14 highly motivated PhD candidates have now been recruited to a range of positions across Europe requiring skills in; structural dynamics, system identification, verification and validation, uncertainty quantification & propagation, joint modelling, modal analysis, Bayesian statistics, real-time dynamic substructuring and modelling wind turbine blades. The successful candidates will be employed as Early Stage Researchers (ESR) in for 3 years and will receive a highly competitive salary.
Recruited Early Stage Researchers
ESR 1- Atmaram Muraleedharan, University of Sheffield, United Kingdom
Atmaram Muraleedharan is working in the DyVirt project focusing on developing new methods of
dynamic verification and validation for applications with significant non-linear effect. He did his
undergraduate degree in civil engineering from College of Engineering, Trivandrum in India and his
Master’s in civil engineering, specialising in offshore engineering from the University of Bologna in
Italy. Atmaram was also a visiting researcher at the Centre for Offshore Foundation Systems in the
University of Western Australia, before moving to the current position in the University of Sheffield’s
Dynamics Research Group. He is also working towards his PhD in Mechanical Engineering on the
same topic. His research interests are in the areas of structural dynamics, offshore engineering and
ESR 2- Georgios Tsialiamanis, University of Sheffield, United Kingdom
I studied at the school of Civil Engineering in the National Technical University of Athens
during the period 2012-2017. My specialization was on Structural Engineering and my thesis
was about high performance surrogate models for stochastic finite element problems. After
my studies I had my internship at Beta-CAE Systems (2017-2018), where I was asked to
apply machine learning techniques in various engineering problems. More specifically, I was
using machine learning to predict the results of car crash test simulations.
My PhD in the DyVirt project is about decision support in the concept of Virtualization and
also about working with ontologies to build a unifying framework for the various components
of Dynamic Virtualization.
ESR 3- Siddhesh Raorane, AGH University of Science and Technology, Poland
“Hello, Neil Armstrong had said, “Research is creating knowledge,” and through the completion of my
Masters and especially my Masters’ thesis, I realised that I am fascinated, thrilled and in love
with this art of creating knowledge, and I want to dive deeper into the beautiful world of
research. My passion for aircrafts urged to me to pursue my Bachelors in Aeronautical
Engineering, from NMIT, Bangalore, India, and then my Masters in Aeronautical Engineer at
Linköping university, Sweden.
I have worked on CFD projects related to aerodynamics and heat transfer that got me closer to
the world of simulations. I was fortunate enough to be given a project by Airinnova AB, Sweden for my Masters’ Thesis, which was based on multi-domain simulations. The aim of my Masters’
Thesis was to develop a tool that numerically designs and simulates aircraft propeller models.
The work that I put in my thesis made me realise that I want to work in the field of numerical
modelling and simulation.
Presently I am an Early Stage Researcher, as a part of the Dynamic Virtualisation: Modelling
Performance of Engineering Structures, at AGH University of Science and Technology, Kraków,
Poland. I am working on ESR3 research project, which is dedicated to multiscale modelling of
elastodynamic equations. The goal of this project is to develop a dynamic multiscale framework
(of space and time scales) for Acoustic Emission (AE) source and wave propagation, allowing
for prediction of AE signals and correlating AE signal features with particular damage features.
Apart from research, I love humour, writing, music and football”
ESR 4- Shreyas Srivatsa, AGH University of Science and Technology, Poland
Shreyas Srivatsa received his Master’s degree in Aerospace Engineering (2017) from the University of Maryland (UMD), College Park, USA and Bachelor’s degree in Mechanical Engineering (2014) from the University Visvesvaraya College of Engineering (UVCE), Bangalore University, Bangalore, India. In the past, he held positions of Research Associate (2017-18) and Project Assistant (2015) at the Indian Institute of Science (IISc), Bangalore.
His research interests include Structural Dynamics, Linear and Nonlinear Finite Element Methods, Smart Structures, Structural Dynamics of Electric Machines, MEMS and Energy Systems. Prior to joining the DyVirt project, he worked with an interdisciplinary team of researchers at IISc, Bangalore on the “Design, Development and Control of High-speed Switched Reluctance Generator for Direct-coupled Operation with Thermal Turbo-machinery”. He has a few conference and journal papers along with a few patents lined up as an output from his work for this project. He has also been a part of the collaboration between IISc, Bangalore and Sandia National Labs, USA for the development of the “Experimental Test Facility for Supercritical Carbon-di-oxide (s-CO2) Brayton Cycle Loop” at IISc, Bangalore, India.
After joining the DyVirt project research group at AGH University of Science and Technology, he has been working on developing multi-scale and multi-domain models for nanomaterial based composites. His focus is on developing smart structures with nanomaterials such as Carbon Nanotubes and MXenes. Currently, he is involved in building a computational framework for MXenes/Epoxy composites, modeling them across different length scales. His next goal is to study the effects due to dynamic loads on nanomaterial based smart structures.
ESR 6- Giancarlo Kosova, Siemens Industry Software, Belgium
Giancarlo Kosova graduated in Aerospace Engineering from the University of Naples Federico II in 2015.
His master’s degree thesis “Operational modal analysis of wind turbines in rotating conditions” comes
from his internship in Siemens Industry Software on the development and implementation of new
methods and technics to analyse the structural behaviour of operating wind turbines including
simulations and testing.
After his graduation he worked for more than 3 years as a CAE stress analyst in aeronautical
engineering, mainly in Stelia-Aerospace, on aircrafts like the Airbus Beluga XL, Airbus A350 XWB and
Aermacchi M345. This work on the project of primary structures using finite elements analysis and
analytical methods ranges from the conception of the structural configuration, through the evaluation
of the loads to the sizing of the components. Thanks to different kinds of work experience, he developed a consistent approach to problem solving focused on both academic and industrial applications.
He is working within the DyVirt project since September 2018 hosted by Siemens Industry Software. His PhD on the topic of “Modal Analysis in Presence of Nonlinearities” is promoted by the University of
Liege. In the technical context of the project, his final aim is to develop new reliable methods for the
numerical representation of structural connectors or joints, which account for all their functions
concurrently and model the nonlinear behaviour. In this sense, the specific objective of the ESR is to
characterise the dynamic behaviour of real structures subjected to operational loads on the base of the
results of experimental tests. His intent is to implement all the steps needed for analysing a real
structure optimising the process to make it reliable and applicable to most of the cases in nonlinear
dynamics. These steps are the following: detection, localisation, nonlinear parameter identification,
parameter identification of the underlying linear system. The development of new finite element
modelling techniques and the building of finite element models is in this context both a means to study
nonlinear systems and a final goal to have a high fidelity model used for simulations and predictions.
ESR 7- Xinyu Jia, University of Thessaly, Greece
The objective of the project is to develop a Bayesian probabilistic framework for uncertainty
quantification and propagation (UQP) in engineering simulations based on complex physics-based
models of dynamic systems, component test data and measurements collected during system
operation. A data-driven framework will also be developed to update predictions of safety and risk
taking into account the uncertainties in future loads. High performance computing techniques will be
integrated into the framework to efficiently handle large-order models of hundreds of thousands or
millions of degree of freedoms (DOF), including distributed or localized nonlinear actions activated
during operation and uncertain/stochastic loads. The framework will be based on the process of
assembling the structure from its individual components and substructures. The project is expected to
include the following:
1. Development of a comprehensive Bayesian uncertainty quantification and propagation
framework with computational tools for model selection, uncertainty calibration and propagation
through assembling a structure from individual components exploiting test/monitoring data.
2. Development of techniques for a more realistic description of uncertainties, taking into account
spatial/temporal distribution of model errors, ignored in existing the formulations, as well as
properly accounting for measurements uncertainties under different environment conditions.
3. Development of model reduction techniques and surrogate models, consistent with the
component assembly process, to mitigate the excessive computational effort for UQP of complex
systems assembled from high-fidelity models of individual components.
4. Exploitation of test/monitoring data collected under different environmental conditions during
operation of a structure to update predictions of reliability, safety and risk taking into account the
uncertainties in future loads
5. Demonstration and validation examples using laboratory experiments.
ESR 8- Tulay Ercan, University of Thessaly, Greece
The main goal of the project is to develop novel Bayesian optimal experimental design (ODE) methods in order to define the most informative, cost-effective, test campaigns for selecting and validating models through the whole assembling scale from coupon to prototype. OED methodologies will be developed for building and refining models of mechanisms, as well as estimating the model parameters that are activated under disparate loading conditions at the assembled substructure or system level (e.g. models of joint behaviour). For nonlinear models, tests will be designed to optimize test characteristics to excite all nonlinearities so that all associated model parameters can be estimated. The project is expected to include:
– Development of a comprehensive Bayesian OED framework with computational and software tools for cost-effective experimental design at component, sub-structural assemblies and system levels.
– Exploration of information-based measures based on expected utility functions to create useful metrics for comparing the value of different experimental designs.
– Investigation of OED techniques and means of enhancing the prediction accuracy for important output quantities of interest.
– Development of OED strategies to be robust to uncertainties arising from experimental conditions, operational variations and environmental and manufacturing variabilities.
– Investigation of asymptotic and sampling algorithms, surrogate models, parallel computing strategies etc. in order to significantly reduce the excessive computational effort arising from large number of model runs for large-order high complexity structural models.
– Demonstration and validation examples using virtual and laboratory experiments.
ESR 9- Silvia Vettori Siemens Industry Software, Belgium
Silvia Vettori gained her Bachelor’s degree in Mechanical Engineering at University of Rome “La Sapienza” in 2015, where she also obtained her Master’s degree in the same discipline in 2018. From September 2017 to March 2018, she spent 6 months as internship student at Siemens Industry Software NV (Leuven, Belgium), during which she developed her Master’s thesis project. The outcome of her work has become part of different conference papers, such as the one that she is going to present at IMAC-37 conference in Orlando (Florida) with the title “Development and validation of data processing techniques for aircraft Ground Vibration Testing”. She is currently involved in the Marie Curie DyVirt PhD program with Siemens Industry Software NV in Leuven (Belgium) and ETH Zurich. Her research project will deal with the development of Virtual Sensing techniques for structural dynamics applications. In this view, both experimental modal analysis (EMA) and operational modal analysis (OMA) techniques will be exploited in order to augment the information about the response of the structures to dynamic loads in operational conditions. Acquired test sensors data will be used in combination with finite element models, after they have been subjected to a modal order reduction process, for Virtual Sensing applications, i.e. for reconstructing the full field response of the system (accelerations, displacements or strains of the entire structure) from measured data available just at few locations. Different Virtual Sensing techniques such as Augmented Kalman Filter or Modal Expansion techniques will be investigated through their application to several types of structure, wind turbines are one of the examples.
ESR 10- Paulo Gonzaga , Siemens Gamesa, Denmark
Paulo Gonzaga gained his bachelor’s degree in Mechanical Engineering at the State University of Western Parana (Brazil) in 2014, including an exchange period at the Technical University of Denmark (2012-2013). After a couple of Industry internships including 6 months in the biggest energy producing dam in the world (ITAIPU dam), he moved to the United States where he obtained his Master’s degree in Aerospace Engineering from the Illinois Institute of Technology in 2016. His main focus of research was sensor integration and dynamic structural modelling. Back in Brazil he started working as a structural and HVAC engineer while working towards a second Master’s degree in Mechanical Engineering at the University of Campinas focusing in Stochastic mechanics and uncertainty quantification. He is currently involved in the Marie Curie DyVirt PhD program with Siemens Gamesa Renewable Energy and the University of Sheffield. His research project will focus on the Development of a framework for uncertainty propagation and risk management for diagnostic and prognostic models of wind turbine blades.
ESR 11- Sebastian Kruse, University of Liverpool, United Kingdom
Sebastian Kruse was born in Berlin/Germany and graduated with Master of Science in mathematics
from Leibniz University Hanover, where he also got his bachelor’s degree. Both his theses dealt with
the theoretical analysis of iterative solution methods for particular nonlinear optimization problems
under specific constraints.
During his studies he worked as a research assistant, inter alia, in the institute for risk and reliability
under the leadership of Prof. Dr. Michael Beer. Further, he spent two years working at MTU
Maintenance GmbH. As student employee in the major project “sfc-optimized Compressor
Maintenance”, a cooperation between MTU and the Technical University Braunschweig, he was part
of the organizational unit for “Industrial Engineering”, where his assignment was to develop an
algorithm for performance-optimized assembly of high-pressure compressors with technical and
legal restrictions. These experiences offered him the opportunity to gain deeper insight into various
engineering disciplines and sparked his interest in the associated problems and questions.
Now located at the University of Liverpool and primarily supervised by Dr. Edoardo Patelli within the
context of the DyVirt project, Sebastian’s particular research project aims at the development of
complex load models to capture spatial and temporal variations of loads on large, complex
structures in dynamic environments. Within his research he is particularly interested in the (wave,
sea current and) wind loads on offshore wind turbines. At the core of the envisaged approach lies
Bayesian model updating in order to combine fragmentary data and available expert knowledge into
an appropriate load model. A method to find the best possible point estimate for the Evolutionary
Power Spectra representing these complex loading scenarios is also to be developed. Currently
Sebastian is examining whether and how the technique of compressive sensing to achieve a low
dimensionality of the problem and the use of copulas for modelling correlations are suitable in this
ESR 12- George Pasparakis, Leibniz University Hannover, Germany
ESR 13- Nikos Tsokanas ETH, Zurich, Switzerland
Nikolaos Tsokanas received his MS diploma from the Faculty of Electrical Engineering & Computer
Science, University of Patras, Greece in August 2018. In his MS thesis, he designed and implemented a
controller for automatic landing of a civil aircraft.
During his studies, he undertook an internship in Airbus Defence & Space, Germany, in the department
of Attitude and Orbit Control Systems.
Before that, he was also employed as an intern in the faculty of Aerospace Engineering in TU Delft, The
Netherlands. His focus there was on real-time acquisition, analysis and control of a fatigue machine.
As of September 2018, he is a PhD student in ETH Zurich, in the Department of Civil, Environmental and
Geomatic Engineering, in the Chair of Structural Dynamics and Earthquake Engineering. His research
focus is on real-time hybrid testing under uncertainties.
He will be working within WP5, whose objective is to create enhanced and accelerated methods for
modeling and testing protocols for validation and verification of models of dynamic response of
structures that incorporate inherent uncertainties of the excitation, operation, and modeling. More
specifically, his task will be to develop methods for hybrid simulation of dynamic response of uncertain
structural systems to uncertain excitation and operating conditions and link them into the general
verification and validation framework developed in WP1.
ESR 14- Tom Simpson ETH, Zurich, Switzerland
Tom Simpson received his Masters degree in Mechanical Engineering in June 2018 from the
University of Sheffield, UK. His thesis focused on the use of manifold learning techniques for
non-linear dynamical systems. During his degree, he also spent a year working at the
Advanced Manufacturing Research Centre as a research assistant investigating novel
machining methods of aerospace alloys.
As of September 2018, he is a PhD student in the DyVirt network at the chair of Structural
Mechanics and Monitoring at ETH Zürich. His research topic is regarding the development of
hybrid testing with an aim towards supporting the virtualization of wind energy assets.
Hybrid testing involves the combining of both physical experiments and numerical models in
order to increase the reliability and complexity of dynamic structural testing.
Within this topic, his research interests include dynamic substructuring, model reduction,
uncertainty quantification, non-linear dynamics and real time control and computation.