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

Cel

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.

Dziedzina nauki (EuroSciVoc)

Słowa kluczowe

Program(-y)

Temat(-y)

System finansowania

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

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Zaproszenie do składania wniosków

(odnośnik otworzy się w nowym oknie) HORIZON-MSCA-2025-DN-01

Wyświetl wszystkie projekty finansowane w ramach tego zaproszenia

Koordynator

QUEEN MARY UNIVERSITY OF LONDON
Wkład UE netto
€ 697 476,24
Koszt całkowity
Brak danych

Uczestnicy (5)

Partnerzy (9)