EPSRC Centre for Doctoral Training Cloud Computing for Big Data


Mike Diessner
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In 2018, I graduated with a Bachelor of Science in Economics from the University of Bonn in Germany. Enjoying the quantitative side of the degree the most, I pivoted into Data Science and worked as an analyst for DHL and a consultancy specialising in the energy markets. I decided to complement this practical experience with a more technical degree. In 2020, I graduated with a Master of Science in Applied Data Science and Statistics from the University of Exeter where I ranked first of all postgraduates enrolled at the School of Mathematics.

The Cloud Computing for Big Data CDT appealed to me as it combines scientific research with applications in industry. We are not just able to conduct research at the forefront of technology but also to solve problems that have a big impact outside of academia. This is further supported by the cohort system that provides a unique community on a day-to-day basis for the duration of the PhD and beyond.

Outside of the CDT, I enjoy cooking and eating good food, rooting for my favourite football team back in Germany (the one and only FC Schalke 04), and bouldering. 

PhD Title

Taming turbulence with Bayesian optimisation

Despite over a century of research, turbulence remains one of the most important unsolved problems in the modern world. Whenever air flows over a commercial aircraft, a high-speed train or a car, or when water flows around the hull of an ocean-going ship liner, a thin layer of turbulence is generated close to the surface of the vehicle.This region of turbulence is responsible for more than half of the vehicle’s energy consumption. Taming the turbulence in this region reduces energy consumption, which in turn reduces transport emissions, leading to vast economic savings and wider health and environmental benefits due to improved air quality. To place this into context: just a 3% reduction in the turbulent forces acting on a long-range commercial aircraft would save £1.2M in jet fuel per aircraft per year and prevent the annual release of 3,000 tonnes of carbon dioxide. There are currently around 23,600 aircraft around the world. Yet, despite this significance, there is no economical system to reduce the effects of turbulence on any transportation vehicle.

Advanced wind tunnel experiments taking place in the School of Engineering at Newcastle University enable the investigation of how turbulence can be manipulated. These experiments make use of two state-of-the-art pieces of equipment developed at Newcastle University: (1) MicroElectro-Mechanical Systems sensors measure the turbulence close to the surface and (2) a blowing rig allows us to blow air upwards to alter the turbulence along a surface. The latter is essentially a metal plate with 666,000 small holes that can be divided in up to 600 smaller sections. Using valves underneath this plate, the blowing of air in each of these sections can be set individually by adjusting certain parameters, such as the blowing amplitude. At the upper end, a total of around 1,800 parameters have to be specified. However, the optimal, turbulenceminimising and energy-saving parameters cannot be found in an analytical fashion as the wind tunnel experiment itself is a black box function and has no known analytical form.

Experiments like these are expensive and time-consuming to perform so that an advanced framework is required to find the optimal parameters in a minimum number of evaluations. As part of my PhD project, I am developing an advanced Bayesian optimisation framework that utilises Gaussian processes that act as surrogates for the underlying physical processes of the experiments. While Bayesian optimisation works well in a low-dimensional parameter space, the Curse of Dimensionality makes it challenging to scale up to hundreds or even thousands of parameters. A main challenge of my project is therefore to find ways to extend the Bayesian optimisation framework to high-dimensional parameter space (for example through dimensionality reduction techniques or low-order additive models). The aim is to use this framework to find ways to reduce turbulence effectively and economically but also to learn more about the underlying physical processes.


Richard Whalley, Kevin Wilson, Yu Guan