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Industry empowerment to Multiphase fluid dynamics simulations using Artificial intelligence and Statistical methods on modern hardware architectures at Scale

Project description

Developing novel solutions for multi-phase fluid dynamics simulations

Transitioning to greener energy solutions and improving energy production and efficiency requires the introduction and development of various technologies that utilise multi-phase, trans/supercritical, and non-Newtonian fluid flows with heat and mass transfer capabilities. However, direct numerical simulations cannot provide the necessary accuracy required for their development. The MSCA-funded SCALE project aims to develop novel simulation solutions and models that use machine learning, which are data-driven and physics-informed. The project will utilise specialised databases and LES and RANS simulations to assess and train the solutions. Furthermore, it will collaborate with industry experts to provide training and optimise the development and validation of these solutions.

Objective

Multi-phase, trans/supercritical and non-Newtonian fluid flows with heat and mass transfer are critical in enhancing the performance of energy production, propulsion and biomedical systems. Examples include: hydraulic turbomachines, ship propellers, CO2-neutral e-fuels and e-motor cooling systems, particle-laden flows in inhalers and focused ultrasounds for drug delivery. What all these cases have in common is the high level of complexity which makes Direct Numerical Simulations impossible. State-of-the-art LES simulations rely on simplified assumptions but do not have yet the desired accuracy, while often require enormously expensive CPU resources. The aim of SCALE is to develop simulation methods and reduced-order models using physics-informed and data-driven Machine Learning and optimisation methods for such flow processes. These will be trained against ‘ground-truth’ databases that will be generated for the first time using both DNS and experimentally validated, industry-relevant LES and multi-fidelity RANS simulations. The new simulation tools will be applied for the first time to industrial problems and their ability to accelerate design times and improve accuracy will be jointly pursued and evaluated with the non-academic partners of SCALE. These are international corporations and market leaders in the aforementioned areas. Holistic training by experts from science and industry includes broad reviews on relevant scientific topics, modern high performance computing architectures suitable for performing such simulations, big data analytics as well as extensive support for mastering scientific tasks and transferring the knowledge acquired to industrial practice. SCALE will also deliver soft skills training from a well-connected cohort of leaders with the ability to communicate across disciplines and within the general public. This coupling of research with industry makes SCALE a truly outstanding network for doctoral candidates to start their career.

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Programme(s)

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Topic(s)

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Funding Scheme

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HORIZON-TMA-MSCA-DN - HORIZON TMA MSCA Doctoral Networks

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Call for proposal

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(opens in new window) HORIZON-MSCA-2022-DN-01

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Coordinator

OTTO-VON-GUERICKE-UNIVERSITAET MAGDEBURG
Net EU contribution

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.

€ 521 078,40
Address
UNIVERSITAETSPLATZ 2
39106 Magdeburg
Germany

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Region
Sachsen-Anhalt Sachsen-Anhalt Magdeburg, Kreisfreie Stadt
Activity type
Higher or Secondary Education Establishments
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Total cost

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.

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Participants (5)

Partners (11)

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