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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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Phillips T (2021) An autoencoder-based reduced-order model for eigenvalue problems with application to neutron diffusion in International Journal for Numerical Methods in Engineering

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Kovalchuk N (2021) Surfactant-mediated wetting and spreading: Recent advances and applications in Current Opinion in Colloid & Interface Science

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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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Moran H (2021) Inertial and buoyancy effects on the flow of elongated bubbles in horizontal channels in International Journal of Multiphase Flow

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Constante-Amores C (2022) Role of kidney stones in renal pelvis flow

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Kahouadji L (2022) A numerical investigation of three-dimensional falling liquid films in Environmental Fluid Mechanics

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

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

 
Description We have devised methodologies that combine AI approaches with physics-based methods to solve complex engineering problems with important energy and manufacturing applications.
Exploitation Route The outcomes of this funding will form part of a framework we are currently developing to ensure that companies across scales (from multi-nationals to SMEs) and sectors remain on the optimal pathway to the transition to Net Zero.
Sectors Agriculture

Food and Drink

Chemicals

Digital/Communication/Information Technologies (including Software)

Education

Energy

Manufacturing

including Industrial Biotechology

Pharmaceuticals and Medical Biotechnology

 
Description The findings from the PREMIERE research have been used to guide the manufacturing of foods in fast-moving consumer goods and sprays in agrichemicals. Our findings will also find applications in the pharma industry.
First Year Of Impact 2023
Sector Agriculture, Food and Drink,Energy,Pharmaceuticals and Medical Biotechnology
 
Description ANTENNA - Advanced tools for predictive cleaning in a world of resource scarcity
Amount £2,188,999 (GBP)
Funding ID EP/V056891/1 
Organisation Engineering and Physical Sciences Research Council (EPSRC) 
Sector Public
Country United Kingdom
Start 06/2021 
End 12/2025
 
Description BOiliNg flows in SmAll and mIcrochannels (BONSAI): From Fundamentals to Design
Amount £846,008 (GBP)
Funding ID EP/T03338X/1 
Organisation Engineering and Physical Sciences Research Council (EPSRC) 
Sector Public
Country United Kingdom
Start 12/2020 
End 09/2024
 
Description Extreme volumetric imaging using single-shot optical tomography with compressive sensing
Amount £201,158 (GBP)
Funding ID EP/V048996/1 
Organisation Engineering and Physical Sciences Research Council (EPSRC) 
Sector Public
Country United Kingdom
Start 04/2021 
End 10/2023
 
Description Hybrid Machine-Learning and Computational Fluid Dynamics Methods in the Energy Industry
Amount £28,600 (GBP)
Funding ID 2367735 
Organisation Engineering and Physical Sciences Research Council (EPSRC) 
Sector Public
Country United Kingdom
Start 09/2019 
End 10/2023
 
Description Inertial Fusion Energy: Optimising High Energy Density Physics in Complex Geometries
Amount £6,141,929 (GBP)
Funding ID EP/X025373/1 
Organisation Engineering and Physical Sciences Research Council (EPSRC) 
Sector Public
Country United Kingdom
Start 06/2023 
End 06/2028
 
Description Risk EvaLuatIon fAst iNtelligent Tool (RELIANT) for COVID19
Amount £1,356,505 (GBP)
Funding ID EP/V036777/1 
Organisation Engineering and Physical Sciences Research Council (EPSRC) 
Sector Public
Country United Kingdom
Start 09/2020 
End 10/2022
 
Description Spray cooling high power dissipation Applications (SANGRIA): From fundamentals to Design
Amount £508,475 (GBP)
Funding ID EP/X015351/1 
Organisation Engineering and Physical Sciences Research Council (EPSRC) 
Sector Public
Country United Kingdom
Start 01/2024 
End 12/2026
 
Title Drop count and size of 38 numerical simulation of a flat fan spray 
Description Data refer to the drop size distribution used in the paper "Data-driven modelling for drop size distributions" by T. Traverso, T. Abadie, O. K. Matar, and L. Magri (arXiv link: https://arxiv.org/abs/2305.18049) Each of the 38 .csv file in this folder is associated with a different working condition of the nozzle. Specifically, the name 'alpha##_Re##_We##.csv' contains the working condition of the nozzle as - alpha## (## is the spray angle) - Re## (## is the Reynolds number) - We## (## is the Weber number) Each file contains as many raws as the number of drops. In the i-th raw, 1) the first element is the Volume of the i-th drop; 2) the second element is the estimated surface of the i-th drop with the method in equation (18) of [1]; 3) the third element is the equivalent diameter of the i-th drop (i.e., as if it was spherical - computed from the volume) The value of the Weber number found in the Arxiv paper is half of that reported here. The correct one is the one in this database. The paper will be corrected in due time. 
Type Of Material Database/Collection of data 
Year Produced 2023 
Provided To Others? Yes  
URL https://zenodo.org/record/8370638
 
Title Drop count and size of 38 numerical simulation of a flat fan spray 
Description Data refer to the drop size distribution used in the paper "Data-driven modelling for drop size distributions" by T. Traverso, T. Abadie, O. K. Matar, and L. Magri (arXiv link: https://arxiv.org/abs/2305.18049) Each of the 38 .csv file in this folder is associated with a different working condition of the nozzle. Specifically, the name 'alpha##_Re##_We##.csv' contains the working condition of the nozzle as - alpha## (## is the spray angle) - Re## (## is the Reynolds number) - We## (## is the Weber number) Each file contains as many raws as the number of drops. In the i-th raw, 1) the first element is the Volume of the i-th drop; 2) the second element is the estimated surface of the i-th drop with the method in equation (18) of [1]; 3) the third element is the equivalent diameter of the i-th drop (i.e., as if it was spherical - computed from the volume) The value of the Weber number found in the Arxiv paper is half of that reported here. The correct one is the one in this database. The paper will be corrected in due time. 
Type Of Material Database/Collection of data 
Year Produced 2023 
Provided To Others? Yes  
URL https://zenodo.org/record/8370639
 
Description CFD simulations of complex interfacial flows in food production 
Organisation Unilever
Department Unilever Research and Development
Country United Kingdom 
Sector Private 
PI Contribution CFD simulations of interfacial flows with complex rheology, heat transfer, and phase change.
Collaborator Contribution They brought industrially-relevant challenges, experience and expertise (and cash).
Impact Technical reports for Unilever and transfer of codes.
Start Year 2023
 
Description Collaboration with Syngenta on spray formation through nozzles 
Organisation Syngenta International AG
Department Syngenta Ltd (Bracknell)
Country United Kingdom 
Sector Private 
PI Contribution We build a workflow for predicting the drop size distributions of sprays from information such as nozzle geometry, liquid properties, and flow rates. The workflow involves a combination of CFD and machine learning algorithms. We are now building on this to introduce shape optimisation to help come up with novel nozzle designs.
Collaborator Contribution Syngenta provided experts time, guidance, and nozzle designs, and industrial relevance. They also provided opportunities for well-defined research topics for which consultancy-type solutions were provided by us.
Impact The outputs involve software based on open-source codes that make predictions of the drop size distributions of sprays based on nozzle design, fluid properties and flow rate.
Start Year 2021
 
Description The application of hybrid methods in the pharma industry 
Organisation GlaxoSmithKline (GSK)
Department GSK Biologicals SA
Country Belgium 
Sector Private 
PI Contribution Machine learning combined with CFD to tackle microfluidics-type problems in pharma
Collaborator Contribution They brought specific examples of challenges and the industrial experience and expertise (in addition to cash).
Impact This is just underway.
Start Year 2025
 
Title DT_SegNet 
Description A comprehensive, two-tiered deep learning approach designed for precise object detection and segmentation in electron microscopy (EM) images. 
Type Of Technology Software 
Year Produced 2024 
Open Source License? Yes  
URL https://zenodo.org/doi/10.5281/zenodo.7510032
 
Title DT_SegNet 
Description A comprehensive, two-tiered deep learning approach designed for precise object detection and segmentation in electron microscopy (EM) images. 
Type Of Technology Software 
Year Produced 2024 
Open Source License? Yes  
URL https://zenodo.org/doi/10.5281/zenodo.10786449
 
Description Workshop at the International Conference on Multiphase Flows 2023 
Form Of Engagement Activity Participation in an activity, workshop or similar
Part Of Official Scheme? No
Geographic Reach International
Primary Audience Professional Practitioners
Results and Impact As shown on p. 97 of this document (http://www.jsmf.gr.jp/icmf2022/doc/program_final.pdf), we organised a special session on the use of machine learning and hybrid methods in multiphase flows at the most prestigious multiphase flows conference in the world, which is held every three years. The session was attended by >100 people and generated significant impact among the academic and industrial communities.
Year(s) Of Engagement Activity 2023