Accurate simulation and understanding of gas turbine performance is very useful for gas turbine users. Such a simulation and performance analysis must start from a design point. When some of the engine component parameters for an existing engine are not available, they must be estimated in order that the performance analysis can be carried out. However, the initially simulated design-point performance of the engine using estimated engine component parameters may give a result that is different from the actual measured performance. This difference may be reduced with better estimation of these unknown component parameters. However, this can become a difficult task for performance engineers, let alone those without enough engine performance knowledge and experience, when the number of design-point component parameters and the number of measurable/target performance parameters become large. In this paper, a gas turbine design-point performance adaptation approach has been developed to best estimate the unknown design-point component parameters and match the available design-point engine measurable/target performance. In the approach, the initially unknown component parameters may be compressor pressure ratios and efficiencies, turbine entry temperature, turbine efficiencies, air mass flow rate, cooling flows, bypass ratio, etc. The engine target (measurable) performance parameters may be thrust and specific fuel consumption for aero engines, shaft power and thermal efficiency for industrial engines, gas path pressures and temperatures, etc. To select, initially, the design point component parameters, a bar chart has been used to analyze the sensitivity of the engine target performance parameters to the design-point component parameters. The developed adaptation approach has been applied to a design-point performance matching problem of an industrial gas turbine engine GE LM2500+ operating in Manx Electricity Authority (MEA), UK. The application shows that the adaptation approach is very effective and fast to produce a set of design-point component parameters of a model engine that matches the actual engine performance very well. Theoretically, the developed techniques can be applied to other gas turbine engines.

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.

Accurate simulation and understanding of gas turbine performance is very useful for gas turbine users. Such a simulation and performance analysis must start from a design point. When some of the engine component parameters for an existing engine are not available, they must be estimated in order that the performance analysis can be carried out. However, the initially simulated design point performance of the engine using estimated engine component parameters may give a result that is different from the actual measured performance. This difference may be reduced with better estimation of these unknown component parameters. However, this can become a difficult task for performance engineers, let alone those without enough engine performance knowledge and experience, when the number of design point component parameters and the number of measurable/target performance parameters become large. In this paper, a gas turbine design point performance adaptation approach has been developed to best estimate the unknown design point component parameters and match the available design point engine measurable/target performance. In the approach, the initially unknown component parameters may be compressor pressure ratios and efficiencies, turbine entry temperature, turbine efficiencies, air mass flow rate, cooling flows, by-pass ratio, etc. The engine target (measurable) performance parameters may be thrust and SFC for aero engines, shaft power and thermal efficiency for industrial engines, gas path pressures and temperatures, etc. To select initially the design point component parameters, a bar chart has been used to analyze the sensitivity of the engine target performance parameters to the design point component parameters. The developed adaptation approach has been applied to a design point performance matching problem of an industrial gas turbine engine GE LM2500+ operating in Manx Electricity Authority (MEA), UK. The application shows that the adaptation approach is very effective and fast to produce a set of design point component parameters of a model engine that matches the actual engine performance very well. Theoretically the developed techniques can be applied to other gas turbine engines.

Part-load performance prediction of gas turbines is strongly dependent on detailed understanding of engine component behavior and mainly that of compressors. The accuracy of gas turbine engine models relies on the compressor performance maps, which are obtained in costly rig tests and remain manufacturer’s proprietary information. The gas turbine research community has addressed this limitation by scaling default generic compressor maps in order to match the targeted off-design measurements. This approach is efficient in small range of operating conditions but becomes less accurate for wide range of operating conditions. In this part of the paper a novel method of compressor map generation which has a primary objective to improve the accuracy of engine models performance at part load conditions is presented. This is to generate a generic form of equations to represent the lines of constant speed and constant efficiency of the compressor map for a generic compressor. The parameters that control the shape of the compressor map have been expressed in their simplest form in order to aid the adaptation process. The proposed compressor map generation method has the capacity to refine current gas turbine performance adaptation techniques, and it has been integrated into Cranfield’s PYTHIA gas turbine performance simulation and diagnostics software tool.

Accurate gas turbine performance simulation is a vital aid to the operational and maintenance strategy of thermal plants having gas turbines as their prime mover. Prediction of the part load performance of a gas turbine depends on the quality of the engine’s component maps. Taking into consideration that compressor maps are proprietary information of the manufacturers, several methods have been developed to encounter the above limitation by scaling and adapting component maps. This part of the paper presents a new off-design performance adaptation approach with the use of a novel compressor map generation method and Genetic Algorithms (GA) optimization. A set of coefficients controlling a generic compressor performance map analytically is used in the optimization process for the adaptation of the gas turbine performance model to match available engine test data. The developed method has been tested with off-design performance simulations and applied to a GE LM2500+ aeroderivative gas turbine operating in Manx Electricity Authority’s combined cycle power plant in the Isle of Man. It has been also compared with an earlier off-design performance adaptation approach, and shown some advantages in the performance adaptation.