Flight testing is an important phase during the development of an aircraft to validate the design. During flight, data is gathered and design problems are identified and solved. The collected data are fundamental for the analysis and Aircraft are properly instrumented to generate large amounts of information. Such huge amount of data needs to be properly evaluated and traditional methods and platforms are no more effective. Flight testing is a significant cost contributor to the aircraft production life cycle and is still extensively deployed. Flight test programmes take several years and more prototypes are built to reduce lead times. Strong adherence to rigour safety and certification requirements and generally unchanged circular advisories inhibit the potential improvement of flight test designs. Innovative algorithms and statistical estimation are not achieving its full potential in the industrialized flight testing environment. The methods in this proposal increase the quality and productivity of an experiment, leading to a required test point reduction or increased predictive capabilities. The purpose of this project is to define and implement a state-of-the-art platform able to support data analysis. This is achieved by adopting a complex hardware architecture to support big data analysis and implementing specific algorithms to support data correlation, time series management and statistical analysis. Furthermore, to support flight test engineers, novel approaches based on machine learning are provided to support the technicians in detecting specific flight conditions. The same platform is also adapted to support the development of the Next Generation Civil Tilt Rotor Technology Demonstrator.

Real-Time Simulations (RTS) are widely recognised as a means to support the validation process of systems and procedures up to the highest operational readiness levels and are therefore widely used to support V3 validation campaigns in the SESAR context. With the development of new airspace users and new Air Traffic Management (ATM) concepts in recent years and those expected in the near future (e.g. U-Space, Advanced Air Mobility (AAM)), V&V processes have become increasingly complex, with increasing demands on the infrastructures for validating these concepts and operational conditions. A wider diffusion of interoperability between specialised simulators could support the need for improved ATM V&V infrastructures to demonstrate the achievement of validation objectives related to future European ATM concepts. The VISORS project aims at supporting a wide diffusion of interoperability standards among ATM validation platforms. An economic analysis of performing validation processes for ATM/AAM/U-space interoperability concepts and solutions through a multi-site validation architecture will be performed. An experimental demonstration test will be defined and performed to collect data for this analysis. The simulation facilities of different partners of the project will be connected through a prototype platform to develop state-of-the-art interoperability solutions. The security aspect related to data exchange between this platform will be assessed. Furthermore, the impact of this distribution of actors involved in validation activities on state-of-the-art HP assessment methodologies will be evaluated, also considering the possible remote execution of related measurements.

Railway crossings degrade significantly faster than plain line track, with lifespans commonly lasting less than 5 years despite their design lives typically being 15 years. This results in Billions of Euros being spent on unplanned corrective maintenance, crossing renewals and delay-minutes, which would not occur if they could be better managed to reach their design lives. XCROSS will develop a suite of integrated disruptive technologies which combine to provide a technological process for the monitoring, inspection, and maintenance intervention of crossing surface profiles. To achieve the objectives, new crossing inspection techniques will be developed using 3D laser scanning technology and computer vision to allow for fast, repeatable and high accuracy crossing measurement. These 3D scans will then be used to create digital twins (DT) of the crossing, embedded with advanced wheel-rail interaction calculation algorithms. Considering these wheel forces and also on-site practical maintenance constraints, the digital twin will use optimisation strategies to calculate the optimal geometry profile achievable via on-site welding and grinding techniques in terms of lifecycle cost. Then, to enable practical implementation of the calculated optimum crossing profile geometries, Augmented Reality (AR) and 3D printing techniques will be developed to provide visualisation guides for on-site welding and grinding. These technologies will allow the maintenance engineers to see their welding and grinding progress ‘live’, and thus be an enabler for a step-change in the quality control of crossing interventions. Lastly, an early warning system will also be developed to act as the initial trigger for the deployment of the 3D scanning, AR and 3D printing. It is vital for detecting the early signs of degradation and thus initiating the deployment of the new maintenance process before the onset of irreparable degradation.

MAASiveTraditional value chains are facing challenges due to the fast-moving markets, customer demands, and unpredictable manufacturing and logistics. To address these challenges, Manufacturing as a Service (MaaS) is introduced as a concept that utilizes existing resources in a value network by connecting manufacturers to service providers on demand through a connected network. The MAASive project aims to develop models of value networks that enable companies to recover from unforeseen external events by connecting to new services and reconfiguring value networks utilizing internal and external manufacturing services. MAASive will provide a toolkit for industry, which will consist of a blend of existing methods and technology applied in the MaaS context, and new models and technology developed as part of the project. Four distinct aspects are addressed in the MAASive project to increase resilience in value networks: network building, impact assessment, reorchestration of networks, and value network operation. The overall aim of MAASive is to increase value network resilience by enabling manufacturers to rapidly respond to unforeseen external events or sudden changes in supply or demand, utilizing manufacturing as a service. MAASive uses an iterative approach to develop technical solutions and identify potential technology risks early on. The project is focused on creating a toolkit from a human-centered perspective and involving professionals and workers in requirement and scenario definition. The iterative approach follows three loops focusing on 1) model foundations, 2) impact simulation and scenarios, and 3) network orchestration and operation. The results of MAASive will be developed in two use case demonstrators. The results of MAASive will contribute to companies being more resilient towards external, unforeseen events, by being able to utilize services in a value network better and faster, while also increasing utilization of network resources.

Car electronics is one of the most valuable source of Critical Raw Materials (CRMs) in cars. What it sounds so strange is the lack of interest of car manufacturers (and the whole automotive sector in general) towards the recovery of these valuable components from End-of-Life Vehicles (ELVs). Maybe, the complex set of barriers (e.g. regulatory, governance-based, market, technological, cultural, societal, gender, etc.) companies must cope with when implementing Circular Economy (CE) are making very difficult its adoption, by limiting potential benefits. All these data show as, even if car manufacturers are investing big capitals trying to shift their business towards more sustainable mobility concepts, the sectorial transition towards CE seems to be far from its completion. Especially at End-of-Life (EoL) phase, there are still many issues to be solved in order to functionally recover materials from cars (e.g. reuse recovered materials for the same purpose they were exploited originally) and the dependence from natural resources when producing new cars (even if electric/hybrid/fuel cell -powered) is still too high. This mandatory systemic transformation requires to all companies/sectors to redefine products lifecycles since the beginning, by considering CE already before to design them. To this aim, the TREASURE project wants to develop a scenario analysis simulation tool able to quantify positive and negative implications of CE, by leading the European automotive supply chain towards its full transition to CE.