Computational Modelling of Compressible Multi-material Flow with Application to Energy Generation

Project Description

An opportunity exists to undertake a PhD with the Matar Fluids Group (https://www.imperial.ac.uk/matar-fluids-group/) in the Department of Chemical Engineering at Imperial College London. The successful student will contribute to the modelling and simulation of multi-material and multiphase high-energy density flows, with application to novel, inertially-confined, nuclear fusion schemes at First Light Fusion (FLF) Ltd. Fusion can provide a clean baseload power supply, which is inherently safer and more secure than fission power. There is an increasingly urgent need for such new technologies to act on climate change. 

FLF is a focused and agile corporation researching energy generation by inertial confinement fusion. The company was spun out from the University of Oxford in June 2011 and is based near Oxford. Inertial confinement fusion for energy generation is a well-established research field and is being pursued in many laboratories worldwide, perhaps most notably in the US at the National Ignition Facility. FLF is exploring several alternative research directions that harness the same fundamental physics, with the prime focus being power generation. FLF’s work to-date has included theoretical analysis, detailed numerical simulation using in-house HPC facilities, and experimental validation. This has allowed description of the accessible parameter space and led to a clear vision of the pathway to fusion. 

Hydrodynamic material interface-tracking represents a challenging problem in terms of both accuracy and computational model robustness. Understanding the dynamics and mixing of materials is critical to the successful design of an inertial confinement fusion target, where high-energy densities also bring additional complexities to the computational fluid models. In general, it is difficult to obtain a detailed understanding of fusion target performance from experimental methods alone. There is therefore a heavy reliance on computational tools for predicting, understanding and extrapolating the parameter design space.
 
It is expected that the successful student will develop the current fluid interface-tracking capabilities of the Matar Fluids Group to include fluid compressibility using the OpenFOAM CFD code. An emphasis on improving the existing state-of-the-art in hydrodynamic methods that conserve mass, energy, and momentum across the interface, as well as the treatment of diffusive processes is anticipated. Code validation and verification will potentially be performed against FLFs in-house numerical tools and experiments, as well as other sources. 

The student will receive support from the Matar Fluids Group, and will have access to its high-performance computing facilities in addition to those provided by Imperial College London. In return for PhD sponsorship, the successful candidate would be required, in the first instance, to join FLF’s numerical physics team on a part-time basis, then full-time after successful completion of their PhD studies.

 

Funding Notes

The PhD scholarship is available from October 1st 2019 and is open to all UK applicants. The scholarship covers both the tuition fees and an annual tax-free bursary, and its standard period is 42 months. The successful applicant is expected to have obtained (or be heading for) a First Class Honours degree at Master’s level (or equivalent) in chemical engineering, another branch of engineering or related science. The post is based in the Department of Chemical Engineering at Imperial College London (South Kensington Campus).


Hybrid Machine-Learning and Computational Fluid Dynamics Methods in the Energy Industry

Project Description

An opportunity exists to undertake a PhD with the Matar Fluids Group (https://www.imperial.ac.uk/matar-fluids-group/) in the Department of Chemical Engineering at Imperial College London funded by BP. The successful student will contribute to the modelling and simulation of multiphase flows using hybrid methods that rely on a combination of machine-learning and computational fluid dynamics. 

Engineering applications of turbulent multi-phase flows typically involve optimising hyper-parameters (related to flow, geometry etc.) to maximise a defined performance metric. In the energy industry, in spite of decades of research, there are a number of significant challenges; overcoming them will lead to a step-change in productivity, efficiency and reduction in emissions. For instance, three-phase flows comprising oil, water, and air, are exceedingly complex and feature poorly understood dynamics, phase formations and transitions. Characterising the three-phase mixture properties, e.g. rheology, is complex, and the prediction of the system behaviour is fraught with large uncertainties.
 
There is also a dearth of predictive models used by industry that can handle fluids that are either Newtonian but have viscosities that exceed those of water by orders of magnitude (‘heavy’ oils), or exhibit highly-complex rheological behaviour. The majority of current models do not provide accurate predictions in terms of flow pattern maps, phase holdup, and pressure gradient when dealing with such systems. Determining sensitivity of the predictions to the use of the numerous closures depending on the flow regime (a common feature of models in the energy industry) through any kind of statistical analysis is also, to a large extent, not part of the workflow in the modelling process. The issues associated with this lack of robustness can be propagated to higher level simulators (e.g. for reservoirs) with a profound impact on the design of production facilities that rely critically on the quality of the models. We either need a physics-based model to disentangle the individual effects of geometry, chemistry, temperature and pressure, and physico-chemical factors on the flow behaviour (difficult to achieve); or a predictive framework through a hybrid approach involving a combination, and tight integration, of data and mechanistic models for solutions with well-defined uncertainty.

 

We focus in this project on modelling fluid-fluid displacements during Enhanced Oil Recovery (EOR) and well bore clean-up, central to energy applications, cross-cutting a number of EPSRC research areas, e.g. energy efficiency, fluid dynamics and aerodynamics, and continuum mechanics. Automated, efficient, derivative free, surrogate model-based optimisation will be developed to replace manual hyper-parameter CFD tuning (current practice), to deal with the 3D flows, strongly-coupled variables, and complex geometries in our applications. 


Informal enquiries about the post and the application process can be made to Prof. Omar Matar () by including a motivation letter and CV.

Funding Notes

The PhD scholarship is available from October 1st 2019 and is open to all UK applicants. The scholarship covers both the tuition fees and an annual tax-free bursary, and its standard period is 42 months. There will also be an opportunity for a three-month placement at BP Sunbury office to gain industry experience. The successful applicant is expected to have obtained (or be heading for) a First Class Honours degree at Master’s level (or equivalent) in a branch of engineering or related science. The post is based in the Department of Chemical Engineering at Imperial College London (South Kensington Campus).


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