Alistair Revell, MEng, PhD, FHEA

Prof, Dir

Accepting PhD Students

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Personal profile


Alistair graduated from UMIST in 2002 with a degree in Aerospace Engineering with French, during which time he also completed a research placement at ENSMA in Poitiers, France. He received his PhD in Turbulence Modelling and Computational Fluid Dynamics (CFD) at The University of Manchester in 2006, including a year at IMFT, Toulouse as a Marie Curie Fellow, 6 months in Paris with the EDF Code_Saturne development team and a Summer project at CTR, Stanford University. He was appointed as a Temporary Lecturer in 2007 to develop Aerospace teaching in Spacecraft Flight and Turbulence Modelling and Simulation. In 2011 he undertook a sabbatical at CIEMAT, Madrid to work on immersed boundary methods and Fluid Structure Interaction, and in 2020 he had a (curtailed) sabbatical at The University of Melbourne to work on machine learning for turbulence modelling.

He previously acted as Director of Social Responsibilty (covering a range of things including outreach, equality & diversity and environmental sustainability) for 5 years. Following a period of several years as deputy Head of Department he is currently Head of Department of Fluids and Environment, and deputy Head of School of Engineering. This is part of a pilot structure within the school-wide Future of Engineering project. 




Research interests

My research spans a range of computational engineering work, with a primary focus on turbulence modelling and simulation across different domains (external aerodynamics, internal thermal hydraulics and some cardiovascular flows). I have also got experience in development and application of methods for fluid-structure interaction (flow control, bio-inspired engineering) as well as substantial involvement in the development of novel software for future computational architectures (in particular lattice Boltzmann method on GPUs, interactive simulation, virtual reality). 

My applied work in the Aerospace and Automotive sectors includes involvement in multiple projects focussed on improving the prediction of turbulence modelling & simulation, and novel strategies for drag reduction. In the energy sector, and in particular in the context of The Modelling & Simulation Centre, I work closely with stakeholders in the Nuclear energy industry where we are helping to translate developments from research projects into open source engineering software used in this sector. More recently I have also been working on a range of projects related to cardiovascular flow, to assess and quantify the role of turbulence in the human body in various cases.


  • Turbulence Simulation (Hybrid RANS-LES Methods, Synthetic Turbulence)
  • Computational Fluid Dynamics across aerodynamics, thermal hydraulics and bioengineering applications.
  • High Performance Computiong, Interactive/Realtime/Immersive Simulation

My group


Expressions of interest are welcomed from potential PhD students. A list of possible research topics for PhD is provided below, organised by the main areas of my research. These are examples of relevant projects either recently started or currently in development. If you have any questions or ideas how to extend or compliment these topics please get in touch!

1) Turbulence Modelling & Simulation

a) Turbulence Simulation for complex geometry environments using Physics-informed Machine Learning
The environmental and economic needs for disruptive improvement in predictive fluid dynamics capability require a new paradigm for the analysis of very large data sets. Complex fluid dynamics applications often push the limits of traditional turbulence models and give rise to non-local flow features which are intrinsically beyond their reach. Particularly so in energy and transport sectors - the focus of this project - where, for instance, engine and aerodynamic efficiency impact directly on the environment. It is computationally unfeasible to apply the more accurate Direct/Large Eddy Simulation across the entire domain and so there is a clear need for methods that offer intelligent compromise. There is growing recognition that future turbulence simulation methods must use machine learning algorithms with the data and results of experiments and high-fidelity simulation methods to improve turbulence models.  Physics-informed Machine Learning (PIML) is the process of updating the functional form of Reynolds stresses in turbulence models based on available data and will complement world-leading expertise in turbulence within the School of MACE. The philosophy in the present project is to develop and employ PIML enhanced turbulence models in an embedded eddy simulation approach, which adapts automatically to the geometry according to variations in flow complexity.

b) Dual finite volume - lattice Boltzmann turbulence simulation for interactive aerodynamic analysis
This project develops an existing virtual wind tunnel prototype designed for automotive design and engineering analysis. The existing system allows the import of digital models into a virtual reality (VR) environment as well as real-time simulation of the air flow around the geometry. This project will develop this capability further, extending the approach to allow the simulation of higher Reynolds number flows in real-time. The major proposed innovation versus the prototype is to limit turbulence simulation to a subset of the virtual domain, running on a GPU-accelerated solver, while the rest of the domain is conducted using lower order methods on CPU. In addition, a new, more efficient integration between the solver and the visualisation environment will be developed and more effective means of interaction and visualisation introduced. The approach to achieving this through this project is threefold; 1) The operational range of the flow in the tunnel will be extended by including Hybrid RANS-LES turbulence modelling. 2) Levels of interactivity will be increased to facilitate a wider range of parameter variations. The ability to specify flow direction and upstream turbulent intensity at run-time will be added to the solver interface and exposed to the user through the game engine. 3) Bottlenecks associated with GPU-CPU data transfer and mapping of simulation data will be circumvented through reorganisation of the data storage patterns. The work will be supervised by Drs Adrian Harwood and Alistair Revell based at the School of Mechanical, Aerospace & Civil Engineering.

c) Zonal RANS modelling using Artificial Intelligence
With ever-increasing computational power and improved user understanding of scale resolving methods, novel developments in RANS have reduced significantly. However there remains a clear need for efficient and reliable RANS modelling, which is likely to remain for the foreseeable future. Industrial design in particular would benefit from faster and more accurate schemes to reduce design cycle whilst examining a broader range of parameters. Many other applications need only bulk flow quantities and thus requisite time averaging with scale-resolving methods limits scope. The need for true grid-refined solutions is also difficult to meet with LES and hybrid methods; both of which tend naturally towards DNS. However in spite of these motivations for RANS progress has stalled on account of wide range of accuracy reported for some flows. Part of the problem with RANS is, given their limited degrees of freedom, they tend to be developed to be good predictors of a particular physics at the expense of others. In particular there is a need for simple models capable of predicting turbulence anisotropy; i.e. NLEVM or EARSM. For a specific range of flow types their ‘predictive efficiency’ (i.e. accuracy/convergence time) is significantly higher than full stress transport modelling approaches. The expertise in turbulence modelling then lies in understanding which model works best where. The objective of this project is to develop a framework to identify a general scheme that enables different RANS models to be used at different physical locations in a domain, potentially in a combination of both steady and unsteady mode. A major element of this work would then be the development of a Artificial Intelligence system to guide the sizing of a zone, and the selection of which model to be used within it. Ultimately the framework should become as automated as possible, with minimal user input.
2) Bioinspiration for engineering

d) Elucidating the role of wing flexibility in insect flight mechanics
The understanding of flapping flight of insects and bio-inspired micro air vehicles requires detailed insight into aerodynamics and structural mechanics. This is an interesting multi-disciplinary problem involving not only the generation of aerodynamic forces for weight support against gravity, but also the significance of structural properties of the wing frame itself and insight into the feedback control mechanism governing flapping kinematics. Most of the flapping flight research to date assumes rigid wings. Clearly, insects exploit their wing flexibility in different ways; however, understanding in this area remains incomplete. This project offers an exceptional PhD candidate the opportunity to work with a world-leading team of engineers to advance this field. The project will investigate the flight mechanics of flexible insect-inspired wings using a combination of numerical, theoretical and experimental techniques. In the first instance we will employ the lattice Boltzmann method, coupled to a finite element solver via the immersed boundary. We will then provide detailed validation of our in-house computational software against a series of lab-based tests designed to investigate the role that wing flexibility plays on flight mechanics. Following subsequent model refinement we aim to envisage a wide parametric investigation in terms of wing construction, shape and flapping motion. A detailed investigation of this kind into the role of wing flexibility has hitherto remained beyond reach of engineering research and as such this project will enable significantly improved understanding of flight mechanics relevant to the advancement of the field of micro-air vehicles and will inform evolutionary biologists on the role of wing morphology within life style of insects. 

e) Piezo-electric/photo-voltaic material for passive energy-harvesting at scale
In this project a concept for simultaneously harvesting both solar and wind energy via an array of flexible flaps combining photovoltaic cells with piezoelectric material will be developed. The primary application is to provide continuous power supply to remote sensors or platforms where a power supply is required over a long duration. Inspiration is taken from the motion of flexible structures in the natural world. Whilst yield from a single such device would be low, the concerted motion of an array of many is envisaged to provide a low cost and reliable source of power. The project would contribute to existing work in this area at the School of MACE and will provide a framework for learning aspects of both numerical and experimental work. Specific tasks include: 1) Survey of state-of-the-art for energy harvesting technology; 2) Testing of available of-the-shelf micro solar panels and identification of potential candidates for dual piezo-solar energy harvesters. 3) Development of  semi-empirical model for projected energy generation. 4) Conduct basic wind tunnel tests of the inverted-flag piezo energy harvester.  5) Comparison of experiment with numerical simulations to provide validation of the numerical tool. Conduct simulations to extend parametric testing of the inverted flag concept.

f) Nature inspired flow control for aircraft aerodynamics

The field of biomimetics allows one to mimic designs and ideas gleaned from the observation of the natural world. In the context of flow control, aerodynamicists have long taken inspiration from birds and fish in search of viable techniques for drag reduction and lift enhancement. Inspired by the pop up of birds feathers in certain flight modes, the amelioration of aerodynamic performance via a Porous and ELastic (PEL) coating has been the focus of recent research. The PEL coating works by reconfiguring/adapting to the separated flow, thereby directly changing the near-wall flow and the subsequent vortex shedding; which can lead to reduced form drag by decreasing the intensity and the size of the recirculation region. In contrast to classical Vortex Generators, which are fixed, and act upstream of the separation point, the flexible PEL coating will be activated only when the boundary layer is separated, and will interact with the flow recirculation zone to reduce drag via interaction with the near wall flow. The objective of this project is to investigate the performance benefit these technologies can deliver for flow at higher Reynolds number, relevant for the next generation of aircrafts. This achievement would contribute to reduced CO2/NOx emissions and improved flight performance in terms of delayed stall and reduced vibrations induced by e.g. gust loading; especially during take-off and landing where boundary layer separation plays a crucial role.
3) Biomedical Engineering

g) Biomechanical Investigation of Repaired Tetralogy of Fallot and Coarctation of Aorta
Congenital Heart Disease (CHD) is a heart condition resulting from an abnormality in heart structure or function that is present at birth. In the UK alone, there are about 4,600 babies born with congenital heart disease each year. Computational Fluid Dynamics (CFD) has had a profound impact on cardiovascular medicine in the past decade and will be used in this PhD project to assess its potential as an assistive tool in diagnosis and treatment of two CHD conditions, namely Tetralogy of Fallot (ToF) and Coarctation of Aorta (CoA). This interdisciplinary project will also identify haemodynamic features that correlate with need for re- operation in the case of ToF and as a tool to predict hypertension in CoA. The overall aim of this interdisciplinary PhD project is to explore the potential of novel Computational Fluid Dynamic (CFD) approaches in modelling two CHD lesions including Tetralogy of Fallot (ToF) and Coarctation of Aorta (CoA) and to accelerate the development of innovative nonsurgical (catheter/transcutaneous) solutions. This is achieved through the following main objectives: 1) Data collection from 8-10 child patients; 2) Conduct CFD simulations to evaluate key hemodynamic conditions for both ToF and CoA; 3) Perform morphological characterisation of the image sets and compare with CFD to identify post-repair evolution in the patient-specific models; 4) Incorporate additional data from the registry such as genetic characterisation and modifiable/non-modifiable risk factors into a data-mining framework.
h) A neural-networks lattice Boltzmann platform to develop functional gradient generation in bioreactors.
The successful in vitro engineering of tissue implants, in particular Osteochondral (OC), is strongly dependent on our ability to: 1) generate 3D scaffolds capable of mimicking the functional gradients of the native tissue (bone and cartilage); 2) replicate under dynamic conditions the in vivo physiological environment that drives stem cell differentiation towards chondro and/or osteogenic lineages. Bioreactors offer the possibility to provide those conditions by employing, in a controlled manner, different media flow regimes, mechanical loads and electrical stimulus to the cell-scaffolds systems. Despite significant advances in the field, current bioreactor technology remains limited to the stimulation of homogenous tissues and is therefore unable to provide differential stimulus to stratified TE constructs such as osteochondral models (comprising a cartilage and a bone part). Bioreactor design remains a highly complex multi-parameter problem and in-silica support is essential. A recently developed in-house software using lattice Boltzmann method optimised for computation on graphical processing units (GPU) will facilitate a large number of simulations to provide data for neural networks. Required code developments will include incorporation of thermal effects, coupled species transport, kinetics models and a receptor-ligand model for cell attachment. The code will first be validated against existing and on-going lab work; testing first isolated and then combined effect of various stimuli. Subsequently we will apply the use of convolutional auto-encoders in the context of a neural network approach with a proven ability to significantly reduce the state size of a fluid dynamics simulation. The resulting tool will be able to efficiently explore the design space towards tailored functional gradient generation and controlled structural properties during the scaffold degradation / tissue growth phase.

My group

My research group focusses on the development of computational fluid dynamics methods for industry and healthcare applications. We specialise in turbulence modelling and simulation, fluid structure interaction and interactive engineering simulation tools

Job vacancies




Director of the Modelling & Simulation Centre (MaSC)

Expertise related to UN Sustainable Development Goals

In 2015, UN member states agreed to 17 global Sustainable Development Goals (SDGs) to end poverty, protect the planet and ensure prosperity for all. This person’s work contributes towards the following SDG(s):

  • SDG 3 - Good Health and Well-being
  • SDG 7 - Affordable and Clean Energy
  • SDG 9 - Industry, Innovation, and Infrastructure
  • SDG 13 - Climate Action

External positions

External Examiner (MScs in Automotive & Mechanical Engineering), Coventry University

Sept 2016Sept 2020

Full EPSRC Peer Review College Member, Engineering & Physical Sciences Research Council (EPSRC)

Sept 2016 → …

Remote Evaluator/Cross Reader for Horizon2020, European Commission

Nov 2015 → …

Visiting Scientist , S T F C Daresbury Laboratory

Jan 2014 → …

Areas of expertise

  • TJ Mechanical engineering and machinery
  • Computational Fluid Dynamcs
  • Turbulence Modelling and Simulation
  • Fluid Structure Interaction
  • Novel Computer Hardware
  • Interactive Simulation
  • Biofluid Mechanics

Research Beacons, Institutes and Platforms

  • Aerospace Research Institute
  • Energy
  • Digital Futures


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