Advanced numerical simulation and modelling for reactor safety – Contributions from the CORTEX, HPMC, McSAFE and NURESAFE projects

A complete list of the papers published within the projects can be found on the project respective websites: http://cortex-h2020.eu (for CORTEX), http://www.fp7-hpmc.eu (for HPMC), http://www.mcsafe-h2020.eu (McSAFE), http://www.nuresafe.eu (for NURESAFE).

[1] C. Demazière et al., Overview of the CORTEX project, Proc. Int. Conf. Physics of Reactors - Reactor Physics paving the way towards more efficient systems (PHYSOR2018), Cancun, Mexico, April 22-26, 2018 (2018). [OpenAIRE]

[2] V. Sanchez et al., High Performance Monte Carlo Computing Projects: from HPCM to McSAFE, NUGENIA Forum, Ljubljana, Slovenia, April 14, 2015.

[3] L. Mercatali et al., McSAFE projects highlights, International Multi Physics Validation Workshop, North Carolina State University, USA, June 2017.

[4] E. Deville and F. Perdu, Documentation of the Interface for Code Coupling: ICOCO, CEA, Paris (2012).

[5] B. Chanaron et al., Advanced multi-physics simulation for reactor safety in the framework of the NURESAFE Project, Ann. Nucl. Energy, 84, 166-177 (2015).

[6] B. Chanaron, The European NURESAFE simulation project for reactor safety, Proc. Int. Conf. Nuclear Engineering (ICONE22), Prague, Czech Republic, July 7-11, 2014 (2014).

[7] C. Demazière, Multi-physics modelling of nuclear reactors: current practices in a nutshell, Int. J. Nucl. Energy Sci. Technol., 7 (4), 288-318 (2013). [OpenAIRE]

[8] I. Lux and L. Koblinger, Monte Carlo particle transport methods: neutron and photons calculations, CRC Press, Boca Raton, FL, USA (1991).

[9] A. Ivanov et al., Internal multi-scale multi-physics coupled system for high fidelity simulation of light water reactors, Ann. Nucl. Energy, 66, pp. 104-112 (2014).

[10] M. Däubler et al., High-fidelity coupled Monte Carlo neutron transport and thermalhydraulic simulations using Serpent 2/SUBCHANFLOW - Part I: Implementation and solution verification, Ann. Nucl. Energy, 83, pp. 352-375 (2015).

[11] F. Calivà F et al., A deep learning approach to anomaly detection in nuclear reactors. Proc. 2018 Int. Joint Conf. Neural Networks (IJCNN2018), Rio de Janeiro, Brazil, July 8-13, 2018 (2018). [OpenAIRE]

[12] F. De Sousa Ribeiro et al., Towards a deep unified framework for nuclear reactor perturbation analy-sis. Proc. IEEE Symposium Series on Computational Intelligence (SSCI 2018), Bengaluru, India, November 18-21, 2018 (2018).

[13] J. Dufek and J. E. Hoogenboom, Description of a stable scheme for steady-state coupled Monte Carlo-thermal-hydraulic calculations, Ann. Nucl. Energy, 68, pp. 1-3 (2014). [OpenAIRE]

[14] J. E. Hoogenboom, Demonstration of the time-dependence after a control rod movement, HPCM Deliverable D3.10 (2013).