Parameter Estimation for a Turbulent Buoyant Jet Using Approximate Bayesian Computation


Approximate Bayesian Computation (ABC) is a powerful tool that allows sparse experimental or other “truth” data to be used for the prediction of unknown model parameters in numerical simulations of real-world engineering systems. In this presentation, I first introduce the ABC approach. I then use ABC to predict unknown inflow conditions in simulations of a two-dimensional (2D) turbulent, high-temperature buoyant jet. For this test case, “truth” data are obtained from a simulation with known boundary conditions and problem parameters. Using spatially- sparse temperature statistics from the 2D buoyant jet “truth” simulation, I show that the ABC method provides accurate predictions of the “true” jet inflow temperature. The success of the ABC approach in the present test suggests that ABC is a useful and versatile tool for engineering fluid dynamics research.

Sep 13, 2016 3:30 PM — 4:30 PM
Bechtel Collaboratory, Discovery Learning Center
Engineering Center, University of Colorado at Boulder, Boulder, CO 80309

Department of Mechanical Engineering, University of Colorado Boulder