SOURCE-LINKED INTELLIGENCE

ENabling sustainable COmbustion technologies using hybrid physics-based Data-driven modelING

CORDIS · observation · Publication date unknown

programme. The 10 doctoral candidates will gain multidisciplinary know-how in sustainable fuels, experimental techniques and numerical simulations of turbulent reacting flows, big data analytics and machine learning, and intersectoral experience (academic and industrial relevant training). Together, they will be able to create knowledge to develop a generalised hybrid ML-based digital infrastructure, with the capability to solve current and future outstanding questions to decarbonise EIIs. The unique training is only possible thanks to the participation of renowned academic institutions with partners specialized in combustion experiments and simulation (ULB, RWTH, CNRS, CNR), data analysis and dimensionality reduction (UPM, ULB), data-driven and ML-based modelling (ULB, RWTH, CNRS) and different companies in the whole chain of knowledge: sustainable fuels (Air Liquid), combustion systems (MITIS, NPT), fuel flexible burners (WS, TENOVA), pollutant remediation strategies (AGC, AMMR) and CFD software (CONVERGE, CFD Direct). Sensing, Data-driven modelling, Machine learning, Novel combus

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recordType

award

status

SIGNED

region

EU

value

2645172

unit

EUR