SIAM-IMA Edinburgh - Study Group with Industry

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SIAM-IMA Edinburgh - Study Group with Industry

 Jun 28 - Jul 01, 2021

The Edinburgh SIAM-IMA student chapter is hosting a week-long student study group with industry supported by the Maxwell Institute for Mathematical Sciences, the Edinburgh Mathematical Society (EMS), the Glasgow Mathematical Journal Trust (GMJT), the Society for Industrial and Applied Mathematics (SIAM), and the Institute for Mathematics and its Applications (IMA). 

The event offers:

1.       An opportunity for graduate students to apply mathematics in an industrial setting,

2.       A chance for collaboration with students from different institutions and from a wide range of research areas, and

3.       Essential skills training in communication and presentation skills.

This event will be based on the format used for the established modelling camps and study groups.   The event offers an opportunity to work on real life industrial problems and would be excellent practise for participation in other study group events.

The SIAM-IMA Student Chapter aims to connect all those who use mathematics in their research, so we are particularly interested in attracting students whose expertise does not necessarily lie within the areas typically classed as applied mathematics.  We strongly encourage applications from any students with a mathematical background, whether their primary interest be in pure mathematics, the physical sciences, informatics or engineering.  Get in touch (  if you have any questions. 

While the event is primarily aimed at PhD students, we encourage interested students at the Master's level to complete an application.  As an online event, there are no costs associated with participation, just your time and enthusiasm! 

Problem Challenges

Mark Foster and Ray Abma, University of Texas

Generating Irregular Sequences for Seismic Imaging Problems

Seismic surveys produce images of the Earth's subsurface using sound energy.  Simultaneous source seismic acquisition can greatly improve the quality of these surveys, while also reducing the environmental and ecological impact.  These surveys use multiple sources with overlapping source energies, resulting in interference in the obtained data.  To interpret the results, it is necessary to separate the signal from the interference. For this to be effective, the signal must be coherent and the interference not.  This project will investigate methods to enforce this by generating irregular sequences of scheduled time delays.  We anticipate the project may be particularly well-suited to those with interests in number theory, dynamical systems and signal processing, but students from all backgrounds are desired.

Oliver Warlow, Ventient Energy

Harnessing SCADA data collected from windfarms

Ventient Energy wishes to harness the Supervisory Control and Data Acquisition (SCADA) data collected from their wind farms to process data recorded by the turbines to make decisions on the health status of the machine.  This predictive health information can be used in conjunction with other sources of information to make decisions on the repair, maintenance, and upgrade of machines. This project is primarily interested in the use of machine learning (ML) to make decisions about the health status of turbines, by defining a suitable model(s) to predict the health status of turbines from 10 minute SCADA data.  Potential techniques and activities that would be useful in this project are common ML strategies, change-point-detection, optimisation, simulation, and risk-assessment.

Michael Crowley, Fluid Mechanics Ltd

Mathematical modelling of a heat absorbing and releasing structure

The Near Isothermal Stirling Heat Pump (NISHP) is a new patented variation of a Stirling heat pump which attempts to achieve isothermal (or more isothermal than adiabatic) gas compression and expansion using a heat absorbing and releasing structure (HARS). The aim of this project is to find a mathematical description of the isothermal efficiency of the HARS that can be incorporated into a transient model for the NISHP performance assessment.  Ultimately, this mathematical model should determine the relative importance of various parameters so the efficiency and power density of the NISHP can be optimized. Potential techniques and activities that would be useful in this project include mathematical modelling, fluid mechanics or thermodynamics; students from all engineering, physical and mathematical backgrounds are desired.


The call for applications is now open.

Apply here