An effective, robust simulation must account for potential sources of uncertainty. Computational fluid dynamics (CFD), in particular, has to deal with many uncertainties from various sources. The real world, after all, forces many kinds of uncertainties upon engineering components – everything from changes in numerical and computational parameters to uncertainty in initial and boundary conditions and geometry. No matter how expensive a flow simulation is, the uncertainties have to be assessed. In CFD, uncertainty is inevitable. But it presents us with a question: how do you know which uncertainties to expect and quantify without using an enormous amount of computing power?
UQit – an open-source Python package – is the answer. It has been developed specially for CFD; a dependable, flexible and cost-effective package for testing various flow simulations (and even experiments). It does this by propagating uncertainty into the inputs and parameters themselves, making their eventual outcomes much more illuminating. With UQit, you will assess a significant amount of reliable data while addressing the sensitivity of the variables at play. You will not only learn what influences your model, but how much influence a variable has. CFD therefore becomes far more reliable with respect to real-world conditions.
However, certain frameworks are required for large flow simulations. UQit techniques are all interrelated but demand attention at particular stages of the process to overcome simulation cost limitations, such as those for wall-bounded turbulent flows. With the right frameworks, you’ll set the appropriate parameters, measure uncertainties, evaluate sensitivity and change conditions easily for future tests. This is especially important when we consider that UQit itself will keep developing to stay tuned with the state-of-the-art techniques. Your organization must be ready to maximize UQit’s potential for increasingly accurate models, saving more time, cost, risk and questions as engineering projects come together.
Here, we will discuss UQit in detail, breaking down its transformative role within uncertainty quantification (UQ). You will learn about its benefits, functionality and application. Then we’ll examine the frameworks necessary for UQit to deliver the results you want to see. This information will be illustrated visually, as well with a case study (see the in-situ analysis). Finally, we’ll summarize and offer guidance for getting started with UQit, raising your potential in computational physics.