Exploiting non-equilibrium turbulence in design using DNS and Machine Learning

The current trend in fan design for large civil turbofans is to reduce the speed, and increase the diameter of the fan. This gives a reduced fan pressure ratio and increased bypass ratio, improving the propulsive efficiency of the engine, however these factors present a challenge to the fan designer, with new areas of the design space being explored, well away from the majority of current designs and experience. This makes accurate low order (Reynolds Averaged Navier Stokes - RANS) models crucial, however previous work has identified shortcomings with the prediction of the non-equilibrium region (where turbulence production exceeds dissipation) of turbulent boundary layers by an industry standard turbulence model in subsonic compressor cases.
This project aims to gain an understanding of loss mechanisms in the non-equilibrium region of transonic turbulent boundary layers, and investigate how low order models may be improved to better capture these phenomena by answering the following research questions:
1. What is the importance of non-equilibrium turbulence for transonic boundary layers?
2. Do current industry standard turbulence models capture non-equilibrium effects?
3. If current models are inadequate, how may they be modified to improve the prediction of non-equilibrium behaviour?
The project will utilise high fidelity computational fluid dynamics techniques that resolve all (Direct Numerical Simulation - DNS) or much (Large Eddy Simulation - LES) of the range of scales of turbulent structures in the flow, giving a detailed insight into the flow physics that is not available with conventional (RANS) CFD or experiments. With the data generated with this work, innovative data-driven techniques in turbulence modelling will be explored to improve the prediction of non-equilibrium phenomena in low order methods suitable for use in industry for design. Efforts will also be made to improve existing turbulence models, such as by tuning modelling constants with reference to the DNS data to better predict these flows.

1. Impact on the UK Aero-Propulsion and Power Generation Industry
The UK Propulsion and Power sector is undergoing disruptive change. Electrification is allowing a new generation of Urban Air Vehicles to be developed, with over 70 active programmes planning a first flight by 2024. In the middle of the aircraft market, companies like Airbus and Rolls-Royce, are developing boundary layer ingestion propulsion systems. At high speed, Reaction Engines Ltd are developing complex new air breathing engines. In the aero gas turbine sector Rolls-Royce is developing UltraFan, its first new architecture since the 1970s. In the turbocharger markets UK companies such as Cummins and Napier are developing advanced turbochargers for use in compounded engines with electrical drive trains. In the power generation sector, Mitsubishi Heavy Industries and Siemens are developing new gas turbines which have the capability for rapid start up to enable increased supply from renewables. In the domestic turbomachinery market, Dyson are developing a whole new range of miniature high speed compressors. All of these challenges require a new generation of engineers to be trained. These engineers will need a combination of the traditional Aero-thermal skills, and new Data Science and Systems Integration skills. The Centre has been specifically designed to meet this challenge.

Over the next 20 years, Rolls-Royce estimates that the global market opportunities in the gas turbine-related aftercare services will be worth over US$700 billion. Gas turbines will have 'Digital Twins' which are continually updated using engine health data. To ensure that the UK leads this field it is important that a new generation of engineer is trained in both the underpinning Aero-thermal knowledge and in new Data Science techniques. The Centre will provide this training by linking the University and Industry Partners with the Alan Turing Institute, and with industrial data labs such as R2 Data Labs at Rolls-Royce and the 'MindSphere' centres at Siemens.

2. Impact on UK Propulsion and Power Research Landscape
The three partner institutions (Cambridge, Oxford and Loughborough) are closely linked to the broader UK Propulsion and Power community. This is through collaborations with universities such as Imperial, Cranfield, Southampton, Bath, Surrey and Sussex. This will allow the research knowledge developed in the Centre to benefit the whole of the UK Propulsion and Power research community.

The Centre will also have impact on the Data Science research community through links with the CDT in Data Centric Engineering (DCE) at Imperial College and with the Alan Turning Institute. This will allow cross-fertilization of ideas related to data science and the use of advanced data analytics in the Propulsion and Power sectors.

3. Impact of training a new generation of engineering students
The cohort-based training programme of the current CDT in Gas Turbine Aerodynamics has proved highly successful. The Centre's independent Advisory Group has noted that the multi-institution, multi-disciplinary nature of the Centre is unique within the global gas turbine training community, and the feedback from cohorts of current students has been extremely positive (92% satisfaction rating in the 2015 PRES survey). The new CDT in Future Propulsion and Power will combine the core underlying Aero-thermal knowledge of the previous CDT with the Data Science and Systems Integration skills required to meet the challenges of the next generation. This will provide the UK with a unique cohort of at least 90 students trained both to understand the real aero-thermal problems and to have the Data Science and Systems Integration skills necessary to solve the challenges of the future.