A Monte Carlo simulation method for non-random vibration analysis

This paper is devoted to the random vibration analysis of jacket platforms under wave loads using the explicit time-domain approach. The Morison equation is first used to obtain the nonlinear random wave loads, which are discretized into random loading vectors at a series of time instants. The Newmark-β integration scheme is then employed to construct the explicit expressions for dynamic responses of jacket platforms in terms of the random vectors at different time instants. On this basis, Monte Carlo simulation can further be conducted at high efficiency, which not only provides the statistical moments of the random responses, but also gives the mean peak values of responses. Compared with the traditional power spectrum method, nonlinear wave loads can be readily taken into consideration in the present approach rather than using the equivalent linearized Morison equation. Compared with the traditional Monte Carlo simulation, the response statistics can be obtained through the direct use of the explicit expressions of dynamic responses rather than repeatedly solving the equation of motion. An engineering example is analyzed to illustrate the accuracy and efficiency of the present approach.

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Efficient Random Vibration Analysis Using Markov Chain Monte Carlo Simulation

SAE International Journal of Materials and Manufacturing ◽

10.4271/2012-01-0067 ◽

2012 ◽

Vol 5 (1) ◽

pp. 77-86 ◽

Cited By ~ 2

Author(s):

Mahdi Norouzi ◽

Efstratios Nikolaidis

Keyword(s):

Monte Carlo Simulation ◽

Monte Carlo ◽

Markov Chain ◽

Markov Chain Monte Carlo ◽

Vibration Analysis ◽

Random Vibration ◽

Random Vibration Analysis

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Uncertainty Cost Functions in Climate-Dependent Controllable Loads in Commercial Environments

Energies ◽

10.3390/en14102885 ◽

2021 ◽

Vol 14 (10) ◽

pp. 2885

Author(s):

Daniel Losada ◽

Ameena Al-Sumaiti ◽

Sergio Rivera

Keyword(s):

Monte Carlo Simulation ◽

Monte Carlo ◽

Simulation Method ◽

Electricity Sector ◽

Cost Functions ◽

Density Estimator ◽

Monte Carlo Simulation Method ◽

Commercial Building ◽

Complementary Method ◽

The Cost

This article presents the development, simulation and validation of the uncertainty cost functions for a commercial building with climate-dependent controllable loads, located in Florida, USA. For its development, statistical data on the energy consumption of the building in 2016 were used, along with the deployment of kernel density estimator to characterize its probabilistic behavior. For validation of the uncertainty cost functions, the Monte-Carlo simulation method was used to make comparisons between the analytical results and the results obtained by the method. The cost functions found differential errors of less than 1%, compared to the Monte-Carlo simulation method. With this, there is an analytical approach to the uncertainty costs of the building that can be used in the development of optimal energy dispatches, as well as a complementary method for the probabilistic characterization of the stochastic behavior of agents in the electricity sector.

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