![]() To make the effect of input uncertainties on calculated accident risk more explicit, distributions and limits were determined for two variables which had approximately proportional effects on calculated doses: Pasquill Category probability (PSPROB) and link population density (LPOPD). These five or six variables were selected as candidates for Latin Hypercube Sampling applications. It was determined that, of the approximately two dozen RADTRAN 4 input more » parameters pertinent to accident analysis, only a subset of five or six has significant influence on typical analyses or is subject to random uncertainties. Therefore, a study of sensitivity of accident risk results to variation of input parameters was performed using representative routes, isotopic inventories, and packagings. However, the necessary linearity is not characteristic of the equations used in calculation of accident dose risk, making a similar tabulation of sensitivity for RADTRAN 4 accident analysis impossible. The sensitivity of calculated dose estimates to various RADTRAN 4 inputs is an available output for incident-free analysis because the defining equations are linear and sensitivity to each variable can be calculated in closed mathematical form. If this is done repeatedly, with many input samples drawn, one can build up a distribution of the output as well as examine correlations between input and output variables. Many simulation codes have input parameters that are uncertain and can be specified by a distribution, To perform uncertainty analysis and sensitivity analysis, random values are drawn from more » the input parameter distributions, and the simulation is run with these values to obtain output values. In some cases, the pairing is restricted to obtain specified correlations amongst the input variables. A sample is selected at random with respect to the probability density in each interval, If multiple variables are sampled simultaneously, then values obtained for each are paired in a random manner with the n values of the other variables. In LHS, the range of each variable is divided into non-overlapping intervals on the basis of equal probability. LHS is a constrained Monte Carlo sampling scheme. LHS UNIX Library/Standalone uses the Latin Hypercube Sampling method (LHS) to generate samples. The LHS samples can be generated either as a callable library (e.g., from within the DAKOTA software framework) or as a standalone capability. Multiple distributions can be sampled simultaneously, with user-specified correlations amongst the input distributions, LHS UNIX Library/ Standalone provides a way to generate multi-variate samples. It performs the sampling by a stratified sampling method called Latin Hypercube Sampling (LHS). To control this setting, click the Advanced Properties button in the Monte Carlo tab.The LHS UNIX Library/Standalone software provides the capability to draw random samples from over 30 distribution types. LHS is available in the Professional edition of the add-in. For complex models with many random variables, this means you can generate results in less time. ![]() In practice, this can be used to generate “better” simulation results, with lower standard error levels, with fewer trials. For two samples, it will divide the sample space in two, and generate one sample from each side. LHS will always return one sample less than 0 and one sample greater than 0. Although the probability of being positive or negative is equal, a true random number generator might return two samples less than 0, or two samples greater than 0. Latin Hypercube Sampling (LHS) is a method of sampling random numbers that attempts to distribute samples evenly over the sample space.Ī simple example: imagine you are generating exactly two samples from a normal distribution, with a mean of 0.
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