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Next-Generation Accelerated and Scalable Modelling Frameworks for Chemically Reactive Flows

Objetivo

Accelerating Europe’s green and digital transitions requires advanced tools capable of modelling and optimising the complex reactive flows that underpin clean energy systems, propulsion technologies, and fire-safety applications. Yet current simulation approaches remain too computationally demanding to capture the multi-physics behaviour of low- and zero-carbon fuels such as hydrogen, ammonia, and e-fuels, which are essential for decarbonising hard-to-electrify sectors. ASCEND addresses this challenge by delivering the next-generation fully integrated modelling framework that leverages artificial intelligence (AI) and high-performance computing (HPC) technologies to accelerate reactive Computational Fluid Dynamics (CFD) simulations. By enabling order-of-magnitude reductions in computational cost while preserving physical fidelity, ASCEND will set a new European benchmark for predictive, fuel-flexible simulation tools.

ASCEND will train 12 Doctoral Candidates to become a new generation of experts at the interface of combustion science, scientific machine learning (SciML), and high-performance computing. Bringing together six leading European universities and research centres, supported by a diverse group of industrial and academic associated partners, the programme offers interdisciplinary training in physics-informed ML, uncertainty quantification, GPU-aware numerical methods, and computational fluid dynamics. This will equip researchers with the cutting-edge skills needed to design fast, trustworthy, and scalable digital tools.

By developing physics-informed, hardware-accelerated ML frameworks that drastically reduce computational cost while maintaining high physical fidelity, ASCEND will strengthen Europe’s scientific leadership, accelerate innovation in clean energy technologies, and contribute to a highly skilled workforce vital for achieving EU climate, digital, and sustainability objectives.

Ámbito científico (EuroSciVoc)

Palabras clave

Programa(s)

Tema(s)

Régimen de financiación

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

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Convocatoria de propuestas

(se abrirá en una nueva ventana) HORIZON-MSCA-2025-DN-01

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Coordinador

QUEEN MARY UNIVERSITY OF LONDON
Aportación neta de la UEn
€ 697 476,24
Coste total
Sin datos

Participantes (5)

Socios (9)