Modeling of a Turbofan Engine With Ice Crystal Ingestion in the NASA Propulsion System Laboratory

Abstract

The main focus of this study is to apply a computational tool for the flow analysis of the turbine engine that has been tested with ice crystal ingestion in the Propulsion Systems Laboratory (PSL) at NASA Glenn Research Center. The PSL has been used to test a highly instrumented Honeywell ALF502R-5 (LF11) turbofan engine at simulated altitude operating conditions. Test data analysis with an engine cycle code and a compressor flow code was conducted to determine the values of key icing parameters that can indicate the risk of ice accretion, which can lead to engine rollback (un-commanded loss of engine thrust). The full engine aerothermodynamic performance was modeled with the Honeywell Customer Deck specifically created for the ALF502R-5 engine. The mean-line compressor flow analysis code, which includes a code that models the state of the ice crystal, was used to model the air flow through the fan-core and low pressure compressor. The results of the compressor flow analyses included calculations of the ice-water flow rate to air flow rate ratio (IWAR), the local static wet bulb temperature, and the particle melt ratio throughout the flow field. It was found that the assumed particle size had a large effect on the particle melt ratio, and on the local wet bulb temperature. In this study the particle size was varied parametrically to produce a non-zero calculated melt ratio in the exit guide vane (EGV) region of the low pressure compressor (LPC) for the data points that experienced a growth of blockage, and resulted in an engine called rollback (CRB). At data points where the engine experienced a CRB having the lowest wet bulb temperature of 492 R at the EGV trailing edge, the smallest particle size that produced a non-zero melt ratio (between 3%–4%) was on the order of 1μm. The particle size was varied from 1μm – 9.5μm to achieve the target melt ratio. For data points that did not experience a CRB which had static wet bulb temperatures in the EGV region below 492 R, a non-zero melt ratio could not be achieved even with a 1μm ice particle size. The highest value of static wet bulb temperature for data points that experienced engine CRB was 498 R with a particle size of 9.5μm. Based on this study of the LF11 engine test data, the range of static wet bulb temperature at the EGV exit for engine CRB was in the narrow range of 492 R – 498 R, while the minimum value of IWAR was 0.002. The rate of blockage growth due to ice accretion and boundary layer growth was estimated by scaling from a known blockage growth rate that was determined in a previous study. These results obtained from the LF11 engine analysis formed the basis of a unique icing wedge which defines a region of ice accretion risk that are being applied to other turbofan engines in order to predict the risk of ice accretion at various altitudes and operating conditions.

Update on the NASA Glenn Propulsion Systems Lab Icing and Ice Crystal Cloud Characterization - 2017

“…The basis of this criteria is described in Ref. [8]. By finding regions of icing, parameter sweeps were conducted to move the location of accretion and change the accretion characteristics.…”

ICICLE: A Model for Glaciated & amp; Mixed Phase Icing for Application to Aircraft Engines

“…It has been shown that air temperatures above freezing can be conducive to ice crystal accretion. This has been observed both in tunnel tests [9], [10] and in engine data analysis [1], [39]. However, models and data for particle sticking in these warm conditionswhether empirical or analyticalare generally scarce.…”

Scaling Evaluation of Ice-Crystal Icing on a Modern Turbofan Engine in PSL Using the COMDES-MELT Code

“…Instrumentation development to acquire details on the cloud generation in the facility and the ability to fully instrument an engine allows researchers better understand of the underlying physical mechanisms in order to further improve and validate the existing in-house engine ice accretion simulation and icing risk prediction codes. Those early research works formed the basis of the icing risk criteria used in the development of the NASA in-house 1D Icing Risk tool [14][15][16][17][18].…”