Research Assistant/Associate in "Inverting Turbulence: Flow Patterns and Parameters from Sparse Data"

Our ability to compute turbulent flows with scale-resolving simulations, like Large Eddy and Direct Numerical simulations, has grown tremendously in the past decades. In these simulations, problem parameters and boundary conditions are specified, and the forward problem is solved. In many real-life settings however, this information maybe uncertain or not available at all.  For many of these turbulent flows observational data, such as velocity or scalar measurements, are available at several (static or moving) sensor locations. These observational data can be assimilated with the governing equations to recover the missing information. This is known as the inverse problem and in this sense, turbulence is "inverted".

Many approaches have been proposed to solve the (ill-conditioned) inverse problem that can be broadly classified into two large categories: optimisation methods (such as data assimilation) and probabilistic methods (Bayesian inference). These methods are however either very time consuming or quickly become unstable for turbulent flows (due to the so- called "butterfly effect").

In this project, we aim to break the impasse by formulating a new data-assimilation algorithm, which is stable when applied to turbulent flows, and has affordable computational cost. We will apply the new approach to an environmental problem, flow and pollutant dispersion around a building. Success in this endeavour can open a new direction of research with many applications in other single or multi-phase flow problems.   

Duties and responsibilities

• Perform DNS (or LES) of turbulent flow and scalar dispersion around a building
• Formulate and implement a stable data-assimilation algorithm
• Examine accuracy, stability, and convergence properties of the algorithm
• Draft journal papers
• Attend relevant workshops and present at conferences

Essential requirements

The successful candidate should already hold, or be near the completion of PhD (or equivalent) in Engineering or a closely related discipline.

Those appointed at Research Associate level

Hold a PhD (or equivalent) in Engineering or a closely related discipline.

Those appointed at Research Assistant level

Near completion of a PhD (or equivalent) in Engineering or a closely related discipline. 

*Candidates who have not yet been officially awarded their PhD will be appointed as Research Assistant

Further Information

For queries relating to the post please email Prof. G. Papadakis g.papadakis@ic.ac.uk

For technical issues when applying online recruitment@imperial.ac.uk

For queries regarding the recruitment process please contact Lisa Kelly: l.kelly@imperial.ac.uk

The College is a proud signatory to the San-Francisco Declaration on Research Assessment (DORA), which means that in hiring and promotion decisions, we evaluate applicants on the quality of their work, not the journal impact factor where it is published. For more information, see https://www.imperial.ac.uk/research-and-innovation/about-imperial-research/research-evaluation/

The College believes the use of animals in research is vital to improve human and animal health and welfare. Animals may only be used in research programmes where their use is shown to be necessary for developing new treatments and making medical advances. Imperial is committed to ensuring that, in cases where this research is deemed essential, all animals in the College’s care are treated with full respect, and that all staff involved with this work show due consideration at every level.

http://www.imperial.ac.uk/research-and-innovation/about-imperial-research/research-integrity/animal-research/ 

Closing date: 31^st Dec 2022

To apply, visit www.imperial.ac.uk/jobs and search by the job reference ENG02367.

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