HEROES
GLAVNA DIREKTSIA BORBA S ORGANIZIRANATA PRESTUPNOST
CE Beacons
PS Life Style
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Sitra, Finnish innovation fund
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CSCP - (Collaborating Center on Sustainable Consumption and Production)
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Euro Health Net
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Hot or cool institute
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ICLEI
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Solita OY
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Athena RC
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Circular Change
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City of Ljubljana
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DECO
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EKPIZO
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Fondazione Sviluppo Sostenibile
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Greenapes
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Let’s do it foundation
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Zeytince
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TAILTEST
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To perform structural tests and FE models validation to support the certification process of an innovative rotorcraft vertical tail.
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To develop, apply and validate advanced numerical models for the simulation of debonding propagation in adhesively bonded or welded structural joints.
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To design and manufacture an innovative multipurpose test rig to be used for testing a full-scale rotorcraft fin, as well as, parts of the fin structure like the spar-to-skin joints. The developed test rig will include a load application mechanism able to apply loads representative for rotorcraft tail structures. Moreover, the test rig will be designed in a way that a variable stiffness of the fuselage to fin joint can be achieved.
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To perform structural tests on a rotorcraft vertical tail, up to ultimate load for at least the representative load cases, applying an adequate number of conventional measuring sensors like strain gages (SGs) and Linear Variable Differential Transformers (LVDTs)or more sophisticated contactless strain measurement systems (e.g. Digital Image Correlation - DIC).
EnerMan
ENERMAN envisions the factory as a living organism that can manage its energy consumption in an autonomous way. It will create an Energy sustainability management framework collecting data from the factory and holistically process them to create dedicated energy sustainability metrics. These values will be used to predict energy trends using industrial processes, equipment and energy cost models. ENERMAN will deliver an autonomous, intelligent decision support engine that will evaluate the predicted trends and access if they match predefined energy consumption sustainability KPIs. If the KPIs are not met, ENERMAN will suggest and implement changes in energy affected production lines control processes: an energy aware flexible control loop on various factory processes will be deployed. The ENERMAN administrators will be able to use the above mechanisms in order to identify how future changes in the production lines can impact energy sustainability using the ENERMAN prediction engine (based on digital twins) to visualize possible sustainability results when in-factory changes are made in equipment, production line. The ENERMAN digital twin will predict the economic cost of the consumed energy based on the collected and predicted Energy Peak load tariff, Renewable Energy System self-production, the variations in demand response, possible virtual generation and prosumer aggregation. Finally, ENERMAN considers the operators actions within the production chain as part of a factory’s energy fingerprint since their activity within the factory impacts the various production lines. In ENERMAN, we include a training mechanism with suggested personnel good practices for energy sustainability improvement through the production lines. Current and predicted energy consumption/sustainability trends on specific assets of the factory are collected and visualized in a Virtual, eXtended reality model of the factory to enhance the situational energy awareness of the factory personnel.
DEMONSTRATE
The objective of DEMONSTRATE, aligned to the ‘Innovative light metallic and thermoplastic airframe section full scale testing’ (JTI-CS2-2020-CFP11-AIR-03-10) topic is the full-scale testing of three airframe section demonstrators, two metallic and a thermoplastic fuselage panel with an integrated stiffening structure, to demonstrate their structural integrity, supported by advanced simulation methodologies correlated with experimental data. A number of enabling technologies have been planned to be developed and matured within the duration of the project, which will result to the realisation of an efficient, reliable and cost-effective test bench suitable for the full-scale testing of curved aeronautical panels, including: the development of a virtual testing methodology for the definition of the stiffened panels boundary and loading conditions; the further development of an innovative, cost-efficient, easily adaptable fuselage panel full-scale test bench concept; the development and/or adaptation of a wide range of novel measurement techniques; the development and application of advanced simulation methodologies. The developed test bench will be used for the execution of static tests on advanced metallic and thermoplastic curved integrally stiffened full-scale panels, representative of a business jet fuselage structure and the execution of an endurance test on an integrally stiffened 4th generation Al-Li curved panel.
SUSTAINABLE
SUSTAINABLE strategic goal is to test and validate new edge artificial intelligence (AI) systems as a Decision Support System, on sound economic and technical basis, for suitable precision agriculture management procedures according to the specific climate, geographical and environment conditions. Having a novel tool to face adversities in agriculture, especially in developing countries, will produce positive effects in the global economy (maintaining the cost of agricultural products), will reduce the scale of economic emigration (often due to one or several years of low crop) and will help distribute more evenly richness across the planet. The strength of this project is the multidisciplinary integration of competences together with the validation of project result in a real environment.
HEAP
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An innovative toolset that helps software developers profile and parallelize existing sequential implementations by exploiting top-level pipeline-style parallelism.
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A highly configurable cache architecture that can be tailored to an application by using the same profiling data as those that were used for parallelization, in order to fully exploit the available computing power.
HEAP
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An innovative toolset that helps software developers profile and parallelize existing sequential implementations by exploiting top-level pipeline-style parallelism.
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A highly configurable cache architecture that can be tailored to an application by using the same profiling data as those that were used for parallelization, in order to fully exploit the available computing power.
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