MYTHOS: Medium-Range Hybrid Low-Pollution FlexiFuel/Hydrogen Sustainable Engine

MYTHOS answers HORIZON-CL5-2022-D5-01-12 Call (i.e. Towards a silent and ultra-low local air pollution aircrafts) and proposes to develop a demonstrated innovative and disruptive design methodology for future short/medium range civil engines capable of using a wide range of liquid and gaseous fuels including SAFs thus aiming at fulfilling the objective of decarbonize civil aviation as foreseen by the ACARE SRIA Goals by 2050. To achieve these ambitious goals, the MYTHOS consortium develops and adopts a multidisciplinary multi-fidelity modelling approach for the characterization of the relevant engine components deploying the full power of the method of machine learning. The latter will lead through hidden-physics discovery to advance data-driven reduced models which will be embedded in a holistic tool for the prediction of the environmental footprint of the civil aviation of all speeds. The methodological concept of the MYTHOS project consists of six main steps. The first step (Step 1) is to define a realistic reference framework in terms of working conditions and design points in which the flexible-fuel engine technology based on bio-fuel and hydrogen have to work. Step 2 requires the definition of a model hierarchy for the engine critical components. In this phase, particular attention must be paid to the search for a good balance between the accuracy of the models and the computational effort required to ensure that both the constructive and structural aspects and the estimate of the polluting levels of the multi-fuel engine guarantee both acceptable computational costs and well-defined margins of uncertainty. In Step 3 the experimental validation of the of multi-fidelity simulations using SAF and H2 will take place. The new validated toolchain will reoptimize some selected points extracted from the Pareto front. The experimental campaign is also preparatory to Step 4, dedicated to using advanced machine learning and data mining techniques to build a data fusion process aimed at optimizing the critical parameters of the new models that characterize the forecast quality in the regimes of interest. Step 5 focuses on constructing advanced ROMs for the engine components and verifying these ROMs against both high-fidelity numerical and experimental results obtained in Step2 and Step 3. Finally, Step 6 encompasses the integration of engine components ROMs in a holistic framework that allows the performance assessment and feasibility analysis of flexifuel solutions for aeronautical propulsion.