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Abstract In this study, the grid inefficiency $$\sigma $$ σ for a mesh-type Frisch-grid ionization chamber (FGIC) was investigated using the finite element method and Monte Carlo method. A grid inefficiency $$\sigma $$ σ evaluation model was developed, which can determine the relationship between the physical parameters of the detector and the grid inefficiency with reasonable accuracy. An artificial neural network (ANN) was applied in the investigation of the grid inefficiency factor $$\sigma $$ σ . The trained ANN was able to describe and predict the grid inefficiency factor $$\sigma $$ σ with different physical parameters for the mesh-type FGIC. Thus, it can serve as a reference for the development of mesh-type FGICs and correct grid inefficiency $$\sigma $$ σ measurements.

In this research, for improving the measurement performance of Prompt Gamma-ray Neutron Activation Analysis (PGNAA) set-up, a new optimization method for set-up design was proposed and investigated. At first, the calculation method for Signal-to-Noise Ratio (SNR) was proposed. Since the SNR could be calculated and quantified accurately, the SNR was chosen as the evaluation parameter in the new optimization method. For discussing the feasibility of the SNR optimization method, two kinds of PGNAA set-ups were designed in the MCNP code, based on the SNR optimization method and the previous signal optimization method, respectively. Meanwhile, the single element spectra analysis method was proposed, and the analysis effect of single element spectra as well as element sensitivity were used for comparing the measurement performance. Since the simulation results showed the better measurement performance of set-up designed by SNR optimization method, the experimental set-ups were built for the further testing, finally demonstrating the feasibility of the SNR optimization method for PGNAA set-up design.