Predicting Dust Damage in Aero-Engines

Abstract

Operating aero-engines in dusty environments results in degradation through a range of damage mechanisms, from erosion and fouling of compressor blades, to deposition on turbine vanes and blocking of blade cooling systems. These mechanisms combine to reduce component efficiencies and degrade engine performance resulting in premature maintenance, increased down-time and potential failure, all of which contribute to increased cost of ownership. The ability to predict degradation rates therefore has significant value to an operator, but the development of damage models is complicated by the wide variability of dust properties which are difficult to ascertain. Monte Carlo simulations provide a means of accounting for this uncertainty in numerical model inputs; however, application of existing damage models is limited in this scenario due to their inherent determinism, lack of generality for different dust types and high computational expense. To enable the large number of simulations required for a Monte Carlo analysis which can be used by operators in a practical setting, reduced-order models which approximate physical phenomena using dimensionless parameters are needed to provide generalised predictions of damage. This thesis builds a methodology to incorporate generalised damage models in performance degradation predictions of aero-engines ingesting dusts with uncertain properties. A novel, reduced-order model for particle deposition on high pressure turbine nozzle guide vanes has been developed which provides a significant reduction in computing time compared to previous methods. The approach uses outputs from numerical simulations to derive reduced-order functions of generalised non-dimensional parameters incorporating particle properties, engine performance variables and the nozzle guide vane geometry. This allows the dust accumulation rate to be predicted and related to reductions in flow capacity and efficiency of the high pressure turbine. The reduced-order model is incorporated into engine performance predictions, producing a coupled degradation model which is used to perform Monte Carlo simulations of aero-engine performance degradation due to the ingestion of a dust with uncertain physical properties. The model has been used to carry out a statistical analysis of performance degradation for aero-engine encounters with volcanic ash, allowing changes in exhaust gas temperature margin and running speeds to be predicted with a quantified level of confidence. Improvements in prediction confidence due to enhanced knowledge of input properties has been investigated, with a potential 80\% increase in confidence available if the number of uncertain model inputs can be reduced to a single particle property. This has enabled predictions of previously ambiguous empirical dust properties and quantification of the engine failure point for particle encounter events. The developed methodology will improve understanding of engine damage rates and aid operations across a range of timescales, from predicting safe operating conditions during volcanic eruptions, to quantifying the economic impacts of proposed routes in arid environments. As the certainty of model input data improves due to enhanced in-situ and remote sensing, the method has potential applications in digital twin approaches to engine health monitoring where degradation rates are predicted from a combination of sensed performance data and model predictions. This will enable a more predictive approach to maintenance scheduling where forecast damage rates are used to minimise un-planned maintenance, engine down-time and though-life operating costs.

Date of Award	31 Dec 2021
Original language	English
Awarding Institution	The University of Manchester
Supervisor	Antonino Filippone (Supervisor) & Nicholas Bojdo (Supervisor)