SINDRI: Synergistic utilisation of INformatics and Data centRic Integrity engineering

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The long-term, safe operation of large industrial assets, including critical low-carbon energy generation infrastructure, will become prohibitively costly if we fail to update, streamline and automate traditional manual processes of design, fabrication, and life-time assessment. This partnership will develop an overarching digital framework encompassing a suite of models that simulate the behaviour of materials from their entry into service through to their end of life. The framework will be developed so it can be incorporated into EDF's federated ecosystem of multi-physics Digital Twins, replacing current manual processes. The partnership will:- Use state-of-the-art characterisation tools, such as those available at the Henry Royce Institute, to observe and quantify the conditions of the material after various conventional and advanced fabrication processes as well as degradation mechanisms relevant to power generation. Advanced characterisation tools enable the assessment of large volumes of materials that fully capture their inherent variability and inhomogeneity, impossible until very recently.- Harmonise the data structure obtained across characterisation platforms, allowing data integration and building a full picture of the material behaviour as a result of fabrication and in-service degradation.- Exploit deep learning algorithms, developed with input from the Alan Turing Institute, to interrogate large microstructural datasets obtained from characterisation work. Taking advantage of access to the materials science community, the partnership will shape the deep learning algorithms with human expertise, maintaining the fidelity of data analysis while removing the slow human-dependent aspects.- Develop and validate meso-scale material models as faithful digital twins of material behaviour, simulating the entry to service condition for various fabrication methods, and in-service degradation mechanisms, built on knowledge gained from characterisation analysis.- Deploy model reduction techniques developed by industrial partner, EDF, to identify the governing parameters of the meso-scale models. Develop engineering models that are informed by meso-scale behaviour but applicable to component- scale, by preserving the governing parameters of the macroscale behaviour. Validate these engineering models against high-fidelity tests on macro-mechanical components.- Build a probabilistic analysis toolkit that can assess the level of safety confidence of a component by applying applied probability theory to the results of the macro-mechanical engineering models informed by the material variability from meso-scale models and accounting for the uncertainty associated with the operational envelope of plants.- Undertake an ultimate verification and validation of the framework and its associated suite of modular material models based on extracted case studies of critical component failure from historic EDF data. By developing various models of materials behaviour in a modular way within the overarching digital framework, the partnership will be able to assemble the fabrication and degradation modules relevant to the historic case studies and quantify the probability of failure using the probabilistic toolkit.- Incorporate the framework and its models as the basis of a component material Digital Twin within EDF's federated ecosystem of multi-physics Digital Twins.