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.
Organisations
- Imperial College London (Lead Research Organisation)
- NIHR Trauma Management MedTech Co-operative (Project Partner)
- University Hospitals Birmingham NHS Foundation Trust (Project Partner)
- Wood (Project Partner)
- Petronas (Malaysia) (Project Partner)
- McGill University (Project Partner)
- The Alan Turing Institute (Project Partner)
- Schlumberger (United States) (Project Partner)
- Procter & Gamble (United States) (Project Partner)
- Syngenta (United Kingdom) (Project Partner)
- Office of Naval Research (Project Partner)
- Bangor University (Project Partner)
- BP Global (Project Partner)
Publications
Panda D
(2023)
Axisymmetric and azimuthal waves on a vibrated sessile drop
in Physical Review Fluids
PAUL S
(2021)
Analysis and control of vapor bubble growth inside solid-state nanopores
in Journal of Thermal Science and Technology
Paul S
(2020)
Single-bubble dynamics in nanopores: Transition between homogeneous and heterogeneous nucleation
in Physical Review Research
Phillips T
(2023)
Solving the discretised neutron diffusion equations using neural networks
in International Journal for Numerical Methods in Engineering
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
Phillips T
(2021)
Reduced-Order Modelling with Domain Decomposition Applied to Multi-Group Neutron Transport
in Energies
Phillips T
(2022)
Multi-Output Regression with Generative Adversarial Networks (MOR-GANs)
in Applied Sciences
Pico P
(2023)
Silver nanoparticles synthesis in microfluidic and well-mixed reactors: A combined experimental and PBM-CFD study
in Chemical Engineering Journal
Povala J
(2020)
Burglary in London: Insights from Statistical Heterogeneous Spatial Point Processes
in Journal of the Royal Statistical Society Series C: Applied Statistics
Povala J
(2022)
Variational Bayesian approximation of inverse problems using sparse precision matrices
in Computer Methods in Applied Mechanics and Engineering
Quilodrán-Casas C
(2022)
Digital twins based on bidirectional LSTM and GAN for modelling the COVID-19 pandemic.
in Neurocomputing
Quilodrán-Casas C
(2021)
Digital twins based on bidirectional LSTM and GAN for modelling the COVID-19 pandemic
Quilodrán-Casas C
(2023)
A data-driven adversarial machine learning for 3D surrogates of unstructured computational fluid dynamic simulations
in Physica A: Statistical Mechanics and its Applications
Ranasinghe G
(2019)
A Methodology for Prognostics Under the Conditions of Limited Failure Data Availability
in IEEE Access
Rappel H
(2022)
Intercorrelated random fields with bounds and the Bayesian identification of their parameters: Application to linear elastic struts and fibers
in International Journal for Numerical Methods in Engineering
Savage T
(2023)
Multi-fidelity data-driven design and analysis of reactor and tube simulations
in Computers & Chemical Engineering
Silva V
(2021)
Machine learning acceleration for nonlinear solvers applied to multiphase porous media flow
in Computer Methods in Applied Mechanics and Engineering
Silva VLS
(2023)
Data Assimilation Predictive GAN (DA-PredGAN) Applied to a Spatio-Temporal Compartmental Model in Epidemiology.
in Journal of scientific computing
South L
(2020)
Semi-Exact Control Functionals From Sard's Method
South L
(2022)
Semi-exact control functionals from Sard's method
in Biometrika
Titus Z
(2021)
Conditioning surface-based geological models to well data using artificial neural networks
in Computational Geosciences
Traverso T
(2023)
Data-driven modeling for drop size distributions
in Physical Review Fluids
Vadeboncoeur A
(2023)
Fully probabilistic deep models for forward and inverse problems in parametric PDEs
in Journal of Computational Physics
Valdes J
(2023)
Direct numerical simulations of liquid-liquid dispersions in a SMX mixer under different inlet conditions
in Chemical Engineering Journal
Valdés J
(2022)
Current advances in liquid-liquid mixing in static mixers: A review
in Chemical Engineering Research and Design
Van Rooij S
(2021)
Numerical optimization of evaporative cooling in artificial gas diffusion layers
in Applied Thermal Engineering
Vargas F
(2022)
Bayesian learning via neural Schrödinger-Föllmer flows
in Statistics and Computing
Wang H
(2022)
Intensified solvent extraction of L-tryptophan in small channels using D2EHPA
in Chemical Engineering and Processing - Process Intensification
Williams A
(2020)
Spreading and retraction dynamics of sessile evaporating droplets comprising volatile binary mixtures
in Journal of Fluid Mechanics
Wu X
(2023)
Modeling for understanding of coronavirus disease-2019 (COVID-19) spread and design of an isolation room in a hospital
in Physics of Fluids
Wynne G.
(2021)
Convergence guarantees for gaussian process means with misspecified likelihoods and smoothness
in Journal of Machine Learning Research
Zhong C
(2023)
Reduced-order digital twin and latent data assimilation for global wildfire prediction
in Natural Hazards and Earth System Sciences
Zhu K
(2023)
Analyzing drop coalescence in microfluidic devices with a deep learning generative model.
in Physical chemistry chemical physics : PCCP
Zhuang Y
(2022)
Ensemble latent assimilation with deep learning surrogate model: application to drop interaction in a microfluidics device
in Lab on a Chip