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.