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Research Papers: Gas Turbines: Controls, Diagnostics, and Instrumentation

Benefits of Active Compressor Stability Management on Turbofan Engine Operability

[+] Author and Article Information
Yuan Liu, Manuj Dhingra, J. V. Prasad

School of Aerospace Engineering, Georgia Institute of Technology, Atlanta, GA 30332

J. Eng. Gas Turbines Power 131(4), 041601 (Apr 14, 2009) (9 pages) doi:10.1115/1.3028565 History: Received May 08, 2008; Revised May 23, 2008; Published April 14, 2009

Active compressor stability management can play a significant role in the intelligent control of gas turbine engines. The present work utilizes a computer simulation to illustrate the potential operability benefits of compressor stability management when actively controlling a turbofan engine. The simulation, called the modular aeropropulsion system simulation (MAPSS) and developed at NASA Glenn, models the actuation, sensor, controller, and engine dynamics of a twin-spool, low-bypass turbofan engine. The stability management system is built around a previously developed stability measure called the correlation measure. The correlation measure quantifies the repeatability of the pressure signature of a compressor rotor. Earlier work has used laboratory compressor and engine rig data to develop a relationship between a compressor’s stability boundary and its correlation measure. Specifically, correlation measure threshold crossing events increase in magnitude and number as the compressor approaches the limit of stable operation. To simulate the experimentally observed behavior of these events, a stochastic model based on level-crossings of an exponentially distributed pseudorandom process has been implemented in the MAPSS environment. Three different methods of integrating active stability management within the existing engine control architecture have been explored. The results show that significant improvements in the engine emergency response can be obtained while maintaining instability-free compressor operation via any of the methods studied. Two of the active control schemes investigated utilize existing scheduler and controller parameters and require minimal additional control logic for implementation. The third method, while introducing additional logic, emphasizes the need for as well as the benefits of a more integrated stability management system.

Copyright © 2009 by American Society of Mechanical Engineers
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Figures

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Figure 1

Potential performance improvements

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Figure 2

Schematic of the MAPSS low-bypass turbofan engine (Parker and Guo (21))

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Figure 3

TBE: An event occurs when correlation measure drops below some specified threshold

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Figure 4

The cumulative distribution function of TBE when correlation measure is calculated at various stall margins is approximately exponential

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Figure 5

Curve fit of average number of events versus stall margin used for the stochastic model based on experimental data from a laboratory compressor rig at Georgia Tech. The labels denote the error in the stall margin of the curve fit from each data point.

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Figure 6

PLA input for all simulation runs and corresponding net thrust demand after PLA rate limiter

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Figure 7

Stall margin and net thrust responses to PLA step input command. The original fuel scheduler is unable to prevent stall without the stall margin regulator. The modified scheduler prevents stall but degrades transient performance.

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Figure 8

Schematic of the direct fuel manipulation control scheme

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Schematic of the fuel scheduler manipulation control scheme

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Schematic of the integrated stability management using a built-in stall margin regulator

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Fuel manipulation results: rise time values for 100 runs. The average rise time is 6.55s. 95% success rate (rise time of 0s indicates a failed run).

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Figure 12

Fuel manipulation results: stall margin, events, and fuel command signals for a representative successful run

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Figure 13

Fuel manipulation results: engine thrust response for a representative successful run

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Fuel manipulation results: stall margin, events, and fuel command signals for a representative failed run

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Fuel manipulation results: engine thrust response for a representative failed run

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Figure 16

Scheduler manipulation results: rise time values for 100 runs. The average rise time is 4.58s. 100% success rate.

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Figure 17

Scheduler manipulation results: stall margin, events, and fuel command signals for a representative run

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Figure 18

Scheduler manipulation results: engine thrust response for a representative run

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Figure 19

Integrated stability management results: engine thrust response for a representative run

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Figure 20

Integrated stability management results: rise time values for 100 runs. The average rise time is 3.91s. 100% success rate.

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Figure 21

Integrated stability management results: stall margin, events, and fuel command signals for a representative run

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Figure 22

Integrated stability management at cruise conditions: rise time values for 100 runs. The average rise time is 17.84s. 99% success rate (failed run not shown).

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Figure 23

Integrated stability management at cruise conditions: stall margin, events, and fuel command signals for a representative successful run

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Figure 24

Integrated stability management at cruise conditions: engine thrust response for a representative successful run

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