A glimpse into a collaboration between academia and industry
By Andrea Leone, Ergys Çokaj, Halvor Snersrud Gustad
Halvor is an industrial-PhD student at TechnipFMC, a global leader in oil and gas engineering, and the Department of Mathematical Sciences of the Norwegian University of Science and Technology (NTNU). Andrea and Ergys are PhD students at the Department of Mathematical Sciences (NTNU) and Early Stage Researchers (ESRs) in the European Training Network THREAD, a network of universities, research organisations, and industry that addresses fundamental modelling problems related to highly flexible slender structures like yarns, cables, hoses or ropes. They are collaborating with Halvor during their secondment at TechnipFMC, working on projects related to the analysis of sensor data from offshore operations.
Halvor, could you tell us about your background and your current position at TechnipFMC?
I studied industrial mathematics at NTNU and completed my master’s thesis in the spring of 2019. While pursuing my degree, I worked at TechnipFMC during one summer and found the work to be very interesting. Therefore, when it came time to choose a topic for my master’s thesis, I was happy to collaborate with TechnipFMC. I was hired by the company in a position of analysis engineer while writing the master thesis and had my first day in the fall of 2019.
Typically my job consists of performing analysis of data we receive from various offshore operations. It demands knowledge of mathematics and coding.
You do an industrial PhD, which means that you’re affiliated both in a university and an industrial company. How does it work?
The industrial PhD program is a collaboration between the candidate, the company, and the degree-conferring institution. As an industrial PhD student, I am affiliated with both TechnipFMC, my industrial company, and the Norwegian University of Science and Technology (NTNU), my academic institution.
In this program, TechnipFMC is the project owner and provides support with industrial-related topics, as well as an industrial supervisor who helps me to ensure the research is relevant to the company’s needs. Meanwhile, NTNU provides academic support and an academic supervisor, who offers guidance on the research methods and theoretical frameworks. As a PhD student, I need to complete various mandatory classes, and NTNU also offers the opportunity to take these classes.
This unique collaboration allows me to gain both academic and industrial experience, which I believe will be helpful in my future career.
What is a normal day for Halvor at work? How much of your working time do you dedicate to the PhD from NTNU and the job at TechnipFMC? Are you able to maintain a reasonable balance?
As a PhD student and an analysis engineer at TechnipFMC, my workday will vary depending on the demands of my job and research. Since I am working on my PhD over four years, I spend 25% of my time on my industrial job, while the rest is spent on my research. The amount of time I spend on each task can vary from week to week, depending on the needs of my research and job.
Fortunately, my company is supportive of my academic pursuits and allows me to travel to my university to collaborate with my research group when necessary. This helps me balance my work responsibilities with my academic goals.
Regarding work-life balance, I think that I am no different from most PhD students.
Halvor, you said you have a master in industrial mathematics and now you’re a structural analysis engineer. Do you consider your academic training adequate for a job like yours? Andrea and Ergys, what is your feeling after a few weeks of experience in the industry?
Halvor: Although my job involves some work in structural analysis, most of my responsibilities revolve around handling data from sensor systems. These sensors are installed on large structures, so having a basic understanding of physics is helpful. However, the core of my job revolves around analyzing the data we receive from these sensors, and my background in industrial mathematics has been extremely useful in this regard. I use my skills in mathematical modeling and data analysis to make sense of the data and generate insights for my team.
Andrea and Ergys: We’ve been here at TechnipFMC for just over a month and it has been a very intense period. We had the chance to follow a few insightful schools and online courses on subsea field development and the fundamentals of subsea products and services. It is fascinating to see how complex and well organized the fields that lay in the seabed are. Coming from a different background, we have also faced some difficulties and challenges, but it is very rewarding and interesting to see the result after math and engineering are put together.
Andrea and Ergys, you are spending an industrial secondment at TechnipFMC as part of your position as ESRs in the THREAD project. What is it about?
Each ESR in THREAD has had the opportunity of spending a three-month research secondment at one or more non-academic institutions. Industrial secondments are meant to offer all ESRs research experience in the non-academic sector as well as close cooperation with internationally leading experts. In our case, we are at the end of our first month of secondment at TechnipFMC, where we are joining the structural analysis engineering group, led by Per Thomas Moe. There is a number of topics we can deal with, like performing analysis of riser systems, evaluation of riser measurement data, and developing advanced numerical models. This is an exciting experience for us since it is our first close contact with industry.
Can you tell us about your collaboration at TechnipFMC?
At the moment we are engaging in a short and long-term projects. In the first case, we are trying to find a way for detecting when the well of a drilling rig has a crack. This project is related to anomaly detection and time series analysis, which are new topics for us. We are applying both supervised (e.g. using convolutional neural networks) and unsupervised (e.g. principal component analysis and clustering algorithms) machine learning techniques to tackle this problem, and the main challenge so far is to find a technique that would work well in practice. The long-term project is about studies of structural fatigue and consists of using geometrically exact rod theory to model a riser, to better predict fatigue on the wellhead. It is stimulating to delve into these tasks and we believe we will benefit from this mixture of academia and industry.
THREAD project – https://thread-etn.eu/
TechnipFMC subsea division – https://www.technipfmc.com/en/what-we-do/subsea/