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About
Background
Reza Norouzikudiani obtained his Bachelor’s degree in Mechanical Engineering from Shiraz University in Iran. Also, He graduated from the University of Tehran, Iran, with his Master Degree in the field of Applied Mechanics. During his master project, he worked on the Inspection of Nonpermanent Joints of Homogeneous Plates Using Guided Waves propagation. In this project, transmission of guided waves through bolted lap joints was examined experimentally and numerically (Simulation in FE software).
He joined STORM-BOTS in October 2021 as ESR6 at the Scuola Superiore Sant’Anna (Pisa, Italy) and is working on the development of multi-physics models for LCEs, and their implementation in numerical codes based on the Finite Element Method.
Research Interests
Liquid Crystal Elastomers, Computational Mechanics, Finite Element Analysis, Smart Materials and Structures
Research project: ESR 6 – Finite element models for dynamical actuation of Liquid Crystal sheets
Advanced 3D printing techniques allow to fabricate liquid crystal elastomers (LCEs) sheets that undergo programmed, transient shape changes upon activation using diverse external stimuli (light, electro-magnetic fields). 4D printing, i.e., design and control of devices based on actuation of 3D-printed LCEs sheets, requires reliable models for the time-dependent response of these materials.
The ESR project targets the development of multi-physics models for LCEs, the derivation of reduced models for thin sheets (membranes, plates, shells), and their implementation in numerical codes based on the Finite Element Method.
The selected ESR will be hired by SSSA and will develop her/his activity at The BioRobotics Institute of Scuola Superiore Sant’Anna (Pisa, Italy), performing secondments at EXACT Lab (Trieste, Italy), at Technical University of Eindhoven (The Netherlands) and at the Max Planck Institute for Intelligent Systems in Stuttgart (Germany).
PhD programme – University: PhD in BioRobotics – Scuola Superiore Sant’Anna
Project's dissemination
Secondments
This project has received funding from the European Union’s Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant agreement No 956150
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