Genetic algorithm for nuclear data evaluation applied to subcritical neutron multiplication inference benchmark experiments

The work presented in this paper focuses on the first 10 years (2011)(2012)(2013)(2014)(2015)(2016)(2017)(2018)(2019)(2020) of radiation test object (RTO) operations at the National Criticality Experiments Research Center. RTOs are subcritical configurations of special nuclear material that are built by hand. These configurations are utilized for benchmark experiments, detector testing/characterization, and training. An overview of the types of measurements used in RTO operations is given as well as a history of RTO operations at Los Alamos National Laboratory from 1944-2011.

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“…82) physics event generators, 83,84 and use of a genetic algorithm to recommend nuclear data changes for prompt neutron multiplicity. 85…”

Section: S86mentioning

confidence: 99%

A New Era of Nuclear Criticality Experiments: The First 10 Years of Radiation Test Object Operations at NCERC

Hutchinson

Bounds

Cutler

et al. 2021

Nuclear Science and Engineering

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“…Radially, the distance from the center of the reactor to the face of the detector enclosures was 55 cm as shown in Figure 2. The NoMAD systems have been used for many neutron multiplicity measurements; most of these experiments have taken place at the National Criticality Experiments Research Center (NCERC) [52,26,49,43,42]. The NoMAD was previously used for subcritical measurements at the RCF [25]; lessons learned from that measurement campaign were utilized in the design of this experiment.…”

Section: Supplementary Materials E: Experimental Setupmentioning

confidence: 99%

Patchy snapshots of nuclear chain reactions

Dumonteil¹,

Bahran

Cutler

et al. 2020

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Stochastic fluctuations of the neutron population within a nuclear reactor are typically prevented by operating the core at a sufficiently high power. This regime, where the evolution of the neutron density is essentially deterministic, is key for automatic protection and safety systems to safely detect unwanted power excursions during an accident, and to rapidly initiate a reactor trip procedure in case it is needed. Recent works, supported by numerical simulations, have however reported that, for large reactors, the branching nature of the fission reactions might induce strongly non-Poissonian patterns in the neutron spatial distribution, and that stochastic fluctuations might still persist at reactor powers close to operating conditions (startup phase). An international program conducted by LANL, IRSN and CEA was therefore setup to experimentally detect and characterize such fluctuations and correlations. An experiment took place in 2017 at the Reactor Critical Facility (RCF) of the Rensselaer Polytechnic Institute (USA). In this paper we will report the main findings of this experimental program, supported by stochastic models and by the development of a dedicated high-fidelity Monte Carlo simulation code. We will in particular describe and explain the strong patchiness in neutron power distributions measured at the RCF, as well as a peculiar 'blinking' behavior of the core, and discuss the consequences of these findings on nuclear safety.

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“…Darwinian evolution and survival of the fittest are the core concept of GA. The work flow of GA consists of three steps fitness, survival, and reproduction in an optimized manner [ 17 ]. The process of GA is detailed in the Figure 6 .…”

Section: Introductionmentioning

confidence: 99%

MWCSGA—Multi Weight Chicken Swarm Based Genetic Algorithm for Energy Efficient Clustered Wireless Sensor Network

Ajmi

Helali

Lorenz

et al. 2021

Sensors

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Nowadays due to smart environment creation there is a rapid growth in wireless sensor network (WSN) technology real time applications. The most critical resource in in WSN is battery power. One of the familiar methods which mainly concentrate in increasing the power factor in WSN is clustering. In this research work, a novel concept for clustering is introduced which is multi weight chicken swarm based genetic algorithm for energy efficient clustering (MWCSGA). It mainly consists of six sections. They are system model, chicken swarm optimization, genetic algorithm, CCSO-GA cluster head selection, multi weight clustering model, inter cluster, and intra cluster communication. In the performance evaluation the proposed model is compared with few earlier methods such as Genetic Algorithm-Based Energy-Efficient Adaptive Clustering Protocol For Wireless Sensor Networks (GA-LEACH), Low energy adaptive Clustering hierarchy approach for WSN (MW-LEACH) and Chicken Swarm Optimization based Genetic Algorithm (CSOGA). During the comparison it is proved that our proposed method performed well in terms of energy efficiency, end to end delay, packet drop, packet delivery ratio and network throughput.

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Genetic algorithm for nuclear data evaluation applied to subcritical neutron multiplication inference benchmark experiments

Cited by 19 publications

References 13 publications

A New Era of Nuclear Criticality Experiments: The First 10 Years of Radiation Test Object Operations at NCERC

A New Era of Nuclear Criticality Experiments: The First 10 Years of Radiation Test Object Operations at NCERC

Patchy snapshots of nuclear chain reactions

MWCSGA—Multi Weight Chicken Swarm Based Genetic Algorithm for Energy Efficient Clustered Wireless Sensor Network

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