Hi @Vishashalu,
In your illustration, are you saying the model is a function of X1 and X2 and there is a deterministic formula that relates X1 to X2? If so, then why would you consider X1 as an input to your model? But if you did, what kind of marginals you assign to that variable in UQLab?
Coming back to your example, you have an uncertain input that is used to compute the viscosity and another to compute the density, then yes, both are independent. And no, how they are used inside your model is (say, to compute a third property, which is not your inputs in any case) is not what we refer to as being correlated. These two parameters might, based on sensitivity analysis, be (either strongly or weakly) interacting.
While dependent and correlated are often used interchangeably to describe the probabilistic inputs in the context of sensitivity analysis, the notion of interaction is something else. You can think of it like this: being correlated (or dependent) is solely a property of your inputs, while interaction is also a property of your model. If two inputs are interacting, then the effect of one input to the model output depends on the other input. Even if the inputs are independent, they might be interacting depending on the computational model.
Indeed it can be daunting with all these terms, but I would like to encourage you to read some introductory materials on the topic; I find the book by Saltelli et al. is a good starting point (at least Chapter 1). Also perhaps before delving into your actual computational model, it might be a good idea to play around with the sensitivity examples shipped with UQLab (especially the ones related to Sobol’).
Coming back to your original problem, how did you model your inputs? Maybe you can attach that part of your code here just for me to check?
And regarding the last point, yes, please open a new topic as I think it’s already a different topic 
.
Hope this makes things clearer for you.
PS:
Thanks a lot for reformatting your post!
