Abstract

Numerical simulations of fluid flows can produce a huge amount of data and inadvertently important flow structures can be ignored, if a thorough analysis is not performed. The identification of these flow structures, mainly in transient situations, is a complex task, since such structures change in time and can move along the domain. With the decomposition of the entire data set into smaller sets, important structures present in the main flow and structures with periodic behaviour, like vortices, can be identified. Therefore, through the analysis of the frequency of each of these components and using a smaller number of components, we show that the Proper Orthogonal Decomposition can be used not only to reduce the amount of significant data, but also to obtain a better and global understanding of the flow (through the analysis of specific modes). In this work, the von Kármán vortex street is decomposed into a generator base and analysed through the Proper Orthogonal Decomposition for the 2D flow around a cylinder and the 2D flow around two cylinders with different radii. We consider a Newtonian fluid and two non-Newtonian power-law fluids, with n=0.7 and n=1.3. Grouping specific modes, a reconstruction is made, allowing the identification of complex structures that otherwise would be impossible to identify using simple post-processing of the fluid flow.

Highlights

  • To use the max Proper Orthogonal Decomposition (POD) method, we considered the data of every 20 time-steps, i.e., δt POD = 50 s, resulting into 400 time-steps to be analyzed

  • We considered the same δtCFD and δt POD of the flow around one cylinder

  • We performed a detailed study on the flow around a single and two distinct cylinders, by decomposing the von Kármán vortex street data into a generator base that was analysed through the Proper Orthogonal Decomposition

Read more

Summary

Introduction

Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations. The Proper Orthogonal Decomposition (POD) method was first introduced by Lumley in 1967 [1], and it allows for decomposing almost any flow into an infinite set of eigenfunctions or modes. The objective of the POD method is to reduce the model in a way that it can capture the most important and reliable information with much less data and effort. This method is famous in Computational Fluid Dynamics (CFD) because it reduces the simulation time and allows for predicting the fluid flow based only on the most important modes

Objectives
Methods
Results
Conclusion
Full Text
Published version (Free)

Talk to us

Join us for a 30 min session where you can share your feedback and ask us any queries you have

Schedule a call