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

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Project objective

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.

Original text from CORDIS.

Participants

• QUEEN MARY UNIVERSITY OF LONDON · LONDONCoordinatorUnited Kingdom
• BARCELONA SUPERCOMPUTING CENTER CENTRO NACIONAL DE SUPERCOMPUTACION · BARCELONASpain
• CENTRE NATIONAL DE LA RECHERCHE SCIENTIFIQUE CNRS · ParisFrance
• EIDGENOESSISCHE TECHNISCHE HOCHSCHULE ZUERICH · ZuerichSwitzerland
• ETEX · ZaventemCity levelBelgium
• FORSCHUNGSZENTRUM JULICH GMBH · JULICHGermany
• NORTH CAROLINA STATE UNIVERSITY · RaleighUnited States
• SHELL RESEARCH LIMITED · LondonUnited Kingdom
• SIEMENS ENERGY INDUSTRIAL TURBOMACHINERY LIMITED · LINCOLNUnited Kingdom
• TECHNISCHE UNIVERSITAET BRAUNSCHWEIG · BraunschweigGermany
• TECHNISCHE UNIVERSITAT DARMSTADT · DarmstadtGermany
• THE UNIVERSITY OF EDINBURGH · EdinburghUnited Kingdom
• UNIVERSITAT POLITECNICA DE CATALUNYA · BARCELONASpain
• UNIVERSITEIT GENT · GentBelgium
• Virtwin-Energy AB · StockholmCity levelSweden

Links

• View on CORDIS
• DOI: 10.3030/101312072