Parametric analysis editing and optimization software
BOSS quattro
SAMTECH
BOSS quattro is our application to manage and take advantage of parametric models. BOSS Quattro features several engines, each of them having a specific purpose:
* Parametric analyses is the simplest way to evaluate the influence of given parameters on the behaviour of your structure. It just launches analyses for each set of parameters values defined by users. Results can then be plotted as a function of the parameters.
* Design of experiments has the same purpose as the parametric analyses. The main difference is that the successive values of the parameters are cleverly chosen using specific algorithms, in order to properly cover the design space while minimizing the amount of analyses.
Both methods can be used to build response surfaces. This is a way to interpolate the results obtained by running analyses and it ends up with a approximate description of the results over the complete design space. These approximations provide fast analysis tools. They can be used for rapid sizing studies in the early design stages.
* Multidisciplinary optimization is the way to compute the optimal set of parameters according to the criteria you define. BOSS quattro features several family of optimization algorithms:
o Gradient based methods (GCM, Conlin, SQP...)
* Sensitivity analysis is used in the gradient based optimization methods but it is also meaningful when applied out of an iterative optimization process.
* Statistic analyses (Monte Carlo) are used to run parametric studies with the sets of parameters built using statistical distributions (Gauss for example).
* Parametric analyses is the simplest way to evaluate the influence of given parameters on the behaviour of your structure. It just launches analyses for each set of parameters values defined by users. Results can then be plotted as a function of the parameters.
* Design of experiments has the same purpose as the parametric analyses. The main difference is that the successive values of the parameters are cleverly chosen using specific algorithms, in order to properly cover the design space while minimizing the amount of analyses.
Both methods can be used to build response surfaces. This is a way to interpolate the results obtained by running analyses and it ends up with a approximate description of the results over the complete design space. These approximations provide fast analysis tools. They can be used for rapid sizing studies in the early design stages.
* Multidisciplinary optimization is the way to compute the optimal set of parameters according to the criteria you define. BOSS quattro features several family of optimization algorithms:
o Gradient based methods (GCM, Conlin, SQP...)
* Sensitivity analysis is used in the gradient based optimization methods but it is also meaningful when applied out of an iterative optimization process.
* Statistic analyses (Monte Carlo) are used to run parametric studies with the sets of parameters built using statistical distributions (Gauss for example).
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