0000000000299647
AUTHOR
Matthieu Petelet
Latin hypercube sampling with inequality constraints
International audience; In some studies requiring predictive and CPU-time consuming numerical models, the sampling design of the model input variables has to be chosen with caution. For this purpose, Latin hypercube sampling has a long history and has shown its robustness capabilities. In this paper we propose and discuss a new algorithm to build a Latin hypercube sample (LHS) taking into account inequality constraints between the sampled variables. This technique, called constrained Latin hypercube sampling (cLHS), consists in doing permutations on an initial LHS to honor the desired monotonic constraints. The relevance of this approach is shown on a real example concerning the numerical w…
Global sensitivity analysis in welding simulations -- what are the material data you really need ?
In this paper, the sensitivity analysis methodology is applied to numerical welding simulation in order to rank the importance of input variables on the outputs of the code like distorsions or residual stresses. The numerical welding simulation uses the finite element method, with a thermal computation followed by a mechanical one. Classically, a local sensitivity analysis is performed, hence the validity of the results is limited to the neighbourhood of a nominal point, and cross effects cannot be detected. This study implements a global sensitivity analysis which allows to screen the whole material space of the steel family mechanical properties. A set of inputs of the mechanical model-ma…