Use of machine learning to optimise turbulent combustion modelling

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Turbulence Engine crop

The 21st century is witnessing significant global challenges, including those related to our environment. Global warming is now a major concern, requiring immediate action by policymakers, industry as well as individuals. In this context, the energy sector needs special attention.

Combustion has been an important form of energy and is expected to remain so for many years to come. Achieving cleaner combustion in energy systems such as gas turbines and reciprocating engines is therefore a top priority. We need to develop new concepts to increase efficiency and thus reduce fuel consumption. We also need to use alternative fuels such as hydrogen, which have very different properties compared with conventional fuels such as natural gas.

This requires more accurate predictive tools that can inform the design process and avoid the trial-and-error cost. Machine learning is one of the tools that can be very beneficial in this regard. This tool can help us develop data-driven models, which are applicable to a wide range of conditions in real-life applications.

In this project, a novel machine learning approach will be applied to translate the physics contained in data into tangible turbulence models with improved accuracy. In particular, this will be done by fusing the machine learning process with an a-posteriori evaluation of the novel models into a single, integrated framework. This will ensure that the models are useable, robust, and can be implemented easily.

Project goals

  • Development of novel combustion models using machine learning
  • Application of new models to study viability and performance of new clean fuels

Supervision team

The University of Melbourne: Professor Richard Sandberg and Associate Professor Mohsen Talei.

RWTH Aachen:Dr Temistocle Grenga, and Professor Heinz Pitsch.

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Who we are looking for

The successful candidate must have:

  • Demonstrated research experience in the field of Aeronautics and Astronautics, and Mechanical Engineering
  • Demonstrated ability to work independently and as part of the team
  • Demonstrated time and project management skills
  • Demonstrated ability to write research reports or other publications to a publishable standard (even if not published to date)

Further details

The project and students will benefit greatly from the complementary expertise and experiences of Professors Sandberg and Pitsch, and will develop broad understanding of fluid dynamics, combustion, simulation, ML and modelling techniques.

Professor Richard Sandberg’s group are specialists in high-fidelity simulation of turbulent flows and their application to aerospace systems to gain physical understanding of flow and noise. He is also an expert in the development of lower-fidelity models based on machine-learning techniques that can be employed in an industrial context.

Professor Pitsch and his group have been researching current challenges in the energy transition by enhancing sustainable energy technologies, such as renewable fuels, innovative carbon-free fuel blends, efficient energy conversion, control of pollutant emission. The institute focuses its research on the fields of turbulent combustion and its various applications, such as engines, gas turbines and furnaces. The research includes chemical kinetics, turbulence theory, multiphase flows and electrochemistry.

Professors Sandberg and Pitsch share common research interests yet are using complementary approaches and tools.

The Melbourne PhD candidate will be enrolled in the PhD program at the School of Mechanical Engineering the University of Melbourne, and the RWTH Aachen PhD candidate will be enrolled in the PhD program at the Institute of Technical Combustion.

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