#Matlab latin hypercube sampling code#

Meanwhile, to improve the efficiency of numerical analysis, MATLAB is employed in the secondary development for ABAQUS. To understand the influence of randomness on micro-mechanical state at tip of EAC, Latin hypercube sampling method is applied to analyze the uncertainty of stress-strain in the oxide film at the EAC tip considering the uncertainties of load and material properties of base metal and oxide film. The calculations are performed in MATLAB environment.Finally, the obtained results are presented and discussed.It is shown that the Latin Hypercube sampling can be used as an effective tool for the investigation of the effect of fire model parameter variability on the fire resistance.Ībstract: Environmentally assisted cracking (EAC) of nickel-based alloys is one of the most significant potential safety hazards in the primary circuit of nuclear power plants. In the example, the variability of the fire model parameters is captured using the Latin Hypercubes sampling method.The thermal analysis of the slab panel as well as the subsequent mechanical analysis are both conducted by using numerical methods described in our previous work. The panel is placed in a fire compartment with given fire model parameter ranges. Namely, we focus on the temperature evolution, duration of fire, andthe maximum temperature reached.In the second part of the paper, an illustrative example is presented.The example is focused on the analysis of the fire resistance of a concrete slab panel.

#Matlab latin hypercube sampling code#

In your case you already have your distribution z in the code above and you also have mu, sigma and 'n' (the size of your distribution), just replace them and you should be able to create your Latin Hypercube.Abstract: The paper is focused on the fire model parameter variability and its effect on the determination of fire resistance of concrete structural members.For the modelling of fire, the parametric temperature-time curve given in EN is used.First part of the paper is aimed on the fire model parameter variability in general.First, fire model parameter ranges are described and their combinations are created using two common sampling methods - Monte Carlo and Latin Hypercubes.Then, the combinations are analysed, unreasonable combinations are identified, and viable combinations are illustrated.Moreover, the characteristics of the temperature-time curves obtained using the parameter combinations are discussed. % Similar to tiedrank, but no adjustment for ties here % maintaining the ranks (and therefore rank correlations) from the % Transform each column back to the desired marginal distribution, % Get gridded or smoothed-out values on the unit interval % correlation structure - in this case multivariate normal % Generate a random sample with a specified distribution and % Copyright 1993-2010 The MathWorks, Inc. % sample before the marginals are adjusted to obtain X. % =LHSNORM(.) also returns Z, the original multivariate normal

% 0.5/N we use a value having a uniform distribution on the % If 'ONOFF' is 'on' (the default), each column has points uniformly % normal cumulative distribution for that column''s marginal distribution. In other words, each column is a permutation If 'ONOFF' is 'off', each column has points equally spaced % X=LHSNORM(MU,SIGMA,N,'ONOFF') controls the amount of smoothing in the % is close to its theoretical normal distribution. % of each column is adjusted so that its sample marginal distribution % the multivariate normal distribution, but the marginal distribution % N from the multivariate normal distribution with mean vector MU % X=LHSNORM(MU,SIGMA,N) generates a latin hypercube sample X of size %LHSNORM Generate a latin hypercube sample with a normal distribution In matlab : edit lhsnorm : function = lhsnorm(mu,sigma,n,dosmooth)

An edit of the lhsnorm function can probably answer your question.