Computational Fluid Dynamics (CFD) simulations are subject to a variety of uncertainties such as initial conditions, boundary conditions, and choice of model form and parameter values. These uncertainties contribute to CFD results disagreeing with test data. Direct sampling of a CFD model can be used to employ various statistical techniques to address these uncertainties; however, typically the relatively long run time for a CFD simulation makes this infeasible. The solution is to take a surrogate modeling approach where first a machine learning (ML) model is trained to predict the CFD model’s results. The ML model is then used in place of the CFD model to run the desired analyses. The ML model’s rapid prediction of CFD results allows more inputs, scenarios, and design possibilities to be investigated and in less time.
SmartUQ is a fast, accurate, and comprehensive ML and Uncertainty Quantification (UQ) software tool optimally designed for simulation, digital twin, and other engineering applications.
Join us for this webinar in which SmartUQ principal application engineer, Gavin Jones, will introduce the use of SmartUQ for CFD applications. The following unique strengths and capabilities of SmartUQ for CFD applications will be highlighted:
