| contributor author | Al Volponi | |
| contributor author | Tom Brotherton | |
| contributor author | Rob Luppold | |
| date accessioned | 2017-05-09T00:27:58Z | |
| date available | 2017-05-09T00:27:58Z | |
| date copyright | March, 2008 | |
| date issued | 2008 | |
| identifier issn | 1528-8919 | |
| identifier other | JETPEZ-27001#021604_1.pdf | |
| identifier uri | http://yetl.yabesh.ir/yetl/handle/yetl/137971 | |
| description abstract | A practical consideration for implementing a real-time on-board engine component performance tracking system is the development of high fidelity engine models capable of providing a reference level from which performance changes can be trended. Real-time engine models made their advent as state variable models in the mid-1980s, which utilized a piecewise linear model that granted a reasonable representation of the engine during steady state operation and mild transients. Increased processor speeds over the next decade allowed more complex models to be considered, that were a combination of linear and nonlinear physics-based elements. While the latter provided greater fidelity over both transient operation and the engine operational flight envelope, these models could be further improved to provide the high level of accuracy required for long-term performance tracking, as well as address the issue of engine-to-engine variation. Over time, these models may deviate enough from the actual engine being monitored, as a result of improvements made during an engine’s life cycle such as hardware modifications, bleed and stator vane schedule alterations, cooling flow adjustments, and the like, that the module performance estimations are inaccurate and often misleading. The process described in this paper will address these shortcomings while maintaining the execution speed required for real-time implementation. | |
| publisher | The American Society of Mechanical Engineers (ASME) | |
| title | Empirical Tuning of an On-Board Gas Turbine Engine Model for Real-Time Module Performance Estimation | |
| type | Journal Paper | |
| journal volume | 130 | |
| journal issue | 2 | |
| journal title | Journal of Engineering for Gas Turbines and Power | |
| identifier doi | 10.1115/1.2799527 | |
| journal fristpage | 21604 | |
| identifier eissn | 0742-4795 | |
| keywords | Physics | |
| keywords | Engines | |
| keywords | Flight | |
| keywords | Artificial neural networks | |
| keywords | Gas turbines | |
| keywords | Modeling | |
| keywords | Hardware AND Flow (Dynamics) | |
| tree | Journal of Engineering for Gas Turbines and Power:;2008:;volume( 130 ):;issue: 002 | |
| contenttype | Fulltext | |