An Adaptation Approach for Gas Turbine Design-Point Performance Simulation

Li, Y. G.; Pilidis, Pericles; Newby, Mike

doi:10.1115/gt2005-68140

Cited by 16 publications

(22 citation statements)

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“…The design point thermodynamic performance of the topping cycle is resolved by means of Turbomatch ( 9 , 10 ) an in-house 0D gas-turbine performance software developed at Cranfield University, with external modules for the heat exchangers and blade cooling calculations. The bottoming cycle is simulated by means of SteamoMatch, another in-house 0D performance software developed at Cranfield University for steam Rankine cycle modelling and simulation ( 11 ) .…”

Section: 0 Generation Of Training Data Setmentioning

confidence: 99%

Advanced gas turbine performance modelling using response surface methods

2018

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Surrogate models are widely used for dataset correlation. A popular application very frequently shown in public literature is in the field of engineering design where a large number of design parameters are correlated with performance indices of a complex system based on existing numerical or experimental information. Such an approach allows the identification of the key design parameters and their impact on the system’s performance. The generated surrogate model can become part of wider computational platforms and enable optimisation of the complex system without the need to run expensive simulations.In this paper, a number of design point simulations for a combined gas-steam cycle are used to generate a response surface. The generated response surface correlates a range of cycle’s key design parameters with its thermal efficiency while it also enables identification of the optimum overall pressure ratio and the high pressure level of the raised steam across a range of recuperator effectiveness, pinch temperature difference across the heat recovery steam generator and the pressure at the condenser. The accuracy of a range of surrogate models to capture the design space is evaluated using root mean square statistical metrics.

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Section: 0 Generation Of Training Data Setmentioning

confidence: 99%

Advanced gas turbine performance modelling using response surface methods

2018

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“…A design point performance adaptation (29) for the model at 30 MW is carried out in order to match the available design point performance of the engine. Once the engine model has been properly updated for the selected design point, an initial compressor map is generated.…”

Section: Symbolmentioning

confidence: 99%

Non-linear model calibration for off-design performance prediction of gas turbines with experimental data

Tsoutsanis

Pilidis

et al. 2017

Aeronaut. j.

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One of the key challenges of the gas turbine community is to empower the condition based maintenance with simulation, diagnostic and prognostic tools which improve the reliability and availability of the engines. Within this context, the inverse adaptive modelling methods have generated much attention for their capability to tune engine models for matching experimental test data and/or simulation data. In this study, an integrated performance adaptation system for estimating the steady-state off-design performance of gas turbines is presented. In the system, a novel method for compressor map generation and a genetic algorithm-based method for engine off-design performance adaptation are introduced. The methods are integrated into PYTHIA gas turbine simulation software, developed at Cranfield University and tested with experimental data of an aero derivative gas turbine. The results demonstrate the promising capabilities of the proposed system for accurate prediction of the gas turbine performance. This is achieved by matching simultaneously a set of multiple off-design operating points. It is proven that the proposed methods and the system have the capability to progressively update and refine gas turbine performance models with improved accuracy, which is crucial for model-based gas path diagnostics and prognostics.

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“…The thermodynamic performance model has been adapted to the real engine at both design and off-design operating conditions by using the adaptation method developed at Cranfield [21]- [23] and the field data of the engine. This is to ensure that the model provide accurate performance predictions that will be used for lifing…”

Section: Fig7 Engine Model Configurationmentioning

confidence: 99%

Creep-Life Usage Analysis and Tracking for Industrial Gas Turbines

Saturday

Ogiriki

et al. 2017

Journal of Propulsion and Power

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Creep life usage analysis and tracking of first stage turbine rotor blades of an aero-derivative industrial gas turbine engine is investigated in this study. An engine performance model is created while blade thermal and stress models are developed for the calculation of the blade material temperatures and stresses at different sections of the blade. A creep life model is developed based on the Larson-Miller Parameter method by taking inputs from the thermal and stress models. An integrated creep life estimation system is developed by bringing together the engine performance model, the blade thermal and stress models, the creep life model and a data acquisition and pre-processing model. Relative creep life consumption analysis using new concepts developed in this research is introduced for the analysis of creep life consumption of the gas turbine engine operating for a period of time; these concepts include Equivalent Creep Life (ECL) and Equivalent Creep Factor (ECF). The developed algorithms have been applied to the creep life tracking of an aero derivative gas turbine engine using its field test data. The results show that it is able to provide a quick evaluation and tracking of engine creep life consumption and provide very useful information for gas turbine operators to support their operation optimization and creep life consumption monitoring.

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An Adaptation Approach for Gas Turbine Design-Point Performance Simulation

Cited by 16 publications

References 0 publications

Advanced gas turbine performance modelling using response surface methods

Advanced gas turbine performance modelling using response surface methods

Non-linear model calibration for off-design performance prediction of gas turbines with experimental data

Creep-Life Usage Analysis and Tracking for Industrial Gas Turbines

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