Woodruff School of Mechanical Engineering

Woodruff School Graduate Women (WSGW) Tech Talks

Title:

Predicting Cavitation Erosion Propensity and Severity in Fuel Injection Systems

Speaker:

Dr. Gina Magnotti

Affiliation:

Argonne National Laboratory

When:

Friday, November 16, 2018 at 11:00:00 AM

Where:

MRDC Building, Room 4211

Host:

Boni Yraguen
byraguen3@gatech.edu

Abstract

Although there have been extensive investigations characterizing cavitation phenomenon in fuel injectors, much is still unknown about the mechanisms driving cavitation-induced erosion, and how these complicated physics should be represented in a model. In lieu of computationally expensive fluid-structure interaction modeling, the Eulerian mixture modeling approach has been accepted as an efficient means of capturing cavitation phenomena. However, there remains a need to link the erosive potential of cloud collapse events with the subsequent material deformation and damage of neighboring surfaces. Even though several cavitation erosion indices have been proposed in the literature, no single metric has been identified as universally applicable across all injector-relevant conditions. The objective of this work is to identify computational metrics that can characterize the erosive potential of cavitation within injector orifices. While a commonly employed cavitation erosion metric, namely the maximum local pressure, was found to provide indications of potential sites for pitting and material rupture from single impact events, no additional information could be determined regarding the material erosion process. To improve representation of the incubation period, a new metric was derived based on the cumulative energy absorbed by the solid material from repeated hydrodynamic impacts. Large eddy simulations for turbulent cavitating flow through channel geometries were performed, where the multiphase and multi-component flow was represented using a homogeneous mixture modeling approach. Through comparison against available experimental data, the stored energy metric was found to accurately predict the influence of flow conditions on the incubation period before material erosion. Additionally, detailed analysis of cavitation cloud collapse events highlighted the strong correlation between cloud collapse mechanisms and their erosive potential.


Biography

Dr. Gina Magnotti received her Ph.D. from the Woodruff School of Mechanical Engineering and is now a Postdoctoral Appointee at Argonne National Laboratory.