The GREYDIENT innovative training network aims at training a next generation of Early Stage Researchers (ESR) to fully sustain the ongoing transition of European personal mobility towards safe and reliable intelligent mobility systems via the recently introduced framework of grey-box modelling approaches. One of the main challenges that we currently face in this context is the integration of the data captured from the plenitude of sensors that are involved in a particular road-traffic scenario, ranging from monitoring car-component loading situations to power network-reliability estimations. The aim is to fully exploit the potential of merging these data with advanced computational models of components and systems that are widely available in industry in order to fully assess the momentarily safety. Grey box models are an answer to this pressing issue, as they are aimed at optimally integrating (black-box) data driven machine learning tools with (white-box) simulation models to greatly surpass the performance of either framework separately. However, the training of professional profiles in Europe who combine knowledge and experience in state-of-the-art data-driven black box and numerical white box approaches with expertise in methods for reliability and safety estimation is scarce. Therefore, GREYDIENT will train its ESR’s in a wide spectrum of fields, including the modelling, propagation and quantification of the relevant variabilities, the application of big data and machine learning methods, as well as the optimal combination of data-driven approaches with numerical models. All our ESR’s will obtain a PhD from an internationally respected University, build experience in communicating and disseminating their work, applying their research skills in a non-academic context and receive in-depth training in transferable skills such commercialization, collaboration and entrepreneurship. This training will be organized in close cooperation with key industry stakeholders.

Rail is a fundamental service for modern societies and the backbone of a sustainable transport system. To meet the numerous challenges ahead, the global rail sector must increasingly rely on the emerging disruptive technologies such as advanced robotics, 3-D printing, high computing power and connectivity, etc. which are integrated with analytical and cognitive technologies that enable machine-to-machine and machine-to-human communication.On top comes the pressure to reduce energy consumption, pollution and the consumption of other resources. Mastering the breakthrough developments of new technologies is of capital importance for the railway industry to deliver smart and efficient solutions.Indeed, essential to the growth of the rail industry is the reduction of the overall life cycle exploitation costs of all rail sub-systems. The Traction Drive sub-system is one of the main sub-systems of a train as it moves the train converting energy from an electrical source (directly or via a chemical source) into a mechanical one. RECET4Rail will focus on the following new technologies for the Traction Drive sub-system: development of design approaches, end-to-end conception time evaluation and feasibility/performance study of 3D printing technologies for new traction’s components use cases; Dynamic Wireless Power Transfer system sizing for actual city profiles focused on opportunistic charging; improving the understanding of the robustness and reliability of high voltage SiC modules; and development of smart maintenance approaches enabled by predictive analytics, trained on big data. RECET4Rail will provide essential knowledge that will lead to future improvement of the high TRL level S2R traction demonstrations on trains done by the S2R Members, preparing also future S2R key work on domains like digitalisation applied to Traction, environmental sustainability (especially devising carbon free traction systems) or reinforcement of standardisation to lower complexity and costs

The deployment of smart grids is crucial for achieving a more secure and sustainable energy future. In fact, beside addressing current concerns with existing electricity systems (e.g., ageing infrastructure and increasing peak demand), smart grids are fundamental for the diffusion of low-carbon technologies, including electric vehicles and variable Renewable Energy Sources. Significant revenue losses are still experienced in the existing power networks, but the convergent advances in computing and ICT, the accelerating deployment of renewable generation and storage technologies offer new opportunities for advanced Operations and Maintenance Management (O&MM) to ensure more reliable, efficient and resilient power electric grids meeting environmental performance goals. Then, ARAMIS proposes the present Algo-Grid project, for deploying a platform for O&MM of power grids. ARAMIS was founded in 2012 in Milan, Italy, by a group of (PhD) researchers of the Politecnico di Milano. The mission of ARAMIS is to develop and deploy methods, models, techniques and algorithms for the analysis and optimization of industrial components and systems, particularly, in terms of safety, reliability, maintenance, resilience, asset management, etc. Algo-Grid is based on specifically designed Prognostics and Health Management (PHM) solutions, time series prediction algorithms, energy flow simulators and optimization algorithms, which can provide real-time optimal configurations of smart grids (generation, transmission and distribution), predict and assess infrastructure health condition, incorporate policy-based rule logic. A preliminary estimation yields a Return On Investement (ROI) of 4,63M€ for the years 2020-2024. Cumulative profits in 2024 deriving from direct sales in EU28 and target markets (including India, China and the USA) will total more than 9,03M€, exploiting a 25,78B€ business opportunity. Concurrently, ARAMIS will create 28 new jobs directly linked to Algo-Grid