Active-control technologies for aircraft engines

Przybylko, S.

doi:10.2514/6.1997-2769

Cited by 5 publications

(3 citation statements)

References 6 publications

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“…Modern gas turbines have increased flexibility and sophisticated control strategies to maximize efficiency, reduce fuel burn, protect engine life and ensure operating stability and safety. Apart from the fuel flow injection control system, some of the most well-documented control features of advanced gas turbines include the variable stator vane (VSV) angle [1,2] and handling bleed-offs to ensure compressor stability [3] at all power settings, the take-off derate to improve life consumption and climb thrust derate settings (with an option for slow and fast washout) to trade-off fuel burn and engine life [4,5]. In the most recent engines, active tip clearance control [6,7] is also used to improve engine efficiency by minimizing the gap between the tip and the casing while avoiding blade rubs under the variable disk and blade thermal and mechanical expansion over the mission.…”

Section: A State Of the Artmentioning

confidence: 99%

Mapping the Effect of Variable HPT Blade Cooling on Fuel Burn, Engine Life and Emissions for Fleet Optimization using Active Control

Pontika

Laskaridis

Gonzalez

et al. 2023

AIAA SCITECH 2023 Forum

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Modern aero engines have increasingly sophisticated control systems. The aim for nextgeneration aircraft is to have even more adaptive and flexible control systems to enable the optimization of economic aspects, operational aspects and fleet management. Among others, an engine control variable that has the potential to offer various life and fuel burn benefits at different flight phases is the High-Pressure Turbine (HPT) blade cooling air. The HPT blades have demanding cooling requirements to protect their life and decelerate HPT efficiency degradation. However, any engine bleed has a penalty in efficiency and results in increased fuel consumption. Previous generation aircraft have a fixed relative blade cooling flow based on a design choice for a trade-off between life and efficiency. However, with adaptive control systems, there is an opportunity to extract the maximum potential benefit under different flight phases and scenarios. With this opportunity comes the challenge of increased complexity in engine behavior necessitating detailed modeling to quantify effects on lifing, fuel burn and safety. This paper focuses on modeling the performance, lifing and emission effects of variable HPT blade cooling air at take-off, climb and cruise. First, the effect of variable cooling on the Turbine Entry Temperature (TET), Exhaust Gas Temperature (EGT), fuel flow, lifing and NOx emissions are modeled at operating point level while the thrust requirement is achieved. Subsequently, a Design of Experiment is performed at mission level with the relative cooling flow at take-off, climb and cruise as the independent variables to train surrogate, analytical models. The analytical models are applied in the probabilistic modeling of system failure rates under different cooling flows. Optimization of engine control variables, in this case, the HPT blade cooling, requires analytical expressions that can be used in objective functions. These analytical models will inform fleet optimizers and active control systems to facilitate the implementation of fleet decisions such as reducing direct operating costs (fuel cost, maintenance reserves, NOx taxation), meeting NOx requirements of airports and extending Time-on-Wing (TOW). The findings indicate that take-off offers an opportunity to protect HPT life with increased cooling, but caution should be exercised in regard to the damage increase at the downstream non-cooled hot gas path components. A decrease in cooling flow at cruise, which is less detrimental to engine life, can offer significant fuel savings and climb

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Section: A State Of the Artmentioning

confidence: 99%

Mapping the Effect of Variable HPT Blade Cooling on Fuel Burn, Engine Life and Emissions for Fleet Optimization using Active Control

Pontika

Laskaridis

Gonzalez

et al. 2023

AIAA SCITECH 2023 Forum

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“…2 Therefore, in order to ensure the operation safety of aeroengine, compressors are currently being designed with conservative stall margin requirements that must include effects due to deterioration, engine control tolerance, engine transient thermal effects, and inlet distortion. 3 Generally, the surge margin of the engine can reach about 30% when it works stably. Such conservative stall margin requirements result in efficiency penalties that impact the overall engine fuel burn and temperature margins, which also mean that the engine performance cannot be fully utilized.…”

Section: Introductionmentioning

confidence: 99%

Real-time prediction for the surge of turboshaft engine using multi-branch feature fusion neural network

Zhang

et al. 2022

Proceedings of the Institution of Mechanical Engineers, Part G:

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The existing aeroengine instability precursor detection methods can be summarized as applying advanced signal processing technologies to various signals from the compressor test rig rather than the whole engine. Besides, these methods seriously depend on the artificial designed feature and threshold and also ignore the limit on the sensors onboard. Thus, with the help of the powerful feature extraction ability of the deep neural network, a real-time surge prediction method based on the multi-branch feature fusion neural network (MBFFNN) is proposed. First, the dataset can be obtained by using overlapping slices to divide surge test data into a sample sequence and using complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) to label each sample precisely. Second, for each sample, the time-domain statistical parameters are calculated and the recurrence plot is obtained by using phase space reconstruction. Finally, the MBFFNN with mixed data type input is designed, and its performance is evaluated by the generated dataset. The experimental results show that compared with multilayer perceptron (MLP), long short-term memory (LSTM), and deep residual network (DRN), MBFFNN has the best performance on two datasets for different surge tests, which demonstrates that the proposed method for surge prediction can accurately judge the state of the aeroengine, identify the instability precursor before the surge, and give an early warning in advance.

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“…In recent decades, active compressor stabilization control technology has become leading edge technology and research hotspot in the field of aero-engine control [3,4], which is essentially different from the traditional surge control scheme. A lot of researchers were contributed to this field and presented different active compressor stabilization methods.…”

Section: Introductionmentioning

confidence: 99%

Key technology analysis for active compressor stabilization control of aero-engine

Huang

San-mai

2011

2011 International Conference on Mechatronic Science, Electric Engineering and Computer (MEC)

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Flow instabilities of rotating stall and surge in aero-engine compressors limit their maximum operating range and performance. For preventing stall and surge, active compressor stabilization control is a hot technology and the way of future industrial applications. In this paper, the principle of active compressor stabilization control, active control laws and methods, related actuators and sensors are analyzed. Key technologies and future research directions of active compressor stabilization control are then presented. And the conclusion could provide reference for system design and industrial applications of active compressor stabilization control.

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Active-control technologies for aircraft engines

Cited by 5 publications

References 6 publications

Mapping the Effect of Variable HPT Blade Cooling on Fuel Burn, Engine Life and Emissions for Fleet Optimization using Active Control

Mapping the Effect of Variable HPT Blade Cooling on Fuel Burn, Engine Life and Emissions for Fleet Optimization using Active Control

Real-time prediction for the surge of turboshaft engine using multi-branch feature fusion neural network

Key technology analysis for active compressor stabilization control of aero-engine

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