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)
- McGill University (Project Partner)
- Petronas (Malaysia) (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
Abubakar H
(2022)
Linear stability analysis of Taylor bubble motion in downward flowing liquids in vertical tubes
in Journal of Fluid Mechanics
Akyildiz Ö
(2022)
Statistical Finite Elements via Langevin Dynamics
in SIAM/ASA Journal on Uncertainty Quantification
Balla M
(2021)
Interaction of two non-coalescing bubbles rising in a non-isothermal self-rewetting fluid
in European Journal of Mechanics - B/Fluids
Basha N
(2023)
Discovery of mixing characteristics for enhancing coiled reactor performance through a Bayesian optimisation-CFD approach
in Chemical Engineering Journal
Batchvarov A
(2020)
Effect of surfactant on elongated bubbles in capillary tubes at high Reynolds number
in Physical Review Fluids
Botsas T
(2022)
Multiphase flow applications of nonintrusive reduced-order models with Gaussian process emulation
in Data-Centric Engineering
Buizza C
(2022)
Data Learning: Integrating Data Assimilation and Machine Learning
in Journal of Computational Science
Chagot L
(2022)
Surfactant-laden droplet size prediction in a flow-focusing microchannel: a data-driven approach.
in Lab on a chip
Chanona E
(2021)
Real-time optimization meets Bayesian optimization and derivative-free optimization: A tale of modifier adaptation
in Computers & Chemical Engineering
Chen J
(2023)
Computational fluid dynamics simulations of phase separation in dispersed oil-water pipe flows
in Chemical Engineering Science
Cheng M
(2020)
Data-driven modelling of nonlinear spatio-temporal fluid flows using a deep convolutional generative adversarial network
in Computer Methods in Applied Mechanics and Engineering
Cheng M
(2021)
A real-time flow forecasting with deep convolutional generative adversarial network: Application to flooding event in Denmark
in Physics of Fluids
Cheng M
(2022)
Spatio-Temporal Hourly and Daily Ozone Forecasting in China Using a Hybrid Machine Learning Model: Autoencoder and Generative Adversarial Networks
in Journal of Advances in Modeling Earth Systems
Cheng S
(2022)
Generalised Latent Assimilation in Heterogeneous Reduced Spaces with Machine Learning Surrogate Models
in Journal of Scientific Computing
Constante-Amores C
(2020)
Rico and the jets: Direct numerical simulations of turbulent liquid jets
in Physical Review Fluids
Constante-Amores C
(2021)
Role of surfactant-induced Marangoni stresses in drop-interface coalescence
in Journal of Fluid Mechanics
Constante-Amores C
(2023)
Direct numerical simulations of turbulent jets: vortex-interface-surfactant interactions
in Journal of Fluid Mechanics
Constante-Amores C
(2021)
Direct numerical simulations of transient turbulent jets: vortex-interface interactions
in Journal of Fluid Mechanics
Constante-Amores C
(2020)
Dynamics of retracting surfactant-laden ligaments at intermediate Ohnesorge number
in Physical Review Fluids
Constante-Amores CR
(2023)
Role of Kidney Stones in Renal Pelvis Flow.
in Journal of biomechanical engineering
Farooq U
(2020)
Numerical simulations of a falling film on the inner surface of a rotating cylinder.
in Physical review. E
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
Gonçalves G
(2020)
Data-driven surrogate modeling and benchmarking for process equipment
in Data-Centric Engineering
Gonçalves G
(2022)
Mechanistic modelling of two-phase slug flows with deposition
in Chemical Engineering Science
Heaney C
(2022)
Extending the Capabilities of Data-Driven Reduced-Order Models to Make Predictions for Unseen Scenarios: Applied to Flow Around Buildings
in Frontiers in Physics
Heaney C
(2022)
An AI-based non-intrusive reduced-order model for extended domains applied to multiphase flow in pipes
in Physics of Fluids
Hossein F
(2023)
Application of ultrasound techniques to liquid-liquid dispersed flows
in International Journal of Multiphase Flow
Hossein F
(2021)
Application of acoustic techniques to fluid-particle systems - A review
in Chemical Engineering Research and Design
Hue S
(2022)
Viscoelastic effects of immiscible liquid-liquid displacement in microchannels with bends
in Physics of Fluids
Inguva P
(2020)
Numerical simulation, clustering, and prediction of multicomponent polymer precipitation
in Data-Centric Engineering
Inguva PK
(2021)
Continuum-scale modelling of polymer blends using the Cahn-Hilliard equation: transport and thermodynamics.
in Soft matter
Kahouadji L
(2022)
A numerical investigation of three-dimensional falling liquid films
in Environmental Fluid Mechanics
Kalli M
(2023)
Effect of surfactants during drop formation in a microfluidic channel: a combined experimental and computational fluid dynamics approach
in Journal of Fluid Mechanics
Kalli M
(2022)
Comparison of surfactant mass transfer with drop formation times from dynamic interfacial tension measurements in microchannels.
in Journal of colloid and interface science
Kiratzis I
(2022)
Effect of surfactant addition and viscosity of the continuous phase on flow fields and kinetics of drop formation in a flow-focusing microfluidic device
in Chemical Engineering Science
Kovalchuk N
(2021)
Effect of Surfactant Dynamics on Flow Patterns Inside Drops Moving in Rectangular Microfluidic Channels
in Colloids and Interfaces
Kovalchuk N
(2023)
Review of the role of surfactant dynamics in drop microfluidics
in Advances in Colloid and Interface Science
Kovalchuk N
(2021)
Surfactant-mediated wetting and spreading: Recent advances and applications
in Current Opinion in Colloid & Interface Science
Maulik R
(2021)
Latent-space time evolution of non-intrusive reduced-order models using Gaussian process emulation
in Physica D: Nonlinear Phenomena
Moran H
(2021)
Inertial and buoyancy effects on the flow of elongated bubbles in horizontal channels
in International Journal of Multiphase Flow
Nathanael K
(2023)
Development of a predictive response surface model for size of silver nanoparticles synthesized in a T-junction microfluidic device
in Chemical Engineering Science
Nazareth R
(2020)
Stability of slowly evaporating thin liquid films of binary mixtures
in Physical Review Fluids
Obeysekara A
(2021)
Prediction of multiphase flows with sharp interfaces using anisotropic mesh optimisation
in Advances in Engineering Software
Pan I
(2022)
Data-centric Engineering: integrating simulation, machine learning and statistics. Challenges and opportunities
in Chemical Engineering Science
Panda D
(2023)
Axisymmetric and azimuthal waves on a vibrated sessile drop
in Physical Review Fluids
Paul S
(2020)
Single-bubble dynamics in nanopores: Transition between homogeneous and heterogeneous nucleation
in Physical Review Research
PAUL S
(2021)
Analysis and control of vapor bubble growth inside solid-state nanopores
in Journal of Thermal Science and Technology
Phillips T
(2022)
Multi-Output Regression with Generative Adversarial Networks (MOR-GANs)
in Applied Sciences
Phillips T
(2023)
Solving the discretised neutron diffusion equations using neural networks
in International Journal for Numerical Methods in Engineering