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

Descripción del proyecto

Desarrollo de soluciones novedosas para simulaciones de dinámica de fluidos multifásicos

La transición hacia soluciones energéticas más ecológicas y la mejora de la producción y la eficiencia energéticas requieren la introducción y el desarrollo de diversas tecnologías que utilicen flujos de fluidos multifásicos, trans/supercríticos y no newtonianos con capacidad de transferencia de calor y masa. Sin embargo, las simulaciones numéricas directas no pueden proporcionar la precisión necesaria para su desarrollo. El equipo del proyecto SCALE, financiado por las acciones Marie Skłodowska-Curie, tiene como objetivo desarrollar soluciones y modelos de simulación novedosos que utilicen el aprendizaje automático, se basen en datos y se fundamenten en la física. El equipo del proyecto utilizará bases de datos especializadas y simulaciones LES y RANS para evaluar y entrenar las soluciones. Además, colaborará con expertos del sector para impartir formación y optimizar el desarrollo y la validación de estas soluciones.

Objetivo

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.

Coordinador

OTTO-VON-GUERICKE-UNIVERSITAET MAGDEBURG
Aportación neta de la UEn
€ 521 078,40
Dirección
UNIVERSITAETSPLATZ 2
39106 Magdeburg
Alemania

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Región
Sachsen-Anhalt Sachsen-Anhalt Magdeburg, Kreisfreie Stadt
Tipo de actividad
Higher or Secondary Education Establishments
Enlaces
Coste total
Sin datos

Participantes (5)

Socios (11)