The objective of this proposal is to overcome the conventional experimental set-up commonly used in structural testing as the proposed for measuring the torsional and bending stiffness of the HLFC Leading Edge configurations by experimental testing. Conventional instrumentation for measuring displacement, like LVDT contact sensing elements; or for stress measuring, like strain gauges or punctual data obtained from fiber Bragg grating, will be replaced for several newfangled solutions in order to develop a new and an innovative monitoring system for the qualitative and quantitative assessment of stress-strain events during structural testing such as overloads, defects appearance or even defect growth. For uniform load application, improved innovative technology will include, a combination of direct uniform load application, with an emerging technology probed successfully for others applications, capable to apply uniform loads for complex tunable elastic strains. This issue will allow control the deformation process as decided for torsion bending of wanted strain case. For the innovative monitoring system, classical punctual and contact strain measurement, will be replaced by a combination of novel SHM (Structural Health Monitoring) sensors in order to qualitative and quantitative assessment of stress-strain during structural testing. Four emerging technologies will be previously proved in laboratory for ensuring a properly performance during the test. These technologies will include elements for the first damage detection; the identification and quantification of stress-strain events, overloads and hot-spot point with non-contact measuring elements; the quick overall deformation measurement for FEM correlations; and the continuous strain measurements for internal areas or areas difficult to be instrumented with other techniques. All of these with the objective of ensuring an efficient, high quality testing that reduces the product development time risk and cost.

For the purpose of creating digitalisation and automation solutions Arrowhead Tools adresses engineering methodologies and suitable integrated tool chains. With the global aim of substantial reduction of the engineering costs for digitalisation/automation solutions. Thus the Arrowhead Tools vision is: - Engineering processes and tool chains for cost efficient developments of digitalization, connectivity and automation systems solutions in various fields of application For the further and wider commercialisation of automation and digitalisation services and products based on SOA, Arrowhead Framework and similar technologies there is a clear need for engineerings tools that integrates existing automation and digitalisation engineering procedures and tool with SOA based automation/digitalisation technology. For this purpose the Arrowhead Tool’s grand challenges are defined as: - Engineering costs reduction by 40-60% for a wide range of automation/digitalisation solutions. - Tools chains for digitalisation and automation engineering and management, adapted to: 1. existing automation and digitalisation engineering methodologies and tools 2. new IoT and SoS automation and digitalisation engineering and management tools 3. security management tools - Training material and kits for professional engineers The results will create impact on: - Automation and digitalisation solution market - Automation and engineering efficiency and the SSBS market - Automation and digitalisation security - Competence development on engineering of automation and digitalisation solution

ICONIC aims to develop innovative physical and digital tools to achieve fundamental breakthroughs for the integrated control of wind farms, considering the whole physical system at farm, turbine, and component levels, in particular the complex aerodynamic interactions among turbines. ICONIC aims to increase farm-wide power production by 15-20% under optimal wind speeds and directions for typical wind farms suffering from wake effects, with a 3%-5% increase in annual energy production (AEP) considering all working conditions over the long term. It targets an LCOE reduction of at least 6% compared with the state-of-the-art control tools deployed in the current wind industry by improving farm-wide AEP and reducing operation & maintenance costs via leveraging the latest AI and digital technologies. Extensive validations for the integrated wind farm control solutions will be conducted via high-fidelity simulation models, experiments at a national-level wind tunnel, historical operational data at BP’s and C-Power’s wind farms, a unique collection of test rigs for critical turbine components at respective companies, and real-world wind farm field tests at C-Power. ICONIC’s integrated wind farm control system will contain (1) novel AI-based wind farm control system to unlock wind farms’ full potential; (2) novel data-enhanced wind turbine controllers to fulfil farm-level commands while balancing power generation and load mitigation; (3) an integration with digital twins (DTs) as extra support to improve control and reduce costs, which contains a first-ever farm-level DT for wind farm flow systems replicating detailed physical flow fields and an innovative turbine-level DT with critical component models for loading and lifetime estimations; (4) extensions of the solutions to future 20MW turbines. ICONIC will establish new knowledge and industrial leadership in key digital, enabling and emerging technologies, and deliver next-generation tools for wind farm operation.

The aim of ACHILES is to develop a more efficient E/E control system architecture optimized for the 3rd generation of EVs by integrating four new technological concepts. Firstly, a new wheel concept design will be equipped with full by-wire braking, including a new friction brake concept. Secondly, a centralized computer platform will host the e-drive functionalities and reduce the number of ECUs and networks while fulfilling safety & security requirements. It will support centralized domain controllers required to implement high automation and autonomy concepts, a key requirement for smart mobility. Thirdly, an out of phase control that will allow to intentionally operate the electric motor inefficiently to dissipate the excess of braking energy in case of fully charged batteries. As a fourth concept, a new torque vectoring algorithm will significantly improve the vehicle dynamics. The advances proposed will reduce the total cost of ownership by 10% and increase the driving range by at least 11% while increasing autonomy. ACHILES will be tested and verified in a real demo vehicle and in a brand-independent testing platform. The project consortium is another major asset. Audi, one of the technologically most advanced OEMs, will integrate these technologies to a next generation of EVs prototype. As a leading supplier, Continental will contribute by the innovative brake system. Elaphe, a leading technology company for e-motor design, will develop the suitable motor technology. TTTech, known for its future oriented network technologies for AI and autonomy-based systems, will be responsible for the networking technology. The academia team consists of the Vrije Universiteit Brussel (Coordinator), Tecnalia, Ikerlan and the Fraunhofer Gesellschaft. It will provide the technological basis, the modelling and the algorithms for this challenging endeavour. Finally, Idiada will conduct the testing and evaluations verifying and proving the achievement of the promised innovation.

The CLARUS project aims to connect the Sustainable Paradigm in the food industry and AI-based applications, with the goal of developing a platform with high communications and processing capabilities, as well as the use of standardized open protocols and data models that will allow resource consumption assessment and traceability for food industry processes. The project will be submitted under the call HE-CL4-DIGITAL-EMERGING-01-09: AI, Data and Robotics for the Green Deal and will target the non-robotic outcome. Currently, two pilots have been selected for validating the CLARUS solution. The first pilot focuses on the production of the frozen food, where energy and water consumption can be reduced using AI and data technologies. The other pilot focuses on the meat by-product production where the aim is to reduce the energy and maintain the quality of the products by optimizing the logistics of the by-products arrival. CLARUS ambitions include not only contributing to resource and logistic optimization methods through the two pilot solutions, but also making a more general contribution through the creation of a Green Deal Index (GDI).