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[EnAble]: Developing and Exploiting Intelligent Approaches for Turbulent Drag Reduction

Lead Research Organisation: Newcastle University
Department Name: Sch of Engineering

Abstract

Whenever air flows over a commercial aircraft or a high-speed train, a thin layer of turbulence is generated close to the surface of the vehicle. This region of so-called wall-turbulence generates a resistive force known as skin-friction drag which is responsible for more than half of the vehicle's energy consumption. Taming the turbulence in this region reduces the skin-friction drag force, which in turn reduces the vehicle's energy consumption and thereby reduces transport emissions, leading to economic savings and wider health and environmental benefits through improved air quality. To place this into context, just a 3% reduction in the turbulent skin-friction drag force experienced by a single 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 in active service around the world. Active wall-turbulence control is seen as a key upstream technology currently at very low technology readiness level that has the potential to deliver a step change in vehicle performance. Yet despite this significance and well over 50 years of research, the complexity of wall-turbulence has prevented the realisation of any functional and economical fluid-flow control strategies which can reduce the turbulent skin-friction drag forces of industrial air flows of interest.

The EnAble project aims to develop, implement and exploit machine intelligence paradigms to enable a new approach to wall-turbulence control. This new form of intelligent fluid-flow control will be used to develop practical wall-turbulence control strategies that can rapidly and autonomously optimise the aerodynamic surface with minimal power input whilst being adaptive to changes in flow speed. This new capability will open up the opportunity to discover new ways to tame wall-turbulence and exploit the latest drag reduction mechanisms to generate significant levels of turbulent skin-friction drag reduction.

Planned Impact

The key to reducing carbon dioxide (CO2) emissions across the global transportation network resides in developing technology which lowers fuel consumption. Net-energy savings by reducing the skin-friction drag force by only a few percent would create a more energy efficient global transportation network. This would lead to vast economic savings and improved air quality, which in turn would lead to an increase in health and quality of life. Net-energy saving approaches are particularly pertinent at present given our government's recent lawful pledge (June 2019) to be a net-zero carbon emission economy by 2050, marking the UK as being the first member of the G7 group to legislate net-zero emissions, resulting in an amendment to the Climate Change Act: https://www.theguardian.com/theresa-may-commits-to-net-zero-uk-carbon-emissions-by-2050. The EnAble project aims to help transition the UK towards being this low-carbon economy. Noting the growing interest in battery technology and the production of green energy, alternatively powering an active low-powered control solution, rather than relying on the usual fossil fueled systems, could yield significant savings in transport emissions ahead of any full-scale alternative-energy revolution. More broadly, energy reduction in any system can only be positive. The EnAble project will develop fundamental underpinning technology in consultation with Airbus and the Aerospace Technology Institute (ATI) to provide pathways to future industrial uptake of our research.

The development of the EnAble project's new machine intelligence technology is very "media-friendly" as it ultimately targets mitigating global climate change and is therefore likely to capture the imagination of the public. To capitalise on this, the EnAble team will take full advantage of the Imperial College Media Team and Newcastle University's Press Office who will support the development of short video clips to promote the project's research outputs via the BBC, Talks for Schools Programme, Reach Out Reporter and ReachOut CPD and STEM outreach for Schools and Colleges. In addition, the EnAble project will dedicate funds to train the PDRAs in public engagement skills via a 2-day course with the Royal Society. The team will use this training to engage with the general public at Science cafes around the UK, such as Café Culture North East, as well as during the Imperial Festival and Imperial Fringe events, and with young adults during the Science and Technology Facilities Council's yearly open week.

The EnAble team will open the source code of Incompact3d (the flow solver which forms a cornerstone of the EnAble project) and the developed machine intelligence tools to the scientific community via a dedicated website. EnAble's Advisory Board will play a key role in developing pathways to impact. Professor Phil Blythe, the Chief Scientific Advisor for the Department for Transport, and Chair of Intelligent Transport Systems at Newcastle University will provide a vital link to UK government where the research from the EnAble project may ultimately help to inform UK government policy on transport, energy and climate change. Dr Stuart Gates, the Aerospace Technology Institute's lead technologist and the institute's specialist for aerodynamics, and focal point for industry and academia will provide a pivotal role in enabling the team to disseminate research findings to stakeholders across the UK aerospace sector. Further, Dr Stephen Rolston, Head of Aeromechanics at Airbus UK, will provide an important link directly back into the UK aerospace industry to ensure every possibility that EnAble's research findings can be further developed and fully exploited by the UK aerospace sector.

Publications

10 25 50
 
Description The EnAble project has developed a new machine-learning (AI) framework, which we have called 'NUBO.' NUBO has been made open-source to the scientific community and can be downloaded from http://www.experimental-fluid-dynamics.com/nubo.html. NUBO has been successfully applied on ARCHER2, the UK's National Supercomputer, and in a series of advanced wind tunnel experiments to control the turbulent flows associated with aircraft. Our research has shown that it possible to reduce skin-friction drag significantly, by as much as 80% in the laboratory setting.
Exploitation Route The next step for this research is to apply NUBO, along with our new actuation and sensing technology which has been developed as part of the project in more industrial settings.
Sectors Aerospace

Defence and Marine

Energy

Transport

URL http://www.experimental-fluid-dynamics.com
 
Description We developed NUBO - our machine learning framework - to be used by a general audience. We have communicated the use of NUBO to various audiences which has generated several other international collaborations including AI-driven optimisation for killing of bacteria in biomedical applications, and on the optimisation of geometry for small satellite applications.
Sector Aerospace, Defence and Marine,Healthcare
 
Description DTP 2018-19 Newcastle University
Amount £5,211,685 (GBP)
Funding ID EP/R51309X/1 
Organisation Engineering and Physical Sciences Research Council (EPSRC) 
Sector Public
Country United Kingdom
Start 08/2018 
End 09/2023
 
Description EPSRC Centre for Doctoral Training in Cloud Computing for Big Data
Amount £3,523,116 (GBP)
Funding ID EP/L015358/1 
Organisation Engineering and Physical Sciences Research Council (EPSRC) 
Sector Public
Country United Kingdom
Start 03/2014 
End 10/2022
 
Description Proof of Concept
Amount £116,599 (GBP)
Organisation United Kingdom Research and Innovation 
Department Northern Accelerator
Sector Charity/Non Profit
Country United Kingdom
Start 03/2022 
End 12/2022
 
Title Data and scripts for reproducing "Optimisation and Analysis of Streamwise-Varying Wall-Normal Blowing in a Turbulent Boundary Layer" 
Description This is the accompanying data and Python scripts to reproduce the figures in "Optimisation and Analysis of Streamwise-Varying Wall-Normal Blowing in a Turbulent Boundary Layer", submitted to Flow, Turbulence and Combustion. 
Type Of Material Database/Collection of data 
Year Produced 2023 
Provided To Others? Yes  
URL https://zenodo.org/record/7687849
 
Title Data and scripts for reproducing "Optimisation and Analysis of Streamwise-Varying Wall-Normal Blowing in a Turbulent Boundary Layer" 
Description This is the accompanying data and Python scripts to reproduce the figures in "Optimisation and Analysis of Streamwise-Varying Wall-Normal Blowing in a Turbulent Boundary Layer", submitted to Flow, Turbulence and Combustion. 
Type Of Material Database/Collection of data 
Year Produced 2023 
Provided To Others? Yes  
URL https://zenodo.org/record/7687850
 
Title NUBO (Newcastle University Bayesian Optimisation) 
Description NUBO, short for Newcastle University Bayesian optimisation, is a Bayesian optimisation framework for the optimisation of expensive-to-evaluate black-box functions, such as physical experiments and computer simulations. It is developed and maintained by the Fluid Dynamics Lab at Newcastle University. NUBO focuses primarily on transparency and user experience to make Bayesian optimisation easily accessible to researchers from all disciplines. Transparency is ensured by clean and comprehensible code, precise references, and thorough documentation. User experience is ensured by a modular and flexible design, easy-to-write syntax, and careful selection of Bayesian optimisation algorithms. NUBO allows you to tailor Bayesian optimisation to your specific problem by writing the optimisation loop yourself using the provided building blocks. Only algorithms and methods that are sufficiently tested and validated to perform well are included in NUBO. This ensures that the package remains compact and does not overwhelm the user with an unnecessarily large number of options. The package is written in Python but does not require expert knowledge of Python to optimise your simulations and experiments. NUBO is distributed as open-source software under the BSD 3-Clause licence. 
Type Of Technology Software 
Year Produced 2023 
Open Source License? Yes  
Impact Open-source software for the scientific community. Has been downloaded 3429 times since April 2023 (date of entry is 5th March 2023). 
URL https://nubopy.com/_build/html/index.html