Naval Architecture, Ocean & Marine EngineeringData Analysis for Engineering (Short Course)

Dates TBC, University of Strathclyde, Glasgow, UK

Course Organisation

The course will the divided in 5 lectures, each covering one of the five days of course, for a total of 30 hours (5 RPD credits). A morning theoretical session (9:00 to 12:00) and an afternoon hands-on session (14:00 to 17:00) will be provided.

Topic Overview

Data Analytics is improving our way to understand complex phenomena as and even faster than a-priory physical models have done in the past.

Engineering Systems are composed by many complex elements, their mutual interaction is not easy to model and predict adopting the conventional first principles physics model based on a-priory physical knowledge, because of the significant number of parameters which influence their behaviour. Moreover, state-of-the-art models built upon the physical knowledge of the system may have computational prohibitive requirements.

First principles physics models describe the behaviour of systems based on governing physical laws and taking into account their mutual interactions. The higher the detail in the modelling of the physical equations the higher the expected accuracy of the results and the computational time required for the simulation. These models are generally rather tolerant to extrapolation and do not require extensive amount of operational measurements. On the other hand, when employing models that are computationally fast enough to be used for online optimisation, the expected accuracy in the prediction of operational variables is relatively low. Additionally, the construction of the model is a process that requires competence in the field, and availability of technical details which are often not easy to get access.

Data Driven Models, instead, exploit advanced statistical techniques in order to build models directly based on the large amount of historical data collected by the recent advanced automation systems without having any a-priory knowledge of the underlining physical system. Data Driven Models are extremely useful when it comes to continuously monitor physical systems to avoid preventive or corrective maintenance and take decisions based on the actual condition of the system. Unfortunately, Data Driven Models need a large amount of data to achieve satisfying accuracies and this can be a drawback when the data’s collection might require a stop of the asset. For these reasons, the two different modelling philosophies must be exploited in conjunction in order to solve their drawbacks and take the best of each approach.

Timetable

The tutors

Luca Oneto was born in Rapallo, Italy in 1986. He received his BSc and MSc in Electronic Engineering at the University of Genoa, Italy respectively in 2008 and 2010. In 2014 he received his PhD from the same university in the School of Sciences and Technologies for Knowledge and Information Retrieval with the thesis ``Learning Based On Empirical Data''.

In 2017 he obtained the Italian National Scientific Qualification for the role of Associate Professor in Computer Engineering and in 2018 he obtained the one in Computer Science. He worked as Assistant Professor in Computer Engineering at University of Genoa from 2016 to 2019. In 2018 he was co-founder of the spin-off ZenaByte s.r.l. He is currently Associate Professor in Computer Science at University of Pisa with particular interests in Statistical Learning Theory and Data Science.

Dr Andrea Coraddu is currently Assistant Professor at the University of Strathclyde. His professional and academic experiences include working as a Research Associate at Newcastle University, Research Engineer at DAMEN R&D department and serving as Postdoctoral Research Fellow at the University of Genoa.

He received his MEng in Naval Architecture and Marine Engineering at the University of Genoa where he also was awarded a PhD in Naval Architecture and Marine Engineering. Dr Coraddu’s research focuses on modelling, optimisation and analysis of ship power plants and propulsion systems. His primary research involves taking advantage of on-board data availability in assessing vessel performance, energy optimisation, and real-time monitoring of the primary systems. Utilising the latest learning algorithms and theoretical results in machine learning, Dr Coraddu is developing data-driven approaches to investigate the behaviour of complex on-board systems and their mutual interaction.

• If you are interested do not hesitate to contact us (andrea.coraddu@strath.ac.uk or luca.oneto@unige.it) for additional details.