ABSTRACT
Objective To study the 137Cs source term from the Fukushima Daiichi nuclear power plant,based on inverse modeling,so as to provide reference for accident assessment and radiation protection.Methods The 137Cs source term was estimated by means of inverse modeling of nuclear accidents based on variational data assimilation combined with truncated total least squares (TTLS-VAR).The environmental monitoring data was balanced,and the dispersion model operator and monitoring data vector were corrected,in order to reduce the influence from atmospheric dispersion model error,and then improve the accuracy of inverse modeling of source term.Results The total amount of 137Cs released was estimated to be 1.74× 1016-3.73× 1016 Bq,with the highest peak of release rates estimated appearing on March 18,2011 and the average release rate in exceed of 1.00× 1012 Bq/s.The estimated total amount of 137 Cs was close to the data published by IAEA and UNSCEAR.Also,the estimated release sequences were in good consistent with Japanese analytical results of source terms and sequence of events.The highest peak of the estimated release rate curve corresponds to the leakage incident of unit 3.Conclusions In this study,the 137Cs source term from the Fukushima Daiichi nuclear power plant is estimated by using TTLS-VAR inverse modeling,which could provide the basis for accident assessment and radiation protection.
ABSTRACT
Objective: To correct the nonlinear error of sensor output, a new approach to sensor inverse modeling based on Back-Propagation Fuzzy Logical System (BP FS) is presented. Methods: The BP FS is a computationally efficient nonlinear universal approximator, which is capable of implementing complex nonlinear mapping from its input pattern space to the output with fast convergence speed. Results: The neuro-fuzzy hybrid system, i. e. BP FS, is then applied to construct nonlinear inverse model of pressure sensor. The experimental results show that the proposed inverse modeling method automatically compensates the associated nonlinear error in pressure estimation, and thus the performance of pressure sensor is significantly improved. Conclusion: The proposed method can be widely used in nonlinearity correction of various kinds of sensors to compensate the effects of nonlinearity and temperature on sensor output.
ABSTRACT
Objective To correct the nonlinear error of sensor output, a new approach to sensor inverse modeling based on Back-Propagation Fuzzy Logical System (BP FS) is presented. Methods The BP FS is a computationally efficient nonlinear universal approximator, which is capable of implementing complex nonlinear mapping from its input pattern space to the output with fast convergence speed. Results The neuro-fuzzy hybrid system, i.e. BP FS, is then applied to construct nonlinear inverse model of pressure sensor. The experimental results show that the proposed inverse modeling method automatically compensates the associated nonlinear error in pressure estimation, and thus the performance of pressure sensor is significantly improved. Conclusion The proposed method can be widely used in nonlinearity correction of various kinds of sensors to compensate the effects of nonlinearity and temperature on sensor output.