• Nuclear Engineering and Technology
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• v.55 no.9
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• pp.3409-3416
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• 2023

Licensing the next-generation of nuclear reactor designs requires extensive use of Modeling and Simulation (M&S) to investigate system response to many operational conditions, identify possible accidental scenarios and predict their evolution to undesirable consequences that are to be prevented or mitigated via the deployment of adequate safety barriers. Deep Learning (DL) and Artificial Intelligence (AI) can support M&S computationally by providing surrogates of the complex multi-physics high-fidelity models used for design. However, DL and AI are, generally, low-fidelity 'black-box' models that do not assure any structure based on physical laws and constraints, and may, thus, lack interpretability and accuracy of the results. This poses limitations on their credibility and doubts about their adoption for the safety assessment and licensing of novel reactor designs. In this regard, Physics Informed Neural Networks (PINNs) are receiving growing attention for their ability to integrate fundamental physics laws and domain knowledge in the neural networks, thus assuring credible generalization capabilities and credible predictions. This paper presents the use of PINNs as surrogate models for accidental scenarios simulation in Nuclear Power Plants (NPPs). A case study of a Loss of Heat Sink (LOHS) accidental scenario in a Nuclear Battery (NB), a unique class of transportable, plug-and-play microreactors, is considered. A PINN is developed and compared with a Deep Neural Network (DNN). The results show the advantages of PINNs in providing accurate solutions, avoiding overfitting, underfitting and intrinsically ensuring physics-consistent results.

• Journal of International Area Studies (JIAS)
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• v.14 no.3
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• pp.57-80
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• 2010

This study aims at the evaluation of procedural changes and policy outcome caused by the Open Method of Coordination(OMC) on Social Inclusion in the European Union. The policy instruments of the OMC introduced by the Lisbon Council can be divided into two groups: the outcome-oriented New Public Management(NPM) and the process-oriented Directly Deliberative Polyarchy(DDP). By considering the adoption process of the NPM instruments, it can be said that OMC could not be effective due to the vagueness of its objectives, the institutional barriers in decentralized decision-making, and the rejection of benchmarking by the Member States. The intended learning by deliberation and peer review as indicated by the normative DDP, is hard to achieve because they are not so reflexive due to relatively restricted and closed participation. We also cannot find any significant reduction of poverty after the long implementation of the OMC. Considering the higher recognition of poverty problem and expanding NGOs concerned with it, however, we may see some significant impact in the future.