Uncertainty Quantification and Management in Engine Conceptual Design

Tai, Jimmy C.; Mines, John M.; Inclan, Eric; Zhu, Stephanie Y.

doi:10.2514/6.2016-5064

Cited by 1 publication

(3 citation statements)

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“…In the context of the overall performance design of the engine, a number of studies have been reported to investigate the influence of uncertain variables on the engine performance [6][7][8][9][10][11]. Zhang et al [6] performed uncertainty analysis for an advanced adaptive cycle engine by coupling Monte Carlo sampling (MCS) with linear models.…”

Section: Introductionmentioning

confidence: 99%

“…Cao et al [8] quantified the effect of component performance uncertainty on a turbofan engine using an artificial neural network-based MCS. Tai et al [9] also integrated the artificial neural network and the MCS to quantify the impact of efficiencies of the fan and high-pressure compressor on the performance of a turbofan engine. Lamorte et al [10] proposed a polynomial response surface-based MCS to investigate the effects of uncertain aerothermoelastic deformations on the performance of a scramjet engine.…”

Section: Introductionmentioning

confidence: 99%

“…When it comes to the time-consuming simulation models, MCS may not be applicable due to the computational burden. To solve this problem, several studies built surrogate models to replace the time-consuming physical simulation model [6,[8][9][10]. However, the surrogate model inevitably leads to approximation error since it cannot perfectly substitute the simulation model.…”

Section: Introductionmentioning

confidence: 99%

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Gaussian Process Model-Based Performance Uncertainty Quantification of a Typical Turboshaft Engine

Liu

Tang

Zhang³

et al. 2021

Applied Sciences

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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.

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