AHEAD · Advanced techniques for quantification and modelling of phase-change processes of renewable fuels and their blends

Advanced techniques for quantification and modelling of phase-change processes of renewable fuels and their blends

Decarbonising the transport sector, responsible for approximately 25% of Green House Gas (GHG) emissions, is increasingly urgent in the race to address climate change. The European Union (EU) has already taken bold steps in this direction with the launch of the European Green Deal, in which achieving net zero GHG emissions by 2050 is the primary strategic objective. Notwithstanding this fact, electrification of internal combustion engine (ICE) vehicles seems to be a long-term process. Recent forecasts are suggesting that electric passenger cars will achieve a market penetration of almost 58% by 2040. Nevertheless, at least for the next three decades, heavy-duty vehicles will be primarily powered by diesel engines, as current state-of-the-art limitations, for instance battery capacity and thermal management, do not allow a similar technology shift when high power output for an extended period of time is required. A disappointing ~25% growth in liquid fossil fuel demand globally is foreseen, due to increased commercial activity, people mobility and product transportation Active research on clean combustion is necessitated by the stringent emissions legislation to be imposed in Europe and the US within the next decade. On a broader perspective, societal and environmental concerns on the use of fossil fuels and after-effects on climate change dictate the development of ICE with enhanced fuel efficiency and reduced emissions primarily operating with renewable fuels. The project demonstrated tangible outcomes in terms of novel diagnostics for multiphase flows, modelling approaches for complex thermodynamics and vaporising sprays, as well as fuel technology for greener internal-combustion engines. The research activities implemented in the course of the Fellowship have demonstrated that a systematic, quantitative characterisation of the fuel injection process, encompassing both the internal flow path of the injector orifice and the downstream spray region, is crucial for the understanding of combustion quality and eventually pollutants emissions. Novel imaging techniques based on x-ray and neutron irradiation have shown great potential for the two-phase flow quantification within real fuel-injector devices, a capability which, of course, is not possible with conventional optical imaging. Furthermore, the novel predictive method implemented, suitable for deriving the thermodynamic properties of fluids of complex composition, offers increased accuracy to the numerical simulations of vaporising fuel sprays. It must be emphasised that the method is not limited to multi-component fuels but can be extended to a wide range of fluids with relevance to industrial and biomedical applications. The numerical framework developed during the fellowship has been extended to non-deterministic modelling methodologies, namely Machine Learning algorithms, which have been proven more robust in the prediction of propagation of flashing sprays in comparison to physics-based models. More importantly the project has demonstrated the potential of synthetic and alternative fuels with respect to enhanced spray atomisation and mixing, eventually leading to reduced pollutants emissions.

Текст от CORDIS, на английски · Данни: CORDIS, © Европейски съюз