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New methods for fluids, plasma, porous media and composites for technological solutions

Projects in progress:

TitleSelect to sort (ascending) Description Research StudentSelect to sort (ascending) Supervisor
Alternative protein sources: growing the next generation computational modelling framework

The alternative protein space represents one of the most dynamic scientific areas at present. Shrinking usable land mass, even accounting for agricultural advances, means a sustainable future is tightly linked with our ability to create and support clean food sources. Computational modelling has an immense (and largely untapped) potential to innovate a technological space still in its infancy. What about this HetSys project? We will use a combination of state-of-the-art techniques - multi-physics fluid mechanics, high performance computing and data-driven approaches - to design a versatile open-source modelling framework supported by fantastic experimental and industrial collaborators from around the world.

Minerva Schuler

(Cohort 5)

Radu Cimpeanu, Ferran Brosa Planella
Blending ultrasound data with physics-based models to predict damage in structural systems

Monitoring of damage degradation in materials is vital as any cracks will reduce local stiffness, accelerating the ageing process of the physical assets. In-situ ultrasonic monitoring data is valuable for giving the inspectors information on the level of what may be wrong with the structural systems. However, purely data-driven representations do not always give inspectors enough information and the ability to predict how the system will behave in the future. This can be achieved by utilising some knowledge of the physics that underpins the system. This project will use the fusion of experimental data and physical models for failure prediction of materials, by taking measurements and developing models that are hybrid in nature.

Nojus Plungė

(Cohort 5)

Emmanouil Kakouris, Rachel Edwards
Developing the capability to forecast extreme Space Weather events

Extreme Space Weather is driven by large-scale eruptions from the Sun called coronal mass ejections. Upon arrival at the Earth, these produce amazing auroral displays but also endanger satellites and astronauts, and disrupt communications and power grids. Forecasting these events is of primary importance to the UK MET Office, one of three centres world-wide providing round-the-clock space weather predictions. This project, in collaboration with the UK MET Office, will develop state-of-the-art plasma simulations to forecast extreme Space Weather and develop advanced statistical techniques to quantify the uncertainties in their prediction.

Yihui Tong

(Cohort 5)

Ravindra Desai, Jeremie Houssineau
Hopping through the interfaces: a multiscale chemo-mechanics model for energy materials Mechanical damage arising from electrochemical processes in energy materials can alter significantly their mass transport capability, and overall performance of energy storage systems. The damage is frequently initiated at material’s internal interfaces, subsequently disrupting ionic and electronic conductivity paths. The coupling between interfacial damage and ionic transport is not yet fully understood, and requires description of its origins at the nanoscale. This project will provide enhanced understanding of the damage-transport coupling for various interfaces in energy materials across the length scales by developing a novel data-driven multiscale methodology based on the Bayesian inference, linking first-principles calculations with the continuum modelling framework, and subject to physical constraints Chantal Baer (Cohort 4) Lukasz Figiel;
Bora Karasulu
When the dust settles: predicting deposition of particulate and aerosols Predicting the deposition of ultra-fine particulate and aerosolized drops is important in a wide range of applications: from understanding pathogen or drug-laden droplet deposition in the respiratory systems to determining the composition of airborne particulate matter using environmental sensors. The physics at play is diverse, and its accurate prediction requires a multi-scale and multi-physics model, far beyond the current state-of-the-art. Such a model will combine techniques from fluid dynamics, kinetic theory, Langevin dynamics, and uncertainty quantification, and tackle a broad range of physics including rarefied gas dynamics, creeping flow, micro-scale evaporation, and Brownian motion Anson Lee (Cohort 4) Duncan Lockerby, James Kermode
Fundamental physics or data science? Why not both: a data-driven modelling framework for interfacial microflows This exciting project lives at the interface between multi-physics modelling, high performance computing and data-driven approaches. The 21st century has brought a revolution in micromanufacturing techniques (LCD, 3D printing etc.) that require understanding and efficient deployment of knowledge at scales below those currently accessible. Enter data-driven equation discovery techniques: novel surrogate modelling methods which can provide insight in scenarios in which simulation or experimental data are available, but traditional derivation approaches break down. Our challenge is to create a new computational framework that harnesses the power of these approaches towards generating new meaningful understanding of fluid flows at small scales. Sebastian Dooley (Cohort 4) Radu Cimpeanu; James Sprittles; Albert Bartok-Partay
Coupling fluid and kinetic codes for laser-driven inertial fusion energy simulations Coupling kinetic solutions of laser-plasma interactions to large-scale fluid simulations will help optimise experiments aimed at achieving thermonuclear fusion driven by lasers. Andrew Angus (Cohort 1) Tony Aber
Investigating the impact of equation of state uncertainties on direct-drive inertial fusion energy simulations Direct-drive inertial fusion energy1 (IFE) requires high-energy lasers to be focussed on a spherical target. The outer material of the target (usually plastic) ablates, driving an implosion of the core deuterium-tritium (DT) fuel. To design efficient future experiments and interpret previous ones an accurate and predictive computational modelling capability is required. This must include a formal understanding of the sources and magnitudes of uncertainty. This project will investigate the uncertainty in direct-drive IFE calculations arising from the equations-of-state used. The primary outcome of the project will be an uncertainty quantification (UQ) framework that could also be applied to other areas of uncertainty, such as opacity, emissivity and thermal transport. Charlotte Rogerson (Cohort 2) Tom Goffrey
Using surrogate models to optimise designs for laser-driven fusion power production Laser-plasma experiments in high-energy density physics (HEDP) and fusion research trigger kinetic scale instabilities whose effects must be included in large-scale fluid simulations. The difference in time and spatial scales between the kinetic and fluid models, along with the cost of the kinetic modelling, have hindered the full inclusion of important kinetic processes in laser-driven fusion simulations. It is critical for the design and interpretation of experiments that this is solved. This project aims to develop a surrogate model for the kinetic processes suitable for including in fluid scale simulations. Ben Gosling (Cohort 3)

Tony Arber, Tom Goffrey and Keith Bennett

Adaptive probabilistic meshless methods for evolutionary systems This project will develop and implement a new class of numerical solvers for evolving systems such as interacting fluid-structure flows. To cope with extreme strain rates and large deformations these new solvers will be adaptive and meshless, and they will also implicitly represent their own solution uncertainty, thus enabling optimal design and uncertainty quantification. This exciting project brings together aspects of continuum mechanics, numerical methods for partial differential equations, and statistical machine learning. Tadashi Matsumoto (Cohort 2) Tim Sullivan
Computational Modelling of Leidenfrost Fractals The Leidenfrost effect levitates small liquid drops above hot surfaces via a strong evaporative flux and is seen when water droplets skate across a hot pan. Understanding when this occurs is critical for the efficiency of numerous technologies, including the spray-cooling of next-generation electronics that our collaborators at Nokia Bell Labs are developing. Experiments at Bell have recently revealed counter-intuitive contact dynamics that display fractal surface wetting in competition with the nanoscale vapour flow under impacting droplets. This project will use unconventional mathematical modelling combined with computational techniques to gain unprecedented insight into this phenomenon combined with theory-driven targeted experiments at Bell. Peter Lewin-Jones (Cohort 2) James Sprittles
Fluctuating Hydrodynamics for Liquid Spreading over Heterogeneous Surfaces Understanding the spreading of liquids over heterogeneous solid surfaces is the key to numerous emerging technologies (e.g. 3D ‘metaljet’ printing) and biological systems (retention of rain by leaves). Jingbang Liu (Cohort 1) James Sprittles
Nonlinear, but under control: a hierarchical modelling approach to manipulating liquid films We are surrounded by situations that depend on a controlled outcome in our day-to-day lives, ranging from controlling the evacuation of crowds, to efficient drug delivery, or cooling systems inside computing centres. Most real-life scenarios rely on complicated models which are too complex to tackle analytically or computationally. Using the framework provided by a beautiful and rich physical problem – controlling nonlinear waves in falling liquid films – the project will provide opportunities to develop analytical and computational multi-physics tools. Acting in tandem for the first time, they become sufficiently powerful to translate robust theoretical strategies into realistic technological solutions. Oscar Holroyd (Cohort 3) Radu Cimpeanu, Susana N. Gomes
Atomistically-informed continuum interface models for functional composites Functional composites are material candidates for high-energy density applications. Their overall energy density can be enhanced by tailoring constituent dielectric properties, breakdown strength, and interfacial polarisation. Aravinthen Rajkumar (Cohort 1) Lukasz Figiel
Statistics of porous media attributes and mixing processes state variables across scales Typical observations of porous media attributes arise from a variety of techniques which have their own spatial resolution and associated uncertainty. Therefore, key statistics of parameters driving transport processes in porous media vary across scales and a scientific foundation for the characterization of basic transport mechanisms requires understanding of all relevant processes across the relevant length and time scales. In this project, we develop a theoretical and computational framework to assimilate data associated with diverse variables collected at a range of scales and combine these to provide predictions of solute dynamics and associated uncertainties. Alisdair Soppitt (Cohort 2) Mohaddeseh Mousavi-Nezhad
Uncertainty Assessment of Solute Mixing in Heterogenous Porous Media The transport of chemical substances in porous media is relevant in many different applications. For example, for the assessment of nuclear waste deposition sites or for the coordination of remediation actions after a contamination hazard, predictive simulation tools are required. These tools have to account for the uncertainty in the flow behaviour and soil parameters, since measurements of the porous media structure and complex turbulent flow behaviour are typically very scarcely available [1-2]. The main goal is to develop a simulation framework for tracer flow and transport that provides probabilistic information about local tracer concentration evolutions. In this context, a probability density function (PDF) method will be developed. It accounts for advective transport, pore-scale dispersion, and chemical fluid phase reactions. A multi-level stochastic method will be developed, which allows to combine expensive methods with cheap approximate solvers to achieve output statistics more efficiently. Furthermore, advanced data science methods for transport uncertainty quantification will be used. Matthew Harrison (Cohort 1) Mohaddeseh Mousavi Nezhad