Monte Carlo Application ToolKit (MCATK)

Adams, Terry R.; Nolen, Steven D.; Sweezy, Jeremy; Zukaitis, Anthony J.; Campbell, Joann M.; Goorley, Tim; Greene, Simon; Aulwes, Rob

doi:10.1051/snamc/201406009

Cited by 6 publications

(4 citation statements)

References 18 publications

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“…is method can be regarded as an improvement of the definite rule. Monte Carlo method [31][32][33][34] is a numerical simulation based on probability theory and mathematical statistics, which can solve many problems difficult to traditional methods. With the development of modern computation technology, the statistical simulation method has become an effective tool to solve or study high-dimensional complex problems.…”

Section: Modeling Based On Monte Carlo Samplingmentioning

confidence: 99%

Trajectory Planning of UAVs with Fault Tolerance Based on Monte Carlo Sampling

Chen

Zhou

et al. 2022

Mathematical Problems in Engineering

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Due to the limitation of system positioning accuracy, it is necessary to correct an unmanned aerial vehicle’s (UAV) error by some correct points arranged in advance, in order to successfully accomplish their tasks. Planning the optimal trajectory of UAVs considering error correction with fault tolerance is a great challenge that almost has not been broken out because corrections may fail by these points. To address the issue, this paper proposes two trajectory planning methods based on “the definite rule” and Monte Carlo sampling. By constructing five calculation models of the two methods in detail, designing the algorithms appropriately, and developing computational programs based on MATLAB, the results of trajectory planning of UAVs from a practical problem are obtained. The results achieve the optimization objectives that the total trajectory length is as short as possible and that the correction points UAV passes are as few as possible, and meanwhile, the optimal trajectory satisfies all the constraints, which illustrate the feasibility and reasonability of trajectory planning based on the definite rule and Monte Carlo sampling. The comparable results show that computation time is 104 and 195 times that of this paper when the Dijkstra algorithm with ant colony algorithm and the greedy algorithm with tabu search algorithm are respectively used, which illustrate the high efficiency of the proposed method. This work provides a feasible solution for UAVs’ trajectory planning that considers the error corrections and failure probability on certain correction points and achieves trajectory planning of UAVs with fault tolerance.

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Section: Modeling Based On Monte Carlo Samplingmentioning

confidence: 99%

Trajectory Planning of UAVs with Fault Tolerance Based on Monte Carlo Sampling

Chen

Zhou

et al. 2022

Mathematical Problems in Engineering

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“…Recent multi-physics modeling efforts 27,28 have revisted the Lady Godiva reactor accident excursions. This work utilized the Monte Carlo Application ToolKit (MCATK) 29,30,31 to perform the neutron transport calculations for the simulations.…”

Section: History Of Criticality Excursion Studies At Los Alamos Using...mentioning

confidence: 99%

Critical Assemblies: Dragon Burst Assembly and Solution Assemblies

et al. 2021

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This work reviewed the historical literature associated with the Dragon experiment and Water Boiler reactors operated at Los Alamos during the Manhattan Project. Frisch's invited talk given at the Fast Burst Reactor Conference held the University of New Mexico in Albuquerque, NM in 1969 is quoted. From the literature review, basic models for the Dragon experiment and for a Water Boiler type assembly (aqueous homogeneous reactor) were created that can be used for conducting multi-physics simulations for criticality excursion studies. This methodology utilizes the coupled neutronic-hydrodynamic method to perform a time-dependent dynamic simulation of a criticality excursion. MCNP® was utilized to calculate important nuclear kinetic parameters that were incorporated into the models. Simulation results compared reasonably well with historic data.

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“…When all particles already hit the time boundary, the time census is completed, and PCT can be performed on the censused particles. More recent applications of PCT include the use of random particle duplication or deletion [4] in Serpent 2 [13], splitting and Russian-Roulette technique [5] in MCATK [14], particle combing technique [15] in TRIPOLI-4 [16,17] and GUARDYAN [9], and a modified combing technique which is most recently introduced in [1].…”

Section: Introductionmentioning

confidence: 99%

Analysis of Population Control Techniques for Time-Dependent and Eigenvalue Monte Carlo Neutron Transport Calculations

Ilham¹,

McClarren²

2022

Preprint

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A comprehensive study on population control technique (PCT) for time-dependent and eigenvalue Monte Carlo (MC) neutron transport simulations is presented. We define PCT as a technique that takes a censused population and returns a controlled, unbiased one. A new perspective based on an abstraction of particle census and population control is explored, paving the way to improved understanding and application of the concepts. Five distinct PCTs identified from the literature are reviewed: Simple Sampling (SS), Splitting-Roulette (SR), Combing (CO), modified Combing (COX), and Duplicate-Discard (DD). A theoretical analysis of how much uncertainty is introduced to a population by each PCT is presented. Parallel algorithms for the PCTs applicable for both time-dependent and eigenvalue MC simulations are proposed. The relative performances of the PCTs based on runtime and tally mean error or standard deviation are assessed by solving time-dependent and eigenvalue test problems. It is found that SR and CO are equally the most performant techniques, closely followed by DD.

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Monte Carlo Application ToolKit (MCATK)

Cited by 6 publications

References 18 publications

Trajectory Planning of UAVs with Fault Tolerance Based on Monte Carlo Sampling

Trajectory Planning of UAVs with Fault Tolerance Based on Monte Carlo Sampling

Critical Assemblies: Dragon Burst Assembly and Solution Assemblies

Analysis of Population Control Techniques for Time-Dependent and Eigenvalue Monte Carlo Neutron Transport Calculations

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