Comparative study of the hydrogen generation during short term station blackout (STSBO) in a BWR

Polo-Labarrios, M.A.; Espinosa-Paredes, Gilberto

doi:10.1016/j.anucene.2015.04.027

Cited by 10 publications

(1 citation statement)

References 18 publications

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“…Continuing for illustrative purposes, high fidelity modeling of reactor thermohydraulics is a key component of the overall accuracy of RUAS water resupply to the SFP. Sandia National Lab's MELCOR model is extensively tested, verified, and widely use in the literature (Wang et al 2005;Darnowski et al 2015;Fernandez-Moguel 2015;Fernandez-Moguel and Birchley 2015;Polo-Labarrios and Espinosa-Paredes 2015;Sevón 2015;Wang et al 2015a,b). Figure 5 indicates MELCOR providing Figure 4 Reactor/SFP functionality.…”

Section: Lvc Simulation Frameworkmentioning

confidence: 99%

A Systems-Of-Systems Conceptual Model and Live Virtual Constructive Simulation Framework for Improved Nuclear Disaster Emergency Preparedness, Response, and Mitigation

Davis

Proctor

Shageer

2016

Journal of Homeland Security and Emergency Management

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Nuclear disasters have severe and far-reaching consequences. Emergency managers and first responders from utility owners to local, state, and federal civil authorities and the Department of Defense (DoD) must be well prepared in order to rapidly mitigate the disaster and protect the public and environment from spreading damage. Given the high risks, modeling and simulation (M&S) plays a significant role in planning and training for the spectrum of derivate scenarios. Existing reactor models are largely legacy, stove-piped designs lacking interoperability between themselves and other M&S tools for emergency preparedness system evaluation and training. Unmanned systems present a growing area of technology promising significant improvement in response and mitigation. To bridge the gap between current and future models, we propose a conceptual model (CM) for integrating live, virtual, and constructive (LVC) models with nuclear disaster and mitigation models utilizing a system-ofsystems (SoS) approach. The CM offers to synergistically enhance current reactor and dispersion simulations with intervening avatar and agent simulations. The SoS approach advances life cycle stages including concept exploration, system design, engineering, training, and mission rehearsal. Component subsystems of the CM are described along with an explanation of input/output requirements. A notional implementation is described. Finally, applications to analysis and training, an evaluation of the CM based on recently proposed criteria found in the literature, and suggestions for future research are discussed.

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