.
I am a PhD student at the National Technical University of Athens since September 2020, under prof. Kyriakos Giannakoglou supervision, while my second half I will be working with Dr. Mauro Odino in Technevalue GmbH. The goal of my project is to identify critical hemodynamic parameters related to the stabilization or the progression of an aneurysm. It also focuses on sensitivity studies and, in specific, uncertainty quantification. Regarding uncertainty quantification, I am developing a non-intrusive Polynomial Chaos Expansion (niPCE) method used for geometric or physiological quantities of interest characterizing the flow inside the aneurysm. Further, I am investigating and developing a Reduced Order Model (ROM) to substitute the computationally expensive CFD-CSM solvers and be used to evaluate the response on the niPCE database. Lastly, I am using CAD-free techniques to account the vessel movement as well as its mesh adaptation.
This project has received funding from the European Union’s Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant agreement No 859836
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