Simulation based or dynamic probabilistic risk assessment methodologies were primarily developed for proving a more realistic and complete representation of complex systems accident response. Such simulation based methodologies have proven to be particularly powerful for systems with control loops and complex interactions between its elements, be they hardware, software, or human, as they provide a natural probabilistic environment to include physical models of system behavior (e.g., coupled neutronics and thermal hydraulic codes for nuclear power plants), mechanistic models of materials or hardware systems to predict failure, and those of natural hazards. Despite the advancements in simulation based methodologies, the fundamental challenge still persists as the space of possible probabilistic system trajectories is nearly infinite in size in simulating even systems of relatively low complexity. In this paper, a framework is developed to identify rare and extreme events and enabling the use of reverse trajectories to trace failures (or other system states) to causes for potential mitigation actions. This framework consists of an Intelligent Guidance module, Trajectory Generation module and Physical Simulation module. The Intelligent Guidance module provides planning information to the Trajectory Generation module that creates scenarios by interacting with the Physical Simulation in its environment. In turn, system trajectories or scenarios are created and post processed to provide updating information to the Intelligent Guidance module or aggregate the results when stopping criteria are met. The objective of guided simulation is to control the growth of the scenario tree and to efficiently identify important scenarios that meet single or multiple criteria. We present several solution strategies, both qualitative and data driven for each module.
翻译:模拟或动态概率风险评估方法主要是为证明复杂系统事故反应的更现实和完整代表性而开发的。这种模拟方法对于控制环及其各元素之间复杂互动的系统,无论是硬件、软件还是人,证明特别强大,因为它们提供了自然概率环境,以包括系统行为物理模型(例如核电厂的中子和热液压代码结合),材料或硬件系统预测故障的机械模型,以及自然灾害的机械模型。尽管在模拟方法方面取得了进展,但基本挑战仍然存在,因为可能的概率系统轨道空间在模拟甚低的系统上几乎是无限的。在本文件中,开发了一个框架,以确定罕见和极端事件,并允许使用逆轨跟踪失败(或其他系统状态),以追踪潜在缓解行动的原因。这个框架包括智能指导模块、轨迹生成模块和物理模拟模拟模块。这个智能指导模块向当前目标的模拟系统提供规划信息信息,在模拟或模拟模型中,通过模拟模型或模拟模型的模型,将模拟模型和模拟模型的模型生成后期演算结果,以模拟或模拟式格式生成。