In SCALABLE, eminent industrials and academic partners will team up to achieve the scaling to unprecedented performance, scalability, and energy efficiency of an industrial LBM-based computational fluid dynamics (CFD) software. Lattice Boltzmann methods (LBM) have already evolved to become trustworthy alternatives to conventional CFD. In several engineering applications they are shown to be roughly an order of magnitude faster than Navier-Stokes approaches in a fair comparison and in comparable scenarios. LBM methods are also flexible so that they can be extended to handle complex, dynamically changing geometries, multiphase flows, and wide range of other multiphysics applications that are of high industrial relevance. In the context of EuroHPC, the distinguishing critical features of the LBM is the algorithmic locality stemming from an explicit time step. This makes the LBM especially well-suited to exploit advanced supercomputer architectures through vectorization, accelerators, and massive parallelization. In the public domain research code waLBerla, superb performance and unlimited scalability has been demonstrated, reaching more than a trillion (10^12) lattice cells already on Petascale systems. This becomes possible through systematic performance engineering and the development of an innovative computer science technology. WaLBerla performance excels because of its uncompromising unique, architecture-specific automatic generation of optimized compute kernels, together with carefully designed parallel data structures. waLBerla, however, is not compliant with industrial applications due to lack of a geometry engine and user friendliness for non-HPC experts. On the other hand, the industrial CFD software LaBS already has such industrial capabilities at a proven high level of maturity, but it still has performance worthy of improvement. Therefore, SCALABLE will transfer the leading edge performance technology from waLBerla to LaBS, thus breaking the silos between the scientific computing world and physical flow modelling world. The collaboration will deliver improved efficiency and scalability for LaBS to be prepared for the upcoming European Exascale systems. The project outcomes will be disseminated through the LaBS software and will directly benefit to the european industry as confirmed by the active involvement of Renault & Airbus in the project, and by the additional numerous letters of support from a wide aeronautics and automotive industrial community. Additionally, SCALABLE will also contribute to fundamental research. This will include energy efficient computing, GPU accelerated kernels, and a novel memory efficient sparse data structure available as open source software within the waLBerla framework.

Coordination and demonstration embracing all areas of IP4 and targets aiming to assist support ensure and promote a common technical approach across the IP4 to allow integrated and coherent Technical Demonstrations as outlined on Shift2Rail master plan. Te objective will be achieved through different activities involving and promoting the collaborative participation of associated projects (for members of Shit2Rail and from open calls) and the partners involved, through the close technical interface with the different projects and the coordination of the integration activities activities to support and achieve consistency and convergence amongst the different projects, feeding a reliable set of technical packages.

MaaSive project will be part of Shift2Rail (S2R), the first rail joint technology initiative focused on accelerating the integration of new and advanced technologies into innovative rail product solutions. It is framed within the innovation Program 4 (IP4), which addresses “IT solutions for attractive Railway services”. MaaSive continues and complements the work accomplished within previous projects, ATTRACkTIVE and CO-ACTIVE, in the areas of travel shopping, trip tracking, booking and ticketing, and the development of a travel companion. The project not only will enhance and provide extra functionalities to the existing IP4 ecosystem, but also makes emphasis in the compatibility of this ecosystem with the Mobility as a Service approach.

SnapEarth will unlock new value, derive actionable service ideas on top of EO big data collections, and anticipate future priorities by leveraging cutting-edge Artificial Intelligence and Cloud technologies and tools. Thanks to an innovative cloud agnostic product, SafeScale that is already operational on Copernicus RUS project led by CSSI, users and service providers on top of SnapEarth will benefit, in a transparent way, from processing platforms and data collections provided by any of the future C-DIAS and any cloud provider. This cloud brokering solution is providing a performant, cost effective environment, also protecting their investment, for the future third parties which are building their own services. The major breakthrough of SnapEarth comes with a new data analytics service, EarthSignature, which aims to automatically extract semantic information from satellite imagery. The extracted semantic information will be indexed by QWANT search engine and then be easily accessible to a wide range of user communities. EO experts and third parties will be able to train deep learning processing chains using their database of labelled EO images. The database will be near real time enriched. Therefore, SnapEarth allows the market move from analysing EO Big Data towards realizing Fast Data. It makes possible to buy basic imagery analysis as a commodity – much like we buy foundation data today. Several user communities are ready to engage in this new approach. SnapEarth proposes already several pilots projects. The first one (EarthSearch) will boost QWANT number of users through access to this wealth of data through natural language. The second one (EarthPress) involves press users who are very interested having contextual data linked to news. The following ones are linked to several EO vertical markets: EarthClimate, Agri, and Food. The last one EarthSelf-service is dedicated to professional third parties in the same model of the DIAS but ensuring independence.