Data-Driven Surrogate-Assisted Evolutionary Fluid Dynamic Optimisation

Lead Research Organisation: University of Exeter
Department Name: Engineering Computer Science and Maths


Computational fluid dynamics (CFD) is fundamental to modern engineering design, from aircraft and cars to household appliances. It allows the behaviour of fluids to be computationally simulated and new designs to be evaluated. Finding the best design is nonetheless very challenging because of the vast number of designs that might be explored. Computational optimisation is a crucial technique for modern science, commerce and industry. It allows the parameters of a computational model to be automatically adjusted to maximise some benefit and can reveal truly innovative solutions. For example, the shape of an aircraft might be optimised to maximise the computed lift/drag ratio.

A very successful suite of methods to tackle optimisation problems are known as evolutionary algorithms, so-called because they are inspired by the way evolutionary mechanisms in nature optimise the fitness of organisms. These algorithms work by iteratively proposing new solutions (shapes of the aircraft) for evaluation based upon recombinations and/or variations of previously evaluated solutions and, by retaining good solutions and discarding poorly performing solutions, a population of optimised solutions is evolved.

An obstacle to the use of evolutionary algorithms on very complex problems with many parameters arises if each evaluation of a new solution takes a long time, possibly hours or days as is often the case with complex CFD simulations. The great number of solutions (typically several thousands) that must be evaluated in the course of an evolutionary optimisation renders the whole optimisation infeasible. This research aims to accelerate the optimisation process by substituting computationally simpler, dynamically generated "surrogate" models in place of full CFD evaluation. The challenge is to automatically learn appropriate surrogates from a relatively few well-chosen full evaluations. Our work aims to bridge the gap between the surrogate models that work well when there are only a few design parameters to be optimised, but which fail for large industry-sized problems.

Our approach has several inter-related aspects. An attractive, but challenging, avenue is to speed up the computational model. The key here is that many of these models are iterative, repeating the same process over and over again until an accurate result is obtained. We will investigate exploiting partial information in the early iterations to predict the accurate result and also the use of rough early results in place of the accurate one for the evolutionary search. The other main thrust of this research is to use advanced machine learning methods to learn from the full evaluations how the design parameters relate to the objectives being evaluated. Here we will tackle the computational difficulties associated with many design parameters by investigating new machine learning methods to discover which of the many parameters are the relevant at any stage of the optimisation. Related to this is the development of "active learning" methods in which the surrogate model itself chooses which are the most informative solutions for full evaluation. A synergistic approach to integrate the use of partial information, advanced machine learning and active learning will be created to tackle large-scale optimisations.

An important component of the work is our close collaboration with partners engaged in real-world CFD. We will work with the UK Aerospace Technology Institute and QinetiQ on complex aerodynamic optimisation, with Hydro International on cyclone separation and with Ricardo on diesel particle tracking. This diverse range of collaborations will ensure research is driven by realistic industrial problems and builds on existing industrial experience. The successful outcome of this work will be new surrogate-assisted evolutionary algorithms which are proven to speed up the optimisation of full-scale industrial CFD problems.

Planned Impact

Aerospace and Aviation

The UK has the second largest aerospace industry in the world with significant capabilities in the key areas, including engines, airframe structures, aerodynamics and air traffic. To maintain the UK's world-leading position in this sector, optimisation techniques like evolutionary algorithms will provide a unique opportunity to address the main complex challenges and create innovative designs. This research will remove the obstacles in applying evolutionary techniques in the aerospace and aviation sector.

One of our project partners, the Aerospace Technology Institute (ATI), was created by the government in 2012 as part of the wider aerospace strategy. As their letter of support attests, the work is "clearly aligned with the key challenges faced in trying to use optimisation for future high-lift aerodynamic design". We will work closely with QinetiQ, a member of the ATI, on the CFD optimisation in complex, nonlinear flows, work which directly addresses the ATI's national strategy for aerospace technology in the UK.


In addition to aerospace and aviation, computational fluid dynamics is used across a wide spectrum of UK industry: in civil engineering (e.g., water systems engineering), environmental engineering (e.g., flood prevention; wind turbines; carbon capture systems), mechanical engineering (e.g., automotive design), biomedical engineering (e.g., anaerobic digesters), the food industry and medical devices. In all these arenas optimisation is used to optimise a benefit, such as efficiency, safety, manufacturing or deployment cost, quality of service, energy consumption or health. Being able to effectively optimise these systems therefore clearly has direct and wide socio-economic benefits. The successful outcome of this project will result in new, efficient ways of optimising large, complex systems. These are systems that, because of their complexity, cannot currently be optimised and are therefore operated sub-optimally. The potential impact of this project is therefore very broad. Moreover the UK industrial CFD community is a significant one with industrial research and development of CFD ongoing as well as application through various specialist consultancies.

Optimisation is increasingly used throughout UK industry and commerce, as well as the public sector and throughout science. In addition to the directly CFD-related applications, the breadth of its use is indicated by applications in air traffic safety, radio and mobile telephone networks, drug design, supply chain configuration, renewable energy devices, energy and transport networks, additive layer manufacturing, automotive structures and so on. We therefore anticipate that the methods developed for evolutionary surrogate-assisted optimisation will have impact across a wide range of the UK economy. We will release open source code to facilitate widespread use of the work.

Public impact

The project will have public impact through Exeter and Surrey's public engagement and outreach programmes, such as F1 in Schools, Future Engineers and Formula Student. It will also impact school workshops interested in computer science and engineering.

Education and Training

This project will train three post-doctoral research associates and an Exeter-funded PhD student to work in at the interface between computational fluid dynamics, evolutionary computing and machine learning. In addition, by spending significant amounts of time working with our industrial partners, they will be able to understand and tackle real-world problems. It will also have an impact on the undergraduate Engineering and Computer Science degree programmes in Surrey and Exeter, through seminars and case studies, involvement in undergraduate teaching and final year projects. The project will therefore have impact by delivering highly skilled people to UK industry and commerce.
Description This grant has developed new methods for optimising engineering systems that involve fluid flow (for example, flow around an aircraft wing or in a vortex separator). It has introduced Bayesian optimisation, a recently developed method for optimisation, to the field of engineering systems involving fluids and developed new methods for the simultaneous optimisation of more than one objective. In addition, it has shown how to use multi-objective optimise industrial systems that are too complicated to model analytically or computationally; for example a coal-fired electricity generating boiler. This has been achieved by building machine learning models of the system from measurements which can then be optimised.
Exploitation Route The methods developed are general and are already being used by one of our collaborators. We have made open source software for the optimisation of these systems available and have a defined a set of real-world benchmarks for the evaluation of optimisers which can be used in further research. The Bayesian optimisation methods we developed are being used by us in the optimisation of digital twins for building energy and for transport. They have prompted further research (accepted for publication) on the most efficient methods for Bayesian optimisation.
Sectors Aerospace, Defence and Marine,Agriculture, Food and Drink,Digital/Communication/Information Technologies (including Software),Energy,Environment,Healthcare,Transport

Description The findings are being used by Hydro International, one of our collaborators, to design vortex separators for separating particulates in waste-water. The contribution of the research is to show how Bayesian optimisation may be used to find the shape of vortex separator that gives best separation; this is difficult because the evaluation of each proposed shape takes many hours of computer simulation. This has led to the granting of a patent assigned to Hydro International, one of our project partners. Hydro International is experimentally testing some of the designs, and a new vortex separator product is under development. An Innovate UK project has been granted to Hydro and Exeter (Investigators: Fieldsend and Tabor).
First Year Of Impact 2017
Sector Environment,Manufacturing, including Industrial Biotechology
Impact Types Economic

Description Knowledge Transfer Partnership
Amount £98,209 (GBP)
Funding ID KTP011008 
Organisation Innovate UK 
Sector Public
Country United Kingdom
Start 06/2018 
End 07/2020
Description Collaboration with Hangzhou University, China 
Organisation Hangzhou Dianzi University
Country China 
Sector Academic/University 
PI Contribution Research collaboration and hosting of a visiting researcher (Chunlin Wang) from Hangzhou University, China to work on optimisation of large scale coal fired boilers for power generation. Our contribution was to host the visiting researcher and to develop new methods for data-driven multi-objective optimisation. Two publications and an application to NSF China have resulted.
Collaborator Contribution Chunlin Wang and colleagues at Hangzhou University contributed expertise and data about the coal fired boilers.
Impact Publications: 1. Alma A. M. Rahat, Chunlin Wang, Richard Everson, and Jonathan Fieldsend. Data-driven multi-objective optimisation of coal-fired boiler combustion systems. Applied Energy. 2. Chunlin Wang, Yang Liu, Richard M Everson, Alma A. M. Rahat, and Song Zheng. Applied gaussian process in optimizing unburned carbon content in fly ash for boiler combustion. Mathematical Problems in Engineering, 2017. Application to NSFC (under review) entitled "Synthetic modeling and optimization for coupled boiler and coal mill systems based on operational data, field knowledge and image information."
Start Year 2016
Title Separator for Separating Solids From a Fluid 
Description A separator for separating solids from a fluid including a tray assembly, the tray assembly including a plurality of nested tray units which define a separator axis and are spaced apart from one another along the separator axis, wherein each tray unit includes comprising an inner surface facing the separator axis 16 extending outwards, away from an aperture in the tray unit disposed at the separator axis, wherein the inner surface comprises an inner portion and an outer portion, wherein the inner portion is disposed between the aperture and the outer portion, and wherein the gradient of the outer portion is greater than the gradient of the inner portion. 
IP Reference US2020108333 
Protection Patent granted
Year Protection Granted 2020
Licensed No
Impact The patent is held by our collaborators, Hydro International. They are currently developing new products (fluid separators) based on the technology described under the auspices of a KTP with Profs Tabor and Fieldsend.