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BackgroundFast neutron pulsed reactors are sensitive to wall scattering neutrons, and their waveforms are changed by reflected neutrons. In addition, their operation may be adversely affected when there are too many reflected neutrons.PurposeThis study aims to solve the problem of fast neutron pulse reactor wall reflection neutron.MethodThe point reactor kinetic method containing the reflection effect, Monte Carlo method, and ANSYS were combined to analyze the Godiva-I transient process in fast neutron pulsed reactor with wall-reflected neutron effect. Firstly, the quench coefficient of the fast neutron pulsed reactor was calculated. Then, the point reactor kinetic method containing the reflection effect was established. Finally, the thermal power obtained from the neutronics calculation was combined with the ANSYS thermal-mechanical module to establish the thermal-mechanical calculation method for fast neutron pulsed reactor, and the effect of wall-reflected neutrons was analyzed and calculated.ResultsThe results show that the reflected neutrons increased the rear edge of the pulse. The reactivity decreases when the flat is washed whilst the core displacement and stress are improved.ConclusionsThe method established in this study can reasonably explain the phenomenon of reduction in attenuation and the increase in power after pulse.

Aimed at the problems in continuous crowd sensing,such as the increased privacy requirements,the unreliable perception data collected and the low enthusiasm of users to participate,this paper proposes an incentive mechanism based on symmetric encryption and double-layer truth discovery(SDIM).First,the symmetric encryption algorithm is used to protect the privacy of the perceived data.When the privacy requirements are high and the number of perceptions is large,the computing overhead and the time of data encryption and reward computing will be greatly reduced.Second,based on a double-layer truth discovery model,an incentive mechanism supporting data reliability evaluation is proposed.The purpose is to simultaneously realize the real time reward of continuous crowd sensing,and improve the fairness of reward when the participants have malicious behavior.Finally,the dual privacy analysis of the proposed method is illustrated.The simulation results show that the proposed method can effectively calculate the true value and the reward according to the data reliability.Notably,it is obviously superior to the comparative model in the time of data encryption and reward computing,and can calculate the reward more fairly when the participants have malicious behavior.

BackgroundRadiation shielding design is an important part of reactor design, and the development of new nuclear power technology for various kinds of reactors has put forward new demands on radiation shielding optimization design methods.PurposeThis study aims to overcome the shortcomings of the traditional multi-objective optimization methods for shielding structures in dealing with the optimization problem of 3D shielding structures, such as slow optimization speed, difficulty in convergence, and poor globalization.MethodsBased on the non-dominated sorting genetic algorithm Ⅲ (NSGA-Ⅲ), the many-objective optimization method for 3D shielding structure design for nuclear reactor was proposed. The Monte Carlo N-Particle Transport Code (MCNP) was employed to analyze comparative performance of the NSGA-III optimization method on the basis of the 3D shielding structure model of nuclear reactors, and shield weight, volume and radiation dose rate in specific regions were taken as the optimization targets.Results & ConclusionsThe numerical simulation results show that the NSGA-III based optimization method for 3D shielding structure design can search for the Pareto-optimal front more efficiently and stably, providing a new idea for the optimization of radiation shielding design.

BackgroundThe mass of a space nuclear power system is a critical parameter due to limitation of loading and high cost of transportation. The radiation shielding system is one of the main contributions of the self-weight of the space nuclear power system.PurposeThis study aims at the shielding optimization problem for the mass minimization of space nuclear power system.MethodsFirstly, the calculation model for the Topaz-II space stack was established and the shielding material was selected. Secondly, a masked multi-objective optimization model was built on the base of Non-dominated Sorting Genetic Algorithm-II (NSGA-II) to obtain an optimization scheme. Then, an intelligent decision support system for the shielding scheme to improve the optimization efficiency was established by coupling the BP neural network algorithm. Finally, the adjustment plan was obtained according to the intelligent decision support system, and verified by using Topaz-II space reactor model.ResultsVerification results show that the intelligent decision support system can be used to set up a basic theoretical model according to the actual needs of the project, and finally provide a space stack shielding scheme that meets the engineering needs.ConclusionsThe validity and feasibility of the method in this paper are proved, providing theoretical and technical support for the optimization of radiation shielding of space reactors.

Time synchronization is the basis and prerequisite for precise information perception in intelligent mines. Many characteristics of underground space in coal mines, such as narrow and long terrain and uneven surface of the roadway, can cause large cumulative effect of synchronization error and poor reliability of signal transmission, which brings great challenges to the research of time synchronization technology among wireless nodes. Two basic time synchronization methods, hierarchical time synchronization and distributed time synchronization method, are introduced in this paper. The special requirements of time synchronization methods for underground coal mine IoTs are analyzed. In addition to the convergence and synchronization precision, the energy efficiency, the topology robustness and the scalability of the synchronization algorithm need to be considered as well. The method should have less communication volume, longer synchronization period and certain topology robustness. Moreover, the method should be able to reduce the cumulative effect of synchronization error caused by large network diameter, and adapt to impact of network scale changes. The current research status of time synchronization of underground coal mine IoTs perception layer is analyzed, and it is concluded that the current research results mainly focus on network structure, synchronization precision and synchronization energy consumption. The research direction of time synchronization technology for underground coal mine IOTs perception layer is prospected, and it is pointed out that the focuses of future research is to design a time synchronization method with certain robustness to topology and time delay in the context of the particularity of underground communication environment and space environment in coal mine: ① It is suggested to improve the robustness of time synchronization method to topology from the perspective of topology dynamic maintenance. ② It is advised to improve the convergence speed of consistent time synchronization method from the perspective of communication topology virtual construction. ③ It is proposed to improve the robustness of time synchronization method to transmission delay from the perspective of timestamp processing and matrix completion.

In order to meet the requirements of high accuracy and low energy consumption of time synchronization among massive nodes of coal mine underground network, a distributed accurate time synchronization algorithm is proposed based on the time-sensitive network (TSN) of coal mine with non-uniform clustering structure of multiple Sink nodes. The underground coal mine TSN is divided into 3 layers, including the TSN convergence layer containing all Sink nodes, the main network consisting of Sink nodes and all cluster head nodes within their respective communication radius, and the secondary network consisting of cluster head nodes and common nodes in the cluster. The TSN convergence layer uses the gPTP algorithm to achieve nanosecond time synchronization among Sink nodes. The main network uses an optimized algorithm based on Kalman filter to predict and compensate for frequency offset, phase offset and noise errors so as to improve the network time synchronization accuracy. The secondary network uses the broadcast-based single and two way hybrid synchronization algorithm to reduce the number of synchronization message packets. The simulation results show that the proposed algorithm can improve the accuracy and stability of network time synchronization effectively and reduce the energy consumption of network time synchronization.

Increasing electric vehicles (EV) charging efficiency and reducing charging cost are important issues of EVs power management and key factor that affect the spread of EVs. Researching the competitive relationship between power grids and EV users is the key to solving this problem. Therefore, the authors propose a charging control strategy that is based on a 1‐N‐type Stackelberg game. In the game, power grid, retailers, and users are all able to do power decision making. Thus, the charging strategy here can flexibly meet different demands of power grid, retailers, and users. The equilibrium of the game model is solved by the inverse induction method. The benefits are compared by simulation, by the disordered charging process, and by charging control methods based on static time‐sharing electricity prices. The influence of the parameters on the charging process in the game model is analysed, and the feasibility of the proposed method is verified. Results show that load peak is reduced by 34.9% while users' charging costs are reduced by 25.1%. The approaches they proposed can effectively solve the competition between the two.

Highly accurate activation calculations are the basis of radiation protection optimization design, and the accuracy of activation calculations depends on nuclear data. Based on the ENDF/B-VIII.0 and JEFF3.3 decay evaluation libraries, the influence of different nuclear data on the reactor structural material activation calculation is analyzed, and the nuclear data-related sensitivity and uncertainty quantification of radioactivity, decay heat and photon sources are carried out by the global sensitivity analysis method and the Latin Hypercube Sampling method. Based on uncertainty data in ENDF/B-VIII.0 and JEFF3.3, quantifying the uncertainty of radioactivity, decay heat and photon source in the shutdown dose calculation of reactor structural materials, the maximum uncertainty of radioactivity and decay heat reach 4.54 and 1.45%, respectively. The half-life and decay energy of 55Fe are the main sources of uncertainty.

This work is interested in verifying and analyzing the advanced neutronics lattice code KYLIN V2.0. Assembly calculations are an integral part of the two-step calculation for core design, and their accuracy directly affects the results of the core physics calculations. In this paper, we use the Doppler coefficient numerical benchmark problem and CPR1000 AFA-3G fuel assemblies to verify and analyze the advanced neutronics lattice code KYLIN V2.0 developed by the Nuclear Power Institute of China. The analysis results show that the Doppler coefficients calculated by KYLIN V2.0 are in good agreement with the results of other well-known nuclear engineering design software in the world; the power distributions of AFA-3G fuel assemblies are in good agreement with the results of the RMC calculations, it’s error distribution is in accordance with the normal distribution. It shows that KYLIN V2.0 has high calculation accuracy and meets the engineering design requirements.

BackgroundNuclear reactor structural material activation analysis is an important part of nuclear reactor radiation safety analysis. The traditional cell-based activation analysis method can not describe the non-uniform spatial activation effect inside the structural material cells, resulting in a large deviation between the activation source and the dose results, so it is necessary to carry out the high-resolution activation analysis method for nuclear reactor structural materials.PurposeThis study aims to develop a high-resolution activation analysis method for nuclear reactor structural materials based on mesh activation analysis, and establish an integrated mesh-based activation analysis code (MCFisMA).MethodsFirst of all, based on Rigorous 2-step method (R2S), Monte Carlo particle transport program MCNP and activation calculation program FISPACT, the mesh-based activation method was used to divide the structural material model into fine spatial mesh, and the activation calculation of each mesh was carried out in turn to realize the precise activation calculation analysis of nuclear reactor structural materials. Then, the MCFisMA was implemented by using the neutron transport method, material activation method and activation-induced dose coupled together. In addition, NUREG/CR-6115 PWR model was used to study the application of high-resolution activation analysis of structural materials.Results & ConclusionsThe correctness and reliability of the proposed method and MCFisMA program is verified with the ITER shutdown dose rate (SDR) benchmark. The method presented in this paper has theoretical significance for improving the calculation accuracy of the activation source term and decay gamma dose field of nuclear reactor structural materials. It provides theoretical and data support for reactor shielding design, overhaul and refueling plan and decommissioning strategy.

1.School of Electrical and Information Engineering, Anhui University of Science and Technology, Huainan 232001, China; 2.School of Electronic and Information Engineering, Suzhou University of Science and Technology, Suzhou 215009, China

In view of the problem that energy consumption of sensor nodes is fast and equipment life is cut caused by large amount of information transmission of wireless sensor network, a kind of compressed sensing image processing algorithm based on wavelet transform was put forward. The algorithm uses sym8 wavelet for image sparse and fragmental processing, and uses measurement matrix for adaptive sampling measurement, and finally reconstructs image through OMP algorithm and wavelet inverse transformation. The experimental results show that the proposed algorithm can obtain reconstructed image with high quality by lower sampling rate compared with traditional compressed sensing algorithm.

For problems that wireless transmission delay in tunnels of coal mine underground was random greatly and current time synchronization algorithms could not be applied directly in coal mine underground because of inability to determine delay distribution and parameters, an estimation method of transmission delay of time synchronization information for Internet of things was proposed based on passive measurement. A class of evaluation node is added to perception layer of Internet of things of coal mine underground for overhearing passively time synchronization packets among perception nodes to acquire transmission delay data. Then parameters under different distribution regularities are estimated by the evaluation nodes based on the maximum likelihood estimation method and optimal distribution regularity is determined according to Kullback-Leibler difference value. Parameter mutation is detected by using log-likelihood-ratio for determining whether the evaluation nodes send delay distribution regularity and parameters to sink nodes. The simulation results show that the method detects mutation of classification of delay distribution regularity and distribution parameters accurately.

For difficult transmission and storage of a large of video monitoring image data of coal mine underground, an image compression method was proposed which was based on improved compressive sending algorithm. Firstly, collected images data are spared by Coiflet2 wavelet transform principle. Secondly, the spared data is measured by measurement matrix to get measurement data. Finally, the images are refactored by orthogonal matching tracking algorithm and inverse transformation. The experimental results show that the method can effectively compress image and reconstruct the image.