This studentship has been developed by the University of Liverpool in partnership with Aircraft Research Association Ltd.
The digital age with ubiquitous physics-based computational engineering tools, such as computational fluid dynamics (CFD), machine learning algorithms and ever-increasing computing power, helped accelerate the development of novel technologies deployed in the civil transport sector, as well as in defence and security, to meet the most demanding economic, environmental and societal challenges. One such example is when multidisciplinary CFD analysis is not only used routinely in the design of next-generation aircraft but also in the preparation of an experimental wind-tunnel test campaign to explore the parameter design space in a comprehensive and cost-effective manner, while ensuring the safe operation of the test. Notwithstanding, experimental studies, having greatly benefited from digitally enhanced data acquisition themselves, are essential in validating and fine-tuning numerical models, and even wholly predictive model building through machine learning, particularly when edge-of-the-envelope flow physics are involved.
Consequently, for the foreseeable future it is inconceivable that high-performance aircraft (and many other game-changing technologies) can be designed without physical wind-tunnel testing. Indeed, practical numerical methods often lack either prediction accuracy or the capability to model some physical phenomena altogether, or both. On the other hand, physical wind-tunnel testing not only becomes expensive when rapid design changes are sought (for which numerical tools are better suited), but, just as numerical tools, are subject to various uncertainties stemming e.g. from wind-tunnel corrections to account for the effects of wall constraint and flow field modification due to the chosen measuring technique or the fundamental flow characteristics of the wind tunnel itself. Experiment and simulation will remain in a symbiotic relation to produce highest-quality data and also to optimise experimentation, even to the extent of potentially increasing the efficiency (i.e. optimised power consumption) of industrial wind-tunnel testing.
Hence, fusion of disparate data from disparate sources (including both experiment and simulation) is paramount, promising step-changes in prediction capability overall to improve high-value design. Future design paradigms will use trusted discipline-based data models with quantified confidence levels. This is the overarching aim of the project. Specifically, it is envisaged to first explore future algorithms, including AI surrogate models, for near real-time joint experimental/numerical data analysis, that is uncertainty-aware, robust and quantifiable, to inform and optimise a wind-tunnel campaign, including on-the-fly. Second, considering the vast amount of data that a high-fidelity CFD run and a fully instrumented wind-tunnel test can produce, particularly for unsteady flow simulations, the first objective calls for high parallelisation utilising future computing systems, such as those explored within this CDT.
This project is part of the EPSRC Funded CDT in Distributed Algorithms: The What, How and where of Next-Generation Data Science. https://www.liverpool.ac.uk/research/research-at-liverpool/research-themes/digital/cdt-distributed-algorithms/
The University of Liverpool is working in partnership with the STFC Hartree Centre and other industrial partners from the manufacturing, defence and security sectors to provide a 4-year innovative PhD training course that will equip over 60 students with the essential skills needed to become future leaders in data science, be it in academia or industry.
ach student is based at the University of Liverpool and every project within the centre is offered in collaboration with an Industrial partner who as well as providing co-supervision and placements will also offer the unique opportunity for students to access state of the art computing platforms, work on real world problems, benchmarking and data. Our graduates will gain unparalleled experiences working across academic disciplines in highly sought-after topic areas, answering industry need.
As well as learning from academic and industrial world leaders, the centre has a dedicated programme of interdisciplinary research training including the opportunity to undertake modules at the global pinnacle of Data science teaching. A large number of events and training sessions are undertaken as a cohort of PhD students, allowing you to build personal and professional relationships that we hope will lead to research collaboration either now or in your future.
The learning nurtured at this centre will be based upon anticipation of the hardware resources arriving on desks of students after they graduate, rather than the hardware available today.
For information technical queries please contact Dr Sebastian Timme ([email protected]
For general application process queries contact Kelli Cassidy: [email protected]