Retraction Note to: Influence of coastal beach stability based on stochastic simulation method and English translation of import product vocabulary

Stochastic simulation is widely applied for estimating the design flood of various hydrosystems. The design flood at a reservoir site should consider the impact of upstream reservoirs, along with any development of hydropower. This paper investigates and applies a stochastic simulation approach for determining the design flood of a complex cascade of reservoirs in the Longtan watershed, southern China. The magnitude of the design flood when the impact of the upstream reservoirs is considered is less than that without considering them. In particular, the stochastic simulation model takes into account both systematic and historical flood records. As the reliability of the frequency analysis increases with more representative samples, it is desirable to incorporate historical flood records, if available, into the stochastic simulation model. This study shows that the design values from the stochastic simulation method with historical flood records are higher than those without historical flood records. The paper demonstrates the advantages of adopting a stochastic flow simulation approach to address design-flood-related issues for a complex cascade reservoir system.

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Stochastic Simulation Method for Reasoning of Dynamic Uncertain Causality Graph (DUCG)

10.1115/1.0000415v ◽

2021 ◽

Author(s):

Hao Nie ◽

Qin Zhang

Keyword(s):

Stochastic Simulation ◽

Simulation Method ◽

Dynamic Uncertain Causality Graph

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A probabilistic evaluation method of energy-saving benefit for daily generation based on the Monte Carlo stochastic simulation method and wind power and load uncertainties

Advances in Energy and Environment Research ◽

10.1201/9781315212876-59 ◽

2017 ◽

pp. 315-322

Author(s):

Lin Guo ◽

Junmei Wang ◽

Chao Ma ◽

Chuncheng Gao ◽

Dunnan Liu ◽

...

Keyword(s):

Monte Carlo ◽

Energy Saving ◽

Wind Power ◽

Stochastic Simulation ◽

Evaluation Method ◽

Simulation Method ◽

Probabilistic Evaluation

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Numerical studies of higher order tau-leaping methods

10.32920/ryerson.14653782.v1 ◽

2021 ◽

Author(s):

Anuj Dhoj Thapa

Keyword(s):

Stochastic Simulation ◽

Computational Cost ◽

Practical Interest ◽

Stochastic Simulation Algorithm ◽

Simulation Method ◽

Equation Model ◽

Fast Reactions ◽

Biochemical Systems ◽

Computationally Intensive ◽

Reacting Systems

Gillespie's algorithm, also known as the Stochastic Simulation Algorithm (SSA), is an exact simulation method for the Chemical Master Equation model of well-stirred biochemical systems. However, this method is computationally intensive when some fast reactions are present in the system. The tau-leap scheme developed by Gillespie can speed up the stochastic simulation of these biochemically reacting systems with negligible loss in accuracy. A number of tau-leaping methods were proposed, including the explicit tau-leaping and the implicit tau-leaping strategies. Nonetheless, these schemes have low order of accuracy. In this thesis, we investigate tau-leap strategies which achieve high accuracy at reduced computational cost. These strategies are tested on several biochemical systems of practical interest.

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Estimating Weights of Evaluation Factors on Basis of Monte Carlo Simulation

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.268-270.1735 ◽

2012 ◽

Vol 268-270 ◽

pp. 1735-1740

Author(s):

Yan Fei Tian ◽

Li Wen Huang

Keyword(s):

Monte Carlo Simulation ◽

Monte Carlo ◽

Random Process ◽

Stochastic Simulation ◽

Research Work ◽

Simulation Method ◽

Distribution Functions ◽

Probability Process ◽

Evaluation Work

Although the value of factor weight in an evaluation work is deterministic, the solving process is random, so connection between weight solution with digital characteristics or distribution functions of specific random variables or random process could be build. Using stochastic simulation method to get a lot of random solutions to the problem, expectation of the random solutions can be used as a estimation solution. On basis of idea of Monte Carlo simulation, this paper analyzed the probability process of calculating factor weight, and provided the procedures of estimating factor weight by means of Monte Carlo simulation. Through discussion and example in this paper, feasibility and validity of this method were proved, which may make foreshadowing for follow-up research work.

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