scholarly journals RELIABLITY OF CURVED TIMBER BEAM EXPOSED TO FIRE

2016 ◽  
Author(s):  
Robert Pečenko ◽  
Tomaž Hozjan ◽  
Goran Turk
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Calculation Method ◽  
Failure Time ◽  
Latin Hypercube Sampling ◽  
Simulation Method ◽  
Mechanical Analysis ◽  
Monte Carlo Simulation Method ◽  
Timber Beam ◽  
Safety Requirement

<p>In this paper the performance based approach to determine the reliability of curved timber beam during the fire is presented. The reliability is preformed with Monte Carlo simulation method, where, in order to reduce the number of simulations, Latin hypercube sampling is applied. The uncertainties are implemented in the advanced calculation method, as randomly generated parameters for both thermal and mechanical analysis. At the end of the paper, the distribution of the mid-span displacement at 30 min, 45 min and at failure time is presented. In addition, failure time that satisfies safety requirement from the Eurocode (<em>β</em> &gt; 3.8) is determined as well.</p>

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

Energies ◽  
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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