Abstract
The gas turbine engine is a widely used thermodynamic system for aircraft. The demand for quantifying the uncertainty of engine performance is increasing due to the expectation of reliable engine performance design. In this paper, a fast, accurate, and robust uncertainty quantification method is proposed to investigate the impact of component performance uncertainty on the performance of a classical turboshaft engine. The Gaussian process model is firstly utilized to accurately approximate the relationships between inputs and outputs of the engine performance simulation model. Latin hypercube sampling is subsequently employed to perform uncertainty analysis of the engine performance. The accuracy, robustness, and convergence rate of the proposed method are validated by comparing with the Monte Carlo sampling method. Two main scenarios are investigated, where uncertain parameters are considered to be mutually independent and partially correlated, respectively. Finally, the variance-based sensitivity analysis is used to determine the main contributors to the engine performance uncertainty. Both approximation and sampling errors are explained in the uncertainty quantification to give more accurate results. The final results yield new insights about the engine performance uncertainty and the important component performance parameters.
Highlights
As a typical thermodynamic system, the gas turbine engine has been widely used to provide propulsion or power to various aircrafts, for example, to provide propulsion to civil airliners and power to helicopters
The Gaussian process model (GPM) associated with the overall performance are established considering the effects of sampling variability in the experimental design and run size of the simulation model
GPMs, a detailed comparison of the proposed GPM-based Latin hypercube sampling (LHS) with Monte Carlo sampling (MCS) is discussed to explain the advantages of the proposed method
Summary
As a typical thermodynamic system, the gas turbine engine has been widely used to provide propulsion or power to various aircrafts, for example, to provide propulsion to civil airliners and power to helicopters. Chen et al [13] applied an interval analysis method based on Taylor series expansion to analyze the effect of component performance uncertainty on an adaptive cycle engine. The probabilistic methods can provide some statistics, e.g., the mean and the standard deviation, about the uncertain variables This information is very useful for engine designers to better understand and reduce the engine performance uncertainty. The proposed GPM-based LHS method is adopted to investigate the overall performance uncertainty of a typical turboshaft engine. Two objectives of this study are to quantitatively describe the characterization and propagation of the component performance uncertainty in the engine, and determine the principal contributors to the performance uncertainty of the engine To this end, a model is firstly built to simulate the turboshaft engine performance based on thermodynamic cycle.
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