Engine inlet guide vane ice impact fragmentation

Yiqiang Han, Jared Soltis, Jose Palacios

Research output: Contribution to journalArticlepeer-review

3 Scopus citations

Abstract

Experimental testing and analytical modeling of ice impact and fragmentation at turbo fan engine inlet were conducted in this research. A rotor testing facility, Adverse Environment Rotor Test Stand (AERTS) Laboratory, was introduced to reproduce natural icing condition and representative ice shapes typically found on engine inlet guide vanes. A total of 42 cases from 14 different icing conditions were conducted and used for high-speed visualization of ice impact tests and model development. Ice particles were analyzed using image postprocessing techniques and correlated using statistical tools. A cut-off in size range for analysis was found necessary before applying an energy-statistical method for model development. A cut-off criterion that related to the fracture stress, strain rate, and fracture toughness was implemented and compared with existing literature. After applying size cut-off, a Rosin-Rammler distribution was fitted to obtain mass-weighted particle distribution. Cumulative distribution function (CDF) was first formulated with comparison to an existing correlation based on hailstone impact experiments. The AERTS prediction successfully matched with experimental measurements, whereas the hailstone correlation constantly overestimate the size distribution. In addition, a probabilistic distribution function (PDF) was used to categorize the postimpact size distribution. The particle cloud was segmented into three groups weighted by mass (16%, 68%, and 16%). This technique provides a simple categorization method of a complicated impact fragmentation process. Instead of simulating the fracturing physics, a group of particles with three categories of particle sizes can be used as initial condition for postimpact aerothermal simulations that can significantly simplify computational fluid dynamics modeling complexity. Overall, a total of 6.1% mean absolute deviation in predicting the mean of particle sizes was achieved for total of 42 test cases in this study.

Original languageEnglish (US)
Pages (from-to)3680-3690
Number of pages11
JournalAIAA journal
Volume56
Issue number9
DOIs
StatePublished - 2018

All Science Journal Classification (ASJC) codes

  • Aerospace Engineering

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