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White Paper: LVC Simulation

Presagis software is used to build conceptual models and Live Virtual Constructive (LVC) Simulation Framework to help agencies and facilities better prepare.

Learn how Presagis simulation software is being used in the building of conceptual models and Live Virtual Constructive (LVC) Simulation Framework to help agencies and facilities better prepare for nuclear disasters and emergencies.

Dr. Michael Proctor, Matthew Davis, and Buder Shageer from UCF’s Industrial Engineering and Management Systems department used a combination of STAGE, Terra Vista, and HeliSIM software to show how unmanned systems can perform through various scenarios, across challenging terrains, and within diverse atmospherics.

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-of-systems (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.