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Because of the increasing share of renewables in the energy market, part load operation of gas turbine combined cycle (GTCC) power plants has become a major issue. In combination with the variable ambient conditions and fuel quality, load variations cause these plants to be operated across a wide range of conditions and settings. However, efficiency improvement and optimization studies are often focused on single operating points. The current study assesses efficiency improvement possibilities for the KA26 GTCC plant, as recently built in Lelystad, The Netherlands, taking into account that the plant is operated under frequently varying conditions and load settings. In this context, free operational parameters play an important role: these are the process parameters, which can be adjusted by the operator without compromising safety and other operational objectives. The study applies a steady state thermodynamic model with second-law analysis for exploring the entire operational space. A method is presented for revealing correlations between the exergy losses in major system components, indicating component interactions. This is achieved with a set of numerical simulations, in which operational conditions and settings are randomly varied, recording plant efficiency and exergy losses in major components. The resulting data is used to identify distinct operational regimes for the GTCC. Finally, the free operational parameters are used as decision variables in a genetic algorithm, optimizing plant efficiency in the operational regimes identified earlier. The results show that the optimal settings for decision variables depend on the regime of operation. [ABSTRACT FROM AUTHOR]

Highlights: [•] Bayesian calibration is applied to power plant performance prediction. [•] Measurements from a plant in operation are used for model calibration. [•] A gas turbine performance model and steam cycle model are calibrated. [•] An integrated plant model is derived. [•] Part load efficiency is accurately predicted as a function of ambient conditions. [Copyright &y& Elsevier]

Purpose -- The purpose of this paper is to present a novel hybrid engine concept for a multi-fuel blended wing body (MFBWB) aircraft and assess the performance of this engine concept. Design/methodology/approach -- The proposed hybrid engine concept has several novel features which include a contra-rotating fan for implementing boundary layer ingestion, dual combustion chambers using cryogenic fuel (liquefied natural gas [LNG] or liquid hydrogen [LH2]) and kerosene in the inter-turbine burner (in flameless combustion mode) and a cooling system for bleed air cooling utilizing the cryogenic fuel. A zero-dimensional thermodynamic model of the proposed hybrid engine is created using Gas Turbine Simulation Program to parametrically analyse the performance of various possible engine architectures. Furthermore, the chosen engine architecture is optimized at a cycle reference point using a developed in-house thermodynamic engine model coupled with genetic algorithm. Findings -- Using LH2 and kerosene, the hybrid engine can theoretically reduce CO2 emissions by around 80 per cent. Using LNG and kerosene, the CO2 emissions are reduced by more than 20 per cent as compared to the baseline engine. Practical implications -- The hybrid engine is being investigated in the AHEAD project co-sponsored by the European Commission. This unique aircraft and engine combination will enable aviation to use cryogenic fuels like LH2 or LNG, and will make aviation sustainable. Originality/value -- The MFBWB concept and the hybrid engine is a novel concept which has not yet been investigated before. The potential implications of this technology are far reaching and will shape the future development in aviation. [ABSTRACT FROM AUTHOR]

Gas path analysis (GPA) is an effective method for determination of turbofan component condition from measured performance parameters. GPA is widely applied on engine test rig data to isolate components responsible for petformance problems, thereby offering substantial cost saving potential. Additional benefits can be obtained from the application of GPA to on-wing engine data. This paper describes the experience with model-based GPA on large volumes of on-wing measured performance data. Critical is the minimization of the GPA results uncertainty in order to maintain reliable diagnostics and condition monitoring information. This is especially challenging given the variable in-flight operating conditions and limited on-wing sensor accuracy. The uncertainty effects can be mitigated by statistical analysis and filtering and postprocessing of the large datasets. By analyzing correlations between measured performance data trends and estimated component condition trends errors can be isolated from the GPA results. The various methods assessed are described and results are demonstrated in a number of case studies on a large turbofan engine fleet. [ABSTRACT FROM AUTHOR]