PREdictive Modelling with QuantIfication of UncERtainty for MultiphasE Systems (PREMIERE)

Lead Research Organisation: Imperial College London
Department Name: Chemical Engineering

Abstract

PREMIERE will integrate challenges identified by the EPSRC Prosperity Outcomes and the Industrial Strategy Challenge Fund (ISCF) in healthcare (Healthy Nation), energy (Resilient Nation), manufacturing and digital technologies (Resilient Nation, Productive Nation) as areas to drive economic growth. The programme will bring together a multi-disciplinary team of researchers to create unprecedented impact in these sectors through the creation of a next-generation predictive framework for complex multiphase systems. Importantly, the framework methodology will span purely physics-driven, CFD-mediated solutions at one extreme, and data-centric solutions at the other where the complexity of the phenomena masks the underlying physics. The framework will advance the current state-of-the-art in uncertainty quantification, adjoint sensitivity, data-assimilation, ensemble methods, CFD, and design of experiments to 'blend' the two extremes in order to create ultra-fast multi-fidelity, predictive models, supported by cutting-edge experimental investigations. This transformative technology will be sufficiently generic so as to address a wide spectrum of challenges across the ISCF areas, and will empower the user with optimal compromises between off-line (modelling) and on-line (simulation) efforts so as to meet an a priori 'error bar' on the model outputs. The investigators' synergy, and their long-standing industrial collaborations, will ensure that PREMIERE will result in a paradigm-shift in multiphase flow research worldwide. We will demonstrate our capabilities using exemplar challenges, of central importance to their respective sectors in close collaboration with our industrial and healthcare partners. Our PREMIERE framework will provide novel and more efficient manufacturing processes, reliable design tools for the oil-and-gas industry, which remove conservatism in design, improve safety management, and reduce emissions and carbon footprint. This framework will also provide enabling technology for the design, operation, and optimisation of the next-generation nuclear reactors, and associated reprocessing, as well as patient-specific therapies for diseases such as acute compartment syndrome.

Planned Impact

The PREdictive Modelling with QuantIfication of UncERtainty for MultiphasE Systems (PREMIERE) programme integrates challenges identified by EPSRC Prosperity Outcomes and the Industrial Strategy Challenge Fund in healthcare (Healthy Nation), energy (Resilient Nation), manufacturing and digital technologies (Resilient Nation, Productive Nation) as areas to drive economic growth. Our framework will combine analysis and assimilation of available experimental, plant, process, operation, or patient data, on near real-time scales with rapid multi-physics-based numerical simulations to provide operators, designers, diagnosticians, and surgeons with solutions to scenarios with a user-defined timescale and uncertainty/risk level. This framework can be deployed in the design, operation-management, and real-time decision-making in clinics, and in manufacturing, nuclear, and oil-and-gas plants. Examples include the capability to predict, with quantified statistical uncertainties the correct thermal hydraulics for critical-heat-flux calculations in ABWR nuclear reactors (4 new AWBRs in the UK each costing £10B), crucial for their safety. Our framework will further support the development of intensified evaporation and extractive separation processes in the nuclear industry. Within healthcare, our framework will enable analysis of available patient data on an appropriate timescale to give a range of optimised treatment options to the practitioner/diagnostician/surgeon; with a known error/likelihood of success. It is important to note that the prediction of circulatory disorders within tissue after trauma due to, for instance, Acute Compartment Syndrome costs the NHS ~£40M p.a. for shin fractures alone, or due to coronary heart disease/atherosclerosis results in 73,000 UK deaths p.a. Within FMCG and fine chemicals/catalysis sectors, our work will lead to new industry practice and product innovation via design of novel processes for controlled, multiphase structured products while managing uncertainty in the plant and the supply-chain. This, in turn, will be enabled through the prediction of the interaction between multi-component fluids and the complex flow fields in processing equipment in FMCG manufacturing plants; FMCG grew to £184bn/year in 2016. Within the oil-and-gas sector (worth >£35B to the UK in 2015 alone), our framework will lead to validated, powerful predictive tools for flow regime transitions, crucial to the design and operation of receiving facilities in pipeline transportation systems, which can be employed reliably for flow assurance, minimising conservatism, overdesign, inefficiency, and carbon emissions. There will also be an increase in sustainability due to superior equipment and process design resulting in reduced pumping requirements for oil-gas transportation, reduced process scale in manufacturing, and reduced process downtime and associated wastage in FMCG. The PREMIERE programme is therefore particularly timely to enhance the UK's global standing in view of increasing competition internationally, and given our significant investments within Energy, Healthcare Technologies, and Manufacturing.

Publications

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Akyildiz Ö (2022) Statistical Finite Elements via Langevin Dynamics in SIAM/ASA Journal on Uncertainty Quantification

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Balla M (2021) Interaction of two non-coalescing bubbles rising in a non-isothermal self-rewetting fluid in European Journal of Mechanics - B/Fluids

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Buizza C (2022) Data Learning: Integrating Data Assimilation and Machine Learning in Journal of Computational Science

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Constante-Amores C (2020) Rico and the jets: Direct numerical simulations of turbulent liquid jets in Physical Review Fluids

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Constante-Amores C (2021) Role of surfactant-induced Marangoni stresses in drop-interface coalescence in Journal of Fluid Mechanics

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Constante-Amores C (2021) Direct numerical simulations of transient turbulent jets: vortex-interface interactions in Journal of Fluid Mechanics

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Constante-Amores CR (2023) Role of Kidney Stones in Renal Pelvis Flow. in Journal of biomechanical engineering

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Gaskin T (2023) Neural parameter calibration for large-scale multiagent models. in Proceedings of the National Academy of Sciences of the United States of America

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Gonçalves G (2020) Data-driven surrogate modeling and benchmarking for process equipment in Data-Centric Engineering

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Gonçalves G (2022) Mechanistic modelling of two-phase slug flows with deposition in Chemical Engineering Science