Volume 85, Issue 1

Coupled neutronic and thermal hydraulic calculation codes are verified for calculating the design of modern and prospective types of nuclear reactors. This verification is done by comparing experimental and calculated results for stationary and transient conditions. This paper presents ShIPR (Shell of Intelligent Package for Reactor) Integrated Development Environment with automatic generation of head programs based on the chain of computational modules. The aim of this study is to find the reason of a discrepancy in the modelling of sub-critical states that was found in previous work. The comparison of ShIPR stationary module with Monte-Carlo code (MCU) and experimental results on ASTRA HTGR critical facility was presented in the paper. To compare the detector readings and MCU calculation with the ShIPR module, the interpolation cross-section procedure was performed. This procedure allows simulating 235 U fission reaction rates (detector readings) in the complicate, annular core, using the micro-cross sections prepared by the cell-lattice code. We found that the calculation accuracy of stationary ShIPR module is on an acceptable level but the macro constants for control rods need to be prepared independently, given with surroundings.

The most important material for shielding is concrete in nuclear facilities which performance can be improved by addition some Nanoparticles (NP) at the various concentrations. Nanoparticles, which have a distinctive potential for bio-radiation and shielding of nuclear reactors, are used in many areas due to their special characteristics, which lead to an improvement in the mechanical properties and the pore structure of the concrete shield. The aim of this research was to initiate a novel coefficient (n), experiment to theory density ratio for integrated NP at different nanoparticle concentrations (xnano), established upon purely mathematical viewpoints and some appropriate physical objectives.

Since estimating the minimum departure from nucleate boiling ratio (MDNBR) requires complex calculations, an alternative method has always been considered. One of these methods is neural network. In this study, the Back Propagation Neural network (BPN) and Radial Basis Function Neural network (RBFN) are introduced and compared in order to estimate MDNBR of the VVER-1000 light water reactor. In these networks, the MDNBR were predicted with the inputs including core mass flux, core inlet temperature, pressure, reactor power level and position of the control rods. To obtain the data required to design these neural networks, an externally coupledcode was developed and its ability to estimate the thermo-hydraulic parameters of the VVER-1000 reactor was compared with other numerical solutions of this benchmark and the Final Safety Analysis Report (FSAR). After ensuring the accuracy of this coupled-code, MDNBR was calculated for 272 different conditions of reactor operating, and it was used to design BPN and RBFN. Comparison of these two neural networks revealed that when the output SMEs of the two systems were approximately the same, the training process in RBFN was much faster than in BPN and the maximum network error in RBFN was less than in BPN.

In this paper, at the same time, two separate objectives and two safety and operational constraints are chosen to optimize fuel reloading pattern of a Material Testing Reactor (MTR), independently and coherently. This is one of the most difficult type of engineering problems as a constrained, non-continuous, combinatorial, and fully multi-objective optimization problem. Decision space is a non-continuous multimodal space restricted by both of the combinatorial and safety constraints. A smart software application and a robust hybrid algorithm have been developed to get Pareto optimal set with respect to both of the economy of irradiating utilizations and nuclear safety based on the heuristic soft computing. The hybrid algorithm is composed of a fast and elitist Multi-Objective Genetic Algorithm (MOGA) and a fast fitness function evaluating system based on the semi-deep learning cascade feed forward Artificial Neural Networks (ANNs). The smart software is used to produce database automatically required for the ANN training and test data. It can be also used to revise data accurately, impose further irradiating benefits or Operating Limits and Conditions (OLCs), and to advise the reactor supervisor on the most desire pattern based on the smart searches and filtering. The results are highly promising. For more details, optimization results dominate conventional operating core parameters, significantly. Also chosen OLCs are protected. Furthermore, this is very good practice to reach a fully developed practical application of the complex soft computing for the nuclear fuel management problems.

An essential component of safety analyses is the investigation of accident scenarios. In this paper water ingress scenarios of spent fuel containers, as they may occur during transport or storage, are examined. In the main body of this paper, a number of paths are studied through which water can gain access to the spent fuel cask and eventually reach the fuel pellet, potentially resulting in an increase in reactivity as a result of over-moderation. The primary objective of this project was to perform an assessment of what, in the unlikely event of a Fukushima- type accident, the impact would be on the reactivity of the cask by analyzing a gradual increase in water level in the spent fuel casks. In addition, the way the k eff of the system responds to such an increase is discussed. The paper also provides the results of an assessment of the reactivity effect of water ingress via various pathways/channels.

Steam generators are one of the most important components of pressurized-water reactors. This component plays the role of heat transfer and pressure boundary between primary and secondary side fluids. The Once Through Steam Generator (OTSG) is an essential component of the integrated nuclear power system. In this paper, steady-state analysis of primary and secondary fluids in the Integral Economizer Once Through Steam Generator (IEOTSG) have been presented by Single Heated Channel (SHC) and subchannel modelling. Models have been programmed by MATLAB and FORTRAN. First, SHC model has been used for this purpose (changes are considered only in the axial direction in this model). Second, the subchannel approach that considers changes in the axial and also radial directions has been applied. Results have been compared with Babcock and Wilcox (B&W) 19- tube once through steam generator experimental data. Thermal- hydraulic profiles have been presented for steam generator using both of models. Accuracy and simplicity of SHC model and importance of localization of thermal-hydraulic profiles in subchannel approach have been proved.