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)
- Unilever (Collaboration)
- Syngenta International AG (Collaboration)
- GlaxoSmithKline (GSK) (Collaboration)
- NIHR Trauma Management HTC (Project Partner)
- UNIVERSITY HOSPITALS BIRMINGHAM NHS FOUNDATION TRUST (Project Partner)
- Wood (Project Partner)
- PETRONAS (Project Partner)
- McGill University (Project Partner)
- The Alan Turing Institute (Project Partner)
- Schlumberger (Project Partner)
- Procter & Gamble Limited (P&G UK) (Project Partner)
- Syngenta (Project Partner)
- Bangor University (Project Partner)
- BP (International) (Project Partner)
- Office for Nuclear Regulation (Project Partner)
Publications
Traverso T
(2023)
Data-driven modeling for drop size distributions
in Physical Review Fluids
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 S
(2022)
Data-driven surrogate model with latent data assimilation: Application to wildfire forecasting
in Journal of Computational Physics
Gonçalves G
(2020)
Data-driven surrogate modeling and benchmarking for process equipment
in Data-Centric Engineering
Gonçalves G
(2020)
Data-driven surrogate modelling and benchmarking for process equipment
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
Quilodrán-Casas C
(2021)
Digital twins based on bidirectional LSTM and GAN for modelling the COVID-19 pandemic
Quilodrán-Casas C
(2022)
Digital twins based on bidirectional LSTM and GAN for modelling the COVID-19 pandemic.
in Neurocomputing
Valdes J
(2023)
Direct numerical simulations of liquid-liquid dispersions in a SMX mixer under different inlet conditions
in Chemical Engineering Journal
Constante-Amores C
(2021)
Direct numerical simulations of transient turbulent jets: vortex-interface interactions
in Journal of Fluid Mechanics
Constante-Amores C
(2020)
Direct numerical simulations of transient turbulent jets: vortex-interface interactions
Constante-Amores C
(2023)
Direct numerical simulations of turbulent jets: vortex-interface-surfactant interactions
in Journal of Fluid Mechanics
Constante-Amores C
(2022)
Direct numerical simulations of turbulent jets: vortex-interface-surfactant interactions
Basha N
(2023)
Discovery of mixing characteristics for enhancing coiled reactor performance through a Bayesian optimisation-CFD approach
in Chemical Engineering Journal
Pico P
(2024)
Drop encapsulation and bubble bursting in surfactant-laden flows in capillary channels
in Physical Review Fluids
Panda D
(2024)
Drop Medusa: Direct numerical simulations of high-frequency Faraday waves on spherical drops
in Physical Review Fluids
Tuckus N
(2024)
Dynamic Neural Assimilation: a deep learning and data assimilation model for air quality predictions
in Discover Applied Sciences
Constante-Amores C
(2021)
Dynamics of a surfactant-laden bubble bursting through an interface
in Journal of Fluid Mechanics
Constante-Amores C
(2020)
Dynamics of a surfactant-laden bubble bursting through an interface
Constante-Amores C
(2020)
Dynamics of retracting surfactant-laden ligaments at intermediate Ohnesorge number
Constante-Amores C
(2020)
Dynamics of retracting surfactant-laden ligaments at intermediate Ohnesorge number
in Physical Review Fluids
Wang D
(2025)
Effect of electric field on droplet formation in a co-flow microchannel
in Physics of Fluids
Kovalchuk N
(2020)
Effect of moderate DC electric field on formation of surfactant-laden drops
in Chemical Engineering Research and Design
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
Batchvarov A
(2020)
Effect of surfactant on elongated bubbles in capillary tubes at high Reynolds number
in Physical Review Fluids
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)
Effect of surfactants on drop formation flow patterns in a flow-focusing microchannel
in Chemical Engineering Science
Cheng S
(2024)
Efficient deep data assimilation with sparse observations and time-varying sensors
in Journal of Computational Physics
Kadeethum T
(2022)
Enhancing high-fidelity nonlinear solver with reduced order model.
in Scientific reports
Gelado S
(2023)
Enhancing Microdroplet Image Analysis with Deep Learning
in Micromachines
Cheng M
(2023)
Ensemble Kalman filter for GAN-ConvLSTM based long lead-time forecasting
in Journal of Computational Science
Zhuang Y
(2022)
Ensemble latent assimilation with deep learning surrogate model: application to drop interaction in a microfluidics device
in Lab on a Chip
Hu J
(2024)
Explainable AI models for predicting drop coalescence in microfluidics device
in Chemical Engineering Journal
Quilodrán-Casas C
(2024)
Exploring unseen 3D scenarios of physics variables using machine learning-based synthetic data: An application to wave energy converters
in Environmental Modelling & Software
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
Vadeboncoeur A
(2023)
Fully probabilistic deep models for forward and inverse problems in parametric PDEs
in Journal of Computational Physics
Vadeboncoeur A
(2022)
Fully probabilistic deep models for forward and inverse problems in parametric PDEs
Cheng S
(2022)
Generalised Latent Assimilation in Heterogeneous Reduced Spaces with Machine Learning Surrogate Models
in Journal of Scientific Computing
Constante-Amores C
(2022)
Impact of droplets onto surfactant-laden thin liquid films
| 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 |