Enhanced Engine Performance During Emergency Operation Using a Model-Based Engine Control Architecture
Jeffrey T. Csank,Joseph W. Connolly +1 more
- 27 Jul 2015
TL;DR: In this paper, the authors discuss the design and application of model-based engine control (MBEC) for use during emergency operation of the aircraft, which is applied to the Commercial Modular Aero-Propulsion System Simulation 40,000 (CMAPSS40,000) and features an optimal tuner Kalman Filter (OTKF) to estimate unmeasured engine parameters, which can then be used for control.
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Abstract: This paper discusses the design and application of model-based engine control (MBEC) for use during emergency operation of the aircraft. The MBEC methodology is applied to the Commercial Modular Aero-Propulsion System Simulation 40,000 (CMAPSS40,000) and features an optimal tuner Kalman Filter (OTKF) to estimate unmeasured engine parameters, which can then be used for control. During an emergency scenario, normally-conservative engine operating limits may be relaxed to increase the performance of the engine and overall survivability of the aircraft; this comes at the cost of additional risk of an engine failure. The MBEC architecture offers the advantage of estimating key engine parameters that are not directly measureable. Estimating the unknown parameters allows for tighter control over these parameters, and on the level of risk the engine will operate at. This will allow the engine to achieve better performance than possible when operating to more conservative limits on a related, measurable parameter.
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Figures
Figure 3 EEC architecture. Lightly shaded blocks are modifications from the standard control architecture. Note that feedback paths were removed for simplification. 
Figure 9 Relationship between rise time and minimum HPC SM for the baseline controller (CMAPSS40k) and MBEC with a reduced surge margin for emergency scenarios. 
Figure 4 Comparison between the standard CMAPSS40k controller and MBEC at an altitude of 885.6ft and 0.1081 Mach. The top plot compares the thrust response compares the demanded and the bottom compares EPR. 
Figure 8 Turbine inlet temperature (T40) 10-3 risk level as a function of core speed (Risk Boundary) 
Figure 7 Calculated failure probability for 200 random cases operating at baseline maximum thrust, overthrust using the T50 sensor measurement to limit risk, and overthrust using the T40 measurement available with MBEC to limit risk. 
Figure 1 MBEC Control Architecture. The thrust controller, T40 Limiter, and SM Limiter all rely on estimated measurements generated from the Optimal Tuner Kalman Filter.
Citations
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Model-Based Engine Control Architecture with an Extended Kalman Filter
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TL;DR: In this article, an extended Kalman filter (EKF) for model-based engine control (MBEC) is proposed to reduce the estimation errors associated with the linearization process.
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