amrPOD
Adaptive mesh refinement (AMR) plays a fundamental role in simulating particular flow phenomena that have vastly different resolution requirements throughout the computational domain. Proper orthogonal decomposition (POD), on the other hand, serves as a popular tool to extract coherent structures from the fluid data and build reduced order models. We present a new method to perform POD on AMR data sets that eliminates repeated operations that arise from using nearest neighbor interpolation of the data onto a uniform grid before performing POD. More fundamentally, we believe that this is the first algorithm to eliminate redundant operations for matrix multiplications with repeated values in each matrix.
We provide all code here for amrPOD to evaluate the efficiency of the algorithm as shown in the paper. Specifically, we stress the algorithm using synthetically generated AMR data to identify where the new algorithm out performs standard matrix operations since the new algorithm requires additional overhead of checking the grid level at various locations. Additionally, we show with genuine AMR data of an axisymmetric buoyant jet and a compiled and optimized version of the code, our algorithm reduces the CPU time. Details of how to use the code are provided in the README.md on github.
Michael Meehan
Postdoctoral Research Associate
Mike is a former research associate in the Paul M. Rady Department of Mechanical Engineering at the University of Colorado Boulder and also a former student in the Turbulence and Energy Systems Laboratory, earned his PhD in May 2022.
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.
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.