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Title: Implicit Kalman filter algorithm for nuclear reactor analysis

Conference · · Trans. Am. Nucl. Soc.; (United States)
OSTI ID:6839449

Artificial intelligence (AI) is currently the hot topic in nuclear power plant diagnostics and control. Recently, researchers have considered the use of simulation as knowledge in which faster than real-time best-estimate simulations based on first principles are tightly coupled with AI systems for analyzing power plant transients on-line. On-line simulations can be improved through a Kalman filter, a mathematical technique for obtaining the optimal estimate of a system state given the information contained in the equations of system dynamics and measurements made on the system. Filtering can be used to systemically adjust parameters of a low-order simulation model to obtain reasonable agreement between the model and actual plant dynamics. The authors present here a general Kalman filtering algorithm that derives its information of system dynamics implicitly and naturally from the discrete time step-series of state estimates available from a simulation program. Previous research has demonstrated that models adjusted on past data can be coupled with an intelligent controller to predict the future time-course of plant transients.

Research Organization:
Univ. of Michigan, Ann Arbor
OSTI ID:
6839449
Report Number(s):
CONF-861102-
Journal Information:
Trans. Am. Nucl. Soc.; (United States), Vol. 53; Conference: American Nuclear Society and Atomic Industrial Forum joint meeting, Washington, DC, USA, 16 Nov 1986
Country of Publication:
United States
Language:
English