Volume 31 Issue 12
Dec.  2016
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Article Navigation >  Journal of Aerospace Power  > 2016  >  31(12): 3042-3053
YANG Shu-bo, WANG Xi, LONG Yi-fu, LI Zhi-peng, HU Zhong-zhi, YIN Kai, ZHANG Rong. Partition method for improvement of piecewise linear model with full envelope covered[J]. Journal of Aerospace Power, 2016, 31(12): 3042-3053. doi: 10.13224/j.cnki.jasp.2016.12.029
Citation: YANG Shu-bo, WANG Xi, LONG Yi-fu, LI Zhi-peng, HU Zhong-zhi, YIN Kai, ZHANG Rong. Partition method for improvement of piecewise linear model with full envelope covered[J]. Journal of Aerospace Power, 2016, 31(12): 3042-3053. doi: 10.13224/j.cnki.jasp.2016.12.029
shu

Partition method for improvement of piecewise linear model with full envelope covered

doi: 10.13224/j.cnki.jasp.2016.12.029

  • 1. School of Energy and Power Engineering, Beijing University of Aeronautics and Astronautics, Beijing 100191, China;

  • 2. Commercial Aircraft Engine Company Limited, Aero Engine (Group) Corporation of China, Shanghai 201108, China

  • Received Date: 2015-03-31
  • Publish Date: 2016-12-28
    Abstract
  • Since ambient conditions vary in a wide range within the full flight envelope, the existing piecewise linear model (PLM), which is based on sea-level static condition with use of corrected parameters for other points in the flight envelope, cannot meet the accuracy for replacing the nonlinear model. To obtain more accurate linear models, a method of partitioning the flight envelope over a grid of Mach number and altitude boxes was suggested. Then, a set of linear models for a given operating condition was selected by picking the nearest (Mach number, altitude) box in the flight envelope. Through the selected set of linear models, interpolating for power level based on a weighted sum of corrected rotor speeds can obtain a linear model with acceptable accuracy. Simulation results of different points within the full flight envelope showed that the maximum error between nonlinear model and the existing PLM was more than 50%, while the maximum error between nonlinear model and the improved PLM was within 8%. It is concluded that the improved PLM performs accurately, especially under the non-standard conditions. In addition, the improved PLM can satisfy the real-time requirement better than the existing PLM.

     

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