Industrial systems
With Prof. Greg Rieker at CU, we have performed joint experimental and computational research on catalytic and ribbon burners used for industrial processing of polymer films on chilled rollers. On the computational side, we have developed a simulation testbed in OpenFOAM for the study of temperature, flow fields, and heat transfer processes (i.e., radiation and convection) above the burner and in the burner/roller interaction region. The simulation tool has been used to examine a wide range of conditions, including different roller speeds, burner/roller spacings, burner orientations, and power fluxes. Burners operating in isolation and as part of a larger array have also been studied. Simulations have provided 3D temperature, flow, and heat transfer fields for each of these cases, revealing several useful design heuristics, including the advantage of a down-firing burner configuration, the negative impacts of convective heat transfer on temperature uniformity, and the advantageous effects on uniformity of flow-blocking by the roller.
Approximate Bayesian computation (ABC) is a data-driven technique that uses many low-cost numerical simulations to estimate unknown physical or model parameters (e.g., boundary conditions and material properties), as well as their uncertainties, given reference data from real-world experiments or higher-fidelity numerical simulations. We have used ABC to estimate unknown parameters in simulations of complex thermal-fluid flows, and the technique has been demonstrated for the estimation of unknown boundary conditions in experiments of high temperature burners used for polymer film flame treatments. These tests show that ABC provides accurate and reasonably certain estimates of inflow parameters even when the model simulations imperfectly represent the physics underlying the reference experimental data. We also demonstrate the use of ABC to estimate unknown model parameters in large eddy simulations and Reynolds averaged Navier-Stokes simulations of turbulent flows. ABC is thus shown to be a versatile technique for estimating unknown physical and model parameters, resulting in substantial improvements in simulation accuracy.
Sam Simons-Wellin
PhD student
Sam’s research to date has been concerned with the design optimization and multiphysics computational model development of industrial scale materials processing systems in collaboration with 3M with the goal of studying material throughput, quality, system efficiency, process, and direct emissions. He further studies the mitigation of environmentally and societelly harmful combustion systems through the development of field-scale-deployable, accurate physical models that interface with advanced flow measurement diagnostics. Models developed to date have examined multifidelity approaches to capturing and describing molecular diffusion and mixing phenomena in fire scenarios as well as methane emission, destruction, and dispersion in polluting flaring systems. Sam incorporates modal decompositions and reduced order flow models that capture spatial and temporal dynamics from chemically and physically detailed simulations, and can be used in a computationally efficient framework to model large scale systems at industrial and weather scales.
Caelan Lapointe
PostDoctoral Associate
Caelan’s research is motivated by efficient simulation and optimization of complex fire phenomena with a focus on industrial and environmental applications.
Peter Hamlington
Associate Professor
Peter is an associate professor in the Paul M. Rady Department of Mechanical Engineering at the University of Colorado Boulder and the principal investigator of the Turbulence and Energy Systems Laboratory.