Project description
Smarter fluid analysis based on machine learning
Multiphase flow involves the simultaneous movement of materials in different phases (liquids, gases or solids). It underpins critical processes in diverse industries, including energy and healthcare. However, its study is challenging as experiments are costly and hard to manage and computational methods are slow and resource-intensive. With the support of the Marie Skłodowska-Curie Actions programme, the MLTI-PHASE project will develop a new way to analyse multiphase flow using machine learning. Researchers will combine machine learning with fluid dynamics using images of how the flow behaves as well as mathematical equations. These inputs will enable the machine learning model to understand and predict flow behaviour, thereby significantly reducing analysis time and cost. This model will be developed and tested on benchmark problems before being applied to real-world systems.
Objective
Multiphase flow (MF) is the simultaneous flow of materials with two or more thermodynamic phases. MF occurs in numerous settings: bioengineering, conventional and nuclear power plants, oil and gas production and transport, pharmaceutical industry, combustion engines, chemical industry, flows inside the human body, biological industry, and process technology, to name a few. Researchers use experimental and theoretical techniques to study MF. Experimental techniques are usually restricted to smaller domains or laboratory scales due to very high costs; in addition, experiments in realistic conditions are very difficult to manage. On the other hand, theory usually requires numerical computation, which is very time-consuming for realistic MF problems.
The objective of this study is to develop a novel machine learning (ML)-based hidden fluid dynamics approach for MF. This innovative method combines ML and fluid dynamics by means of information accessible from photographs of flow visualizations and governing equations. The developed approach will considerably reduce the cost and time required to analyse MF systems. It will unlock many opportunities to improve the design and efficiency of existing and future MF systems, thus lowering design/operational costs.
This will be the first study to develop a ML-based model for MF. The novelty of the proposal is that it will open the opportunity to explore, via a relatively cheap and fast computation method, the vast number of design parameter variations normally needed for the optimisation of MF systems (e.g. efficiency, pressure drop, etc.). This contrasts with expensive and time-consuming experiments and time-inefficient parameter studies involving traditional computational fluid dynamics.
The proposal will be carried out in three stages:
1) To develop a ML-based model for MF.
2) To test the model with several benchmark MF problems.
3) To use the model to improve design and reduce cost in an actual practical environment.
Fields of science (EuroSciVoc)
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Keywords
Project’s keywords as indicated by the project coordinator. Not to be confused with the EuroSciVoc taxonomy (Fields of science)
Project’s keywords as indicated by the project coordinator. Not to be confused with the EuroSciVoc taxonomy (Fields of science)
Programme(s)
Multi-annual funding programmes that define the EU’s priorities for research and innovation.
Multi-annual funding programmes that define the EU’s priorities for research and innovation.
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HORIZON.1.2 - Marie Skłodowska-Curie Actions (MSCA)
MAIN PROGRAMME
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Topic(s)
Calls for proposals are divided into topics. A topic defines a specific subject or area for which applicants can submit proposals. The description of a topic comprises its specific scope and the expected impact of the funded project.
Calls for proposals are divided into topics. A topic defines a specific subject or area for which applicants can submit proposals. The description of a topic comprises its specific scope and the expected impact of the funded project.
Funding Scheme
Funding scheme (or “Type of Action”) inside a programme with common features. It specifies: the scope of what is funded; the reimbursement rate; specific evaluation criteria to qualify for funding; and the use of simplified forms of costs like lump sums.
Funding scheme (or “Type of Action”) inside a programme with common features. It specifies: the scope of what is funded; the reimbursement rate; specific evaluation criteria to qualify for funding; and the use of simplified forms of costs like lump sums.
HORIZON-TMA-MSCA-PF-EF - HORIZON TMA MSCA Postdoctoral Fellowships - European Fellowships
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Call for proposal
Procedure for inviting applicants to submit project proposals, with the aim of receiving EU funding.
Procedure for inviting applicants to submit project proposals, with the aim of receiving EU funding.
(opens in new window) HORIZON-MSCA-2022-PF-01
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Net EU financial contribution. The sum of money that the participant receives, deducted by the EU contribution to its linked third party. It considers the distribution of the EU financial contribution between direct beneficiaries of the project and other types of participants, like third-party participants.
- Limerick
Ireland
The total costs incurred by this organisation to participate in the project, including direct and indirect costs. This amount is a subset of the overall project budget.