Monte Carlo Predictions of Aero-Engine Performance Degradation due to Particle IngestionCitation formats

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Monte Carlo Predictions of Aero-Engine Performance Degradation due to Particle Ingestion. / Ellis, Matthew; Bojdo, Nicholas; Filippone, Antonino; Clarkson, Rory .

In: Aerospace, Vol. 8, No. 6, 146, 25.05.2021.

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@article{f6352420f13642a59e69d310f258eff3,
title = "Monte Carlo Predictions of Aero-Engine Performance Degradation due to Particle Ingestion",
abstract = "Aero-engines which encounter clouds of airborne particulate experience reduced performance due to the deposition of particles on their high pressure turbine nozzle guide vanes. The rate of this degradation depends on particle properties, engine operating state and duration of exposure to the particle cloud, variables which are often unknown or poorly constrained, leading to uncertainty in model predictions. A novel method coupling one-dimensional gas turbine performance analysis with generalised predictions of particle deposition is developed and applied through the use of Monte Carlo simulations to better predict high pressure turbine degradation. This enables a statistical analysis of deterioration from which mean performance losses and confidence intervals can be defined, allowing reductions in engine life and increased operational risk to be quantified. The method is demonstrated by replicating two particle cloud encounter events for the Rolls-Royce RB211-524C engine and is used to predict empirical particle properties by correlating measured engine performance data with Monte Carlo model inputs. Potential improvements in the confidence of these predictions due to more tightly constrained input and validation data is also demonstrated. Finally, the potential combination of the Monte Carlo coupled degradation model with in-service engine performance data and particle properties determined through remote or in-situ sensing is outlined and its role in a digital twin to enable a predictive approach to operational capability discussed.",
author = "Matthew Ellis and Nicholas Bojdo and Antonino Filippone and Rory Clarkson",
year = "2021",
month = may,
day = "25",
doi = "10.3390/aerospace8060146",
language = "English",
volume = "8",
journal = "Aerospace",
issn = "2226-4310",
publisher = "MDPI",
number = "6",

}

RIS

TY - JOUR

T1 - Monte Carlo Predictions of Aero-Engine Performance Degradation due to Particle Ingestion

AU - Ellis, Matthew

AU - Bojdo, Nicholas

AU - Filippone, Antonino

AU - Clarkson, Rory

PY - 2021/5/25

Y1 - 2021/5/25

N2 - Aero-engines which encounter clouds of airborne particulate experience reduced performance due to the deposition of particles on their high pressure turbine nozzle guide vanes. The rate of this degradation depends on particle properties, engine operating state and duration of exposure to the particle cloud, variables which are often unknown or poorly constrained, leading to uncertainty in model predictions. A novel method coupling one-dimensional gas turbine performance analysis with generalised predictions of particle deposition is developed and applied through the use of Monte Carlo simulations to better predict high pressure turbine degradation. This enables a statistical analysis of deterioration from which mean performance losses and confidence intervals can be defined, allowing reductions in engine life and increased operational risk to be quantified. The method is demonstrated by replicating two particle cloud encounter events for the Rolls-Royce RB211-524C engine and is used to predict empirical particle properties by correlating measured engine performance data with Monte Carlo model inputs. Potential improvements in the confidence of these predictions due to more tightly constrained input and validation data is also demonstrated. Finally, the potential combination of the Monte Carlo coupled degradation model with in-service engine performance data and particle properties determined through remote or in-situ sensing is outlined and its role in a digital twin to enable a predictive approach to operational capability discussed.

AB - Aero-engines which encounter clouds of airborne particulate experience reduced performance due to the deposition of particles on their high pressure turbine nozzle guide vanes. The rate of this degradation depends on particle properties, engine operating state and duration of exposure to the particle cloud, variables which are often unknown or poorly constrained, leading to uncertainty in model predictions. A novel method coupling one-dimensional gas turbine performance analysis with generalised predictions of particle deposition is developed and applied through the use of Monte Carlo simulations to better predict high pressure turbine degradation. This enables a statistical analysis of deterioration from which mean performance losses and confidence intervals can be defined, allowing reductions in engine life and increased operational risk to be quantified. The method is demonstrated by replicating two particle cloud encounter events for the Rolls-Royce RB211-524C engine and is used to predict empirical particle properties by correlating measured engine performance data with Monte Carlo model inputs. Potential improvements in the confidence of these predictions due to more tightly constrained input and validation data is also demonstrated. Finally, the potential combination of the Monte Carlo coupled degradation model with in-service engine performance data and particle properties determined through remote or in-situ sensing is outlined and its role in a digital twin to enable a predictive approach to operational capability discussed.

UR - https://doi.org/10.3390/aerospace8060146

U2 - 10.3390/aerospace8060146

DO - 10.3390/aerospace8060146

M3 - Article

VL - 8

JO - Aerospace

JF - Aerospace

SN - 2226-4310

IS - 6

M1 - 146

ER -