Assessing the Errors in Practical Gas Turbine Modelling

Evans, C.; Rees, D.

doi:10.1115/99-gt-138

Cited by 5 publications

(2 citation statements)

References 3 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The identification is conducted in the frequency domain, which allows s-domain multivariable models to be directly estimated. The work is an extension of that previously conducted on the identification of transfer-function models, which was presented at previous ASME gas turbine conferences [1,2] and has been published in a number of recent journal papers [3][4][5].…”

Section: Introductionmentioning

confidence: 92%

Multivariable Modelling of Gas Turbine Dynamics

Evans

Chiras

Guillaume

et al. 2001

Volume 4: Manufacturing Materials and Metallurgy; Ceramics; Structures and Dynamics; Controls, Diagnostics and Instrumentation;

Self Cite

View full text Add to dashboard Cite

The linear multivariable modelling of an aircraft gas turbine is presented. A frequency-domain identification method is employed to estimate a family of models from engine data at a range of operating points. It is found that the fuel feed to shaft speed dynamics can be represented by second-order models. This matches the results obtained for the reduced-order linearised thermodynamic models derived from the engine physics. A direct comparison can thus be made between the estimated and thermodynamic models, which shows significant discrepancies between them for the engine tested. This work illustrates how modern system identification techniques can be used to verify engine models. The multivariable framework employed means that additional inputs and outputs can be easily incorporated into the model.

show abstract

Section: Introductionmentioning

confidence: 92%

Multivariable Modelling of Gas Turbine Dynamics

Evans

Chiras

Guillaume

et al. 2001

Volume 4: Manufacturing Materials and Metallurgy; Ceramics; Structures and Dynamics; Controls, Diagnostics and Instrumentation;

Self Cite

View full text Add to dashboard Cite

show abstract

“…It is possible to detect the presence of the engine nonlinearity by analysing the small-signal data at a single operating point. If a signal contains only harmonics that are odd multiples of the fundamental (such as an IRMLBS or an odd-harmonic multisine) then all the frequency contributions at the output resulting from any even-order nonlinearities will fall at even harmonics [14]. Thus the even nonlinearities can be detected just by inspection of the frequency content of the system input and output signals.…”

Section: Detecting the Nonlinearitymentioning

confidence: 99%

Global Nonlinear Modelling of Gas Turbine Dynamics Using NARMAX Structures

Chiras

Evans

Rees

2001

Volume 4: Manufacturing Materials and Metallurgy; Ceramics; Structures and Dynamics; Controls, Diagnostics and Instrumentation;

Self Cite

View full text Add to dashboard Cite

This paper examines the estimation of a global nonlinear gas turbine model using NARMAX techniques. Linear models estimated on small-signal data are first examined and the need for a global nonlinear model is established. A nonparametric analysis of the engine nonlinearity is then performed in the time and frequency domains. The information obtained from the linear modelling and nonlinear analysis is used to restrict the search space for nonlinear modelling. The nonlinear model is then validated using large-signal data and its superior performance illustrated by comparison with a linear model. This paper illustrates how periodic test signals, frequency domain analysis and identification techniques, and time-domain NARMAX modelling can be effectively combined to enhance the modelling of an aircraft gas turbine.

show abstract