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University of A Coruña

Country: Spain

University of A Coruña

57 Projects, page 1 of 12
  • Funder: EC Project Code: 101108665
    Funder Contribution: 181,153 EUR

    The main objective of FUNnY SUMO is to develop a highly accurate 3D numerical model of monopile offshore wind turbines (MOWT) considering simultaneously all the environmental loads (current + waves + wind), to be used as a high-fidelity model for the definition of a surrogate model that enables the application of data-driven design in the next generation of MOWTs. This project will set the playground for the next generation of offshore data-driven wind turbines and will have an impact on the European industry while boosting the candidate’s applied-research career. The CFD and Surrogate models will be validated with a recent experimental study by FUNnY SUMO’s secondment. The method will be divided mainly into two parts; First is CFD modeling, where a CFD model will be developed for each load acting on the MOWT, and then integration in a complete CFD model of the current + waves + wind effects on the MOWT, aiming at 80% accuracy relative to experimental measurements. The second is developing three Surrogate models using the CFD results. This project is divided into 5 Work Packages (WPs) to efficiently achieve the specific objectives of this proposal. WP1 deals with management, The Specific objectives will be addressed within WP2–4, where WP2 considers Meshing and model settings, WP3 is the validation of numerical models, WP4 deals with the Surrogate model definition, and WP5 addresses the Dissemination activities. The results of the project will facilitate Europe’s energy independence by bolstering innovative design processes in MOWTs based on the application of surrogate models (SM). Furthermore advanced MOWTs will be manufactured and deployed in the EU’s geographical area.

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  • Funder: EC Project Code: 101106038
    Funder Contribution: 236,500 EUR

    Deep mineralization in mafic rocks has emerged as one of the most secures technologies for long-term carbon sequestration. Although small-scale validations have shown promising results, the fracture-controlled hydraulic properties of mafic rocks are still not properly understood, which imposes significant uncertainties for industrial-scale operation. The objective of GEOMIMIC is to develop a better understanding of fluid flow and fracture-matrix interaction to improve carbon mineralization efficiency in fractured mafic reservoirs. In the outgoing phase at Georgia Institute of Technology (USA), we will develop a comprehensive taxonomy of mafic rocks, which will set the foundations for a screening framework to evaluate potential storage sites. We will also provide new fundamental knowledge on transport properties and coupled hydro-chemo-mechanical processes in fractured media using an innovative experimental setup for studying reactive fluid transport in fractured samples. We will perform complementary numerical simulations for upscaling testing results to the field spatial and temporal scales relevant to carbon mineralization. Finally, in the return phase at Universidade da Coruña (Spain), we will use for the first time a testing approach originally conceived to measure rock fracture toughness, to assess coupled chemo-mechanical phenomena. GEOMIMIC will contribute to the selection of suitable CO2 storage sites and accelerate the design and operation of field validations, which is urgently required as climate change intensifies. This MSCA PF will provide me a unique opportunity to learn core technical skills in subsurface applications, as well as key transferable skills to become an independent researcher. I will take fully advantage of available facilities and support from my supervisors, who are highly experienced experts in the subject-matter of the project and are in an excellent position maximize the outputs of the project and benefit my future career.

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  • Funder: EC Project Code: 101063104
    Funder Contribution: 165,313 EUR

    Grammar Assistance Using Syntactic Structures. In the times when a human's right to a cultural identity is often at odds with the standardized requirements placed by e.g. employment systems, automatic grammar coaching serves an important purpose of advising on standard grammar varieties while not imposing social pressures typical for a traditional grammar classroom or reinforcing established social roles. Such systems already exist but most of them are for English and few of them offer meaningful instructive feedback. Furthermore, they typically rely completely on neural methods, which means they require huge computational resources which most of the world cannot afford. I propose a grammar coaching system for Spanish that relies on (i) a rich linguistic formalism capable of giving informative feedback; and (ii) a new, faster parsing algorithm which makes using this formalism practical in a real-world grammar coaching application. My approach is feasible for any language for which there is a formalized grammar and is less reliant on hugely expensive and environmentally costly neural methods (while still benefiting from them where appropriate). I thus seek to contribute to Greener AI and to address global education challenges by raising the standards of inclusivity and engagement in grammar coaching while retaining the precision of systematized linguistic knowledge.

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  • Funder: EC Project Code: 273515
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  • Funder: EC Project Code: 274660
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