Full Content is available to subscribers

Subscribe/Learn More  >

Engine Parameter Estimation in Test Cells Using Hybrid Physics/Empirical Models

[+] Author Affiliations
Jonathan A. DeCastro, Liang Tang

Impact Technologies, LLC, Rochester, NY

Dean K. Frederick

Saratoga Control Systems, Inc., Saratoga Springs, NY

Paper No. GT2011-45633, pp. 169-176; 8 pages
  • ASME 2011 Turbo Expo: Turbine Technical Conference and Exposition
  • Volume 3: Controls, Diagnostics and Instrumentation; Education; Electric Power; Microturbines and Small Turbomachinery; Solar Brayton and Rankine Cycle
  • Vancouver, British Columbia, Canada, June 6–10, 2011
  • Conference Sponsors: International Gas Turbine Institute
  • ISBN: 978-0-7918-5463-1
  • Copyright © 2011 by ASME


Estimation of engine parameters such as thrust in test cells is a difficult process due to the highly nonlinear nature of the engine dynamics, the complex interdependency of thrust and the engine’s health condition, and factors that corrupt thrust measurements due to test stand construction. Because the frequency content of the corrupting dynamics is close to the engine’s dynamics, filtering the thrust signal is not sufficient for extraction of the true dynamic content. A configurable thrust estimation system is developed for accurate data reduction which provides “virtual” measurements of thrust and other necessary parameters at steady state and during aggressive engine transients. The thrust estimation framework consists of a representative nonlinear engine model coupled with an adaptive structural dynamics model. To account for discrepancies between the physics-based model and the true engine, a hybrid model using a novel neural network (NN) enhancement to a physics-based engine model is presented that reduces certain modeling errors between the engine model and the physical plant. This includes engine-to-engine variation, engine degradation and any essential neglected dynamics. To fuse the model and sensor measurements, this hybrid model is used within a constant-gain extended Kalman filter batch estimator which is able to reconstruct the true dynamic performance of the engine using noisy or corrupted sensor measurements and control inputs. The Kalman filter estimates measured and unmeasured parameters and state variables such as engine component deterioration parameters and effective flow areas.

Copyright © 2011 by ASME



Interactive Graphics


Country-Specific Mortality and Growth Failure in Infancy and Yound Children and Association With Material Stature

Use interactive graphics and maps to view and sort country-specific infant and early dhildhood mortality and growth failure data and their association with maternal

Citing articles are presented as examples only. In non-demo SCM6 implementation, integration with CrossRef’s "Cited By" API will populate this tab (http://www.crossref.org/citedby.html).

Some tools below are only available to our subscribers or users with an online account.

Related Content

Customize your page view by dragging and repositioning the boxes below.

Related eBook Content
Topic Collections

Sorry! You do not have access to this content. For assistance or to subscribe, please contact us:

  • TELEPHONE: 1-800-843-2763 (Toll-free in the USA)
  • EMAIL: asmedigitalcollection@asme.org
Sign In