Section 2 Gas Turbines

its dynamic heating conditions. Journal of Engineering for Gas Turbines and Power - Transactions of ASME. 2015;

*Modeling of Turbomachines for Control and Diagnostic Applications*

[24] Baqersad J, Niezrecki C, Avitabile P. Full-field dynamic strain prediction on a wind turbine using displacements of

stereophotogrammetry. Mechanical Systems and Signal Processing. 2015; **62–63**:284-295. DOI: 10.1016/j.ymssp.

optical targets measured by

2015.03.021

[18] Jiang X, Mendoza E, Lin TP. Bayesian calibration for power splitting in single shaft combined cycle plant diagnostics. In: Proceedings of ASME Turbo Expo 2015, 15–19 June 2015. USA:

ASME; 2015. p. 11. ASME Paper

[19] Palmer C, Hettler E. Thrust measurement model-based correction system for turbine engine test cell dynamic data. In: Proceedings of ASME Turbo Expo 2015, 15–19 June 2015. USA:

ASME; 2015. p. 8. ASME Paper

[20] Kacprzynski GJ, Gumina M, Roemer MJ, Caguiat DE, Galie TR, McGroarty JJ. A prognostic modelling approach for predicting recurring maintenance for shipboard propulsion system. In: Proceedings of ASME Turbo Expo 2015, 15-19 June 2015. USA: ASME; 2015 ASME Paper No.2001-GT-

[21] Cortés O, Urquiza G, Hernández JA. Optimization of operating conditions for compressor performance by means of neural network inverse. Applied

[22] Loboda I, Zárate M, Yepifanov S, Maravilla Herrera C, Ruiz P. Estimation of gas turbine unmeasured variables for

Energy. 2009;**86**:2487-2493

an online monitoring system. International Journal of Turbo & Jet Engines. 2018. pp. 1-16. e-ISSN: 2191-0332 (e-publication)

[23] The World's Most Powerful Available Wind Turbine Gets Major Power Boost. UK: MHI Vestas Offshore; 2018. Available from: www.mhivesta soffshore.com [Accessed: 09 March

**137**(3):1-10

GT2015-43878

GT2015-43720

0218

2020]

**10**

**Chapter 2**

**Abstract**

real test data.

**1. Introduction**

**13**

identification, approximation

Simulation

Turbine Engine Starting

The process of engine control development requires the models that describe engine operation and its response on a control action. The development flow required numerous models to be engaged, like component-level non-linear model, engine-level non-linear model, linear dynamic model, etc. Models made a great progress during the recent years and became reliable tools for control engineers. However, most models are derivatives from the component-level non-linear model, which in its turn consumes the component performances. Things turn different when one addresses the starting range of engine operation. The problem here is all about the missing performances of the engine components, as it is quite hard to harvest these performances in this region as the processes that happen in the engine are transient by nature. Different scientists offered different approaches to the problem of building the component level non-linear model of the sub-idle region, but the general idea is to somehow extrapolate the known performances to the subidle region. However, there are no known reports about a model that considers all aspects of this approach and simulates the engine starting. In this chapter, you can find an alternative view on a problem of simulation of a sub-idle operation. The proposed model belongs to a group of linear dynamic models including the static model as well as simplified static model to support the dynamic model. Instead of trying to extrapolate component performances and get the full-scale componentlevel model, you will see that the canonical component performances are replaced by the direct relations between parameters that are used in the control algorithms, like gas-path parameters against the RPM. As well in this chapter, you will find the exact instructions on how to create the model and an example of the one with the

**Keywords:** turbine engine, starting, mathematical model, experimental data,

thermodynamic relations under the hood. But their usage is impossible or

Models are known for their benefit to reduce the price for engine development replacing the numerous experiments with the simulations. This has the biggest value for the control engineers as they need the greatest amount of experimental data. Industry came to a gold standard on how to simulate the models of engine operation within the range limited with idle on one side and the maximum on the other side. These models are well structured and validated, having the well-known

*Sergiy Yepifanov and Feliks Sirenko*

**Chapter 2**
