Given the current trend of the EU political agenda in the energy sector linking their strategy to accelerate decarbonization with the adoption of digital technologies, it is easy to deduce that supercomputing is a great opportunity for the clean energy transition in Europe (even beyond the current crisis caused by the invasion of Ukraine by Russia). However, while Europe is working towards a decarbonized energy ecosystem, with a clear vision and targets set by the European Green Deal, it is also recognized that energy domain scientists are not realizing the full potential that HPC-powered simulations can offer. This situation is a result of the lack of HPC-related experience available to scientists. To this end, different organizations and projects such as RISC2 are working so that a wide range of scientists in the energy domain can access the considerable experience accumulated through previous collaborations with supercomputing specialists.<\/p>\n

In accordance with all of the above, it seems appropriate to consider the launch of coordination actions between both domains at both the European and Latin American levels to support the development of adjusted data models and simulation codes for energy thematic areas, while making use of the latest technology in HPC.<\/p>\n

With different countries and regions in the world trying to win the race for the development of the most efficient Computing and Artificial Intelligence technology, it seems equally logical to support the development of high-performance computing research infrastructure. Scientific communities can now access the most powerful computing resources and use them to run simulations focused on energy challenges. Simulation enables planning and working towards tomorrow’s clean energy sources in a digital framework, greatly reducing prototyping costs and waste.<\/p>\n

As examples, there are several fields in which the advances that the exploitation of digital methodologies (HPC jointly with data science and artificial intelligence) can bring will produce the resolution of more ambitious problems in the energy sector:<\/p>\n

\n
• Improvement in the exploitation of energy sources<\/strong>\n
  \n
  • Weather forecast or turbines in off-and on-shore wind energy<\/li>\n
  • Design of new devices such as wind turbines, solar thermal plants, collectors, solid state batteries, etc<\/li>\n
  • Computational Fluid Dynamics (CFD) analysis of heat transfer between solar radiation, materials, and fluids<\/li>\n<\/ul>\n<\/li>\n
  • Design of advanced materials of energy interest<\/strong>\n
    \n
    • Materials for innovative batteries via accurate molecular dynamics and\/or ab initio simulations to design and characterize at the atomic-scale new cathodes and electrolytes<\/li>\n
    • Materials for photovoltaic devices via multiscale simulations where atomic-scale ab-initio simulations are combined with mesoscale approaches to design efficient energy harvesting devices<\/li>\n<\/ul>\n<\/li>\n
    • Energy distribution<\/strong>\n
      \n
      • Integrated energy system analysis, optimization of the energy mix and smart grids fed with renewable energies, and further distribution in the electricity grid<\/li>\n
      • Economic energy models<\/li>\n
      • Smart meters and sensor deployment and further application to energy efficiency in buildings, smart cities, etc Exploitation on additional infrastructures such as fog\/edge computing<\/li>\n
      • New actors (prosumers) in a distributed electricity market, including energy systems integration<\/li>\n<\/ul>\n<\/li>\n<\/ul>\n

        Behind all the previous items, there is a solid track of research that forms the foundations of this effort in reinforcing research on digital topics. Some examples are:<\/p>\n

        \n
        • Design of Digitalized Intelligent Energy Systems, for example, their application to cities in which zero-emissions buildings or intelligent power systems are pursued<\/li>\n
        • Deeper understanding of the physics behind the energy sources, for example, multiscale simulation or of the atmospheric flow for wind farm operation through CFD\u2013RANS or LES simulation coupled to mesoscale models taking advantage of the capabilities offered by exascale computing<\/li>\n
        • New designs of Fluids Structure Interactions (FSI), for example, for full rotor simulations coupled to Computational Fluid Dynamics (CFD) simulations. Structural dynamics (fatigue) in different devices<\/li>\n
        • Optimization of codes by the means of new mathematical kernels, not simply computational porting<\/li>\n
        • Integration of different computing platforms seamlessly combining HPC, HTC, and High-Performance Data Analytics methodologies<\/li>\n<\/ul>\n

          Moreover, the advanced modeling of energy systems is allowed thanks to the tight synergy with other disciplines from mathematics to computer science, from data science to numerical analysis. Among the other high-end modeling requires:<\/p>\n

          \n
          • Data Science, as handlina g large volume of data is key to energy-focused HPC and HTC simulations and data-driven workflow<\/li>\n
          • Designs customized machine and deep learning techniques for improved artificial intelligence approaches<\/li>\n
          • Efficient implementation of digital platforms, their interconnections and interoperability<\/li>\n<\/ul>\n

             <\/p>\n

            By CIEMAT<\/a><\/p>\n","protected":false},"excerpt":{"rendered":"

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