Reactor Power Level Estimation by Fusing Multi-Modal Sensor Measurements

EasyChair Preprint 3513

Abstract

Estimates of the power level of a nuclear reactor based on measurements from an independent monitoring sensor system can help in the compliance verification of its declared operations. We present a three-level fusion method to estimate the power level of a nuclear reactor using features derived from infrared, electromagnetic, and acoustic sensor measurements collected in proximity to the reactor. Based on a simplified analytical model of the secondary coolant system of the reactor, we identify partial regression functions of the power level in terms of the temperature difference between inlet and outlet of coolant pipes, and the activity levels of four fans and four pumps, which are estimated as features from the sensor measurements. The power level estimator employs a combination of aggregate and complementary fusion steps at three levels to incorporate the multi-modal features in a structure that reflects the secondary cooling system and its partial regression functions. Using the measurements from a test campaign at an operational reactor, we show that this estimator achieves 3.47% or lower root mean square error under 5-fold cross validation. More generally, these results illustrate a progressive reduction in estimation error as additional modalities are appropriately incorporated, and that the fuser outperforms single modality features and their sub-combinations.

Keyphrases: Multiple Classifiers, Nuclear Reactor, multi-sensor fusion, reactor power level, secondary coolant system

@booklet{EasyChair:3513,
  author    = {Nageswara Rao and Christopher Greulich and Satyabrata Sen and Kenneth Dayman and Jason Hite and Will Ray and Richard Hale and Andrew D. Nicholson and Jared Johnson and Riley D. Hunley and Monica Maceira and Chengping Chai and Omar Marcillo and Tom Karnowski and Randall Wetherington},
  title     = {Reactor Power Level Estimation by Fusing Multi-Modal Sensor Measurements},
  howpublished = {EasyChair Preprint 3513},
  year      = {EasyChair, 2020}}