Energy and exergy modeling of three shaft turboprop engine used in regional aircraft

Compliance with sustainability goals in the aviation industry has become the main motivation for the design of aircraft systems. However, the environmental impacts of aviation have necessitated the design of more fuel-efficient engines. In this thesis, detailed performance analysis, exergy analysis and modelling has been carried out for a conceptual turboprop engine, which is very similar to the PW127-E engine, which is one of the turboprop engines important for regional airline transport. The analysis of the turboprop engine has been carried out theoretically and verified with a simulation programme. Engine data obtained from the simulation program were used in the modelling analysis. On the other hand, the effects of flight conditions (Mach and altitude) and engine design parameters (combustion chamber exit temperature (CCET), booster pressure ratio (BPR), component isentropic efficiencies) on performance indices and thermodynamic indices are analysed. In the first stage of this thesis, the ideal and real parametric cycle equations of the turboprop engine were coded in MATLAB environment and the performance parameters were observed. The results obtained are compared with the results obtained from the gas turbine engine simulation programme. In the second stage of the thesis, exergy analyses were performed for the PW127-E engine model both on component basis and for the whole engine and the results obtained were presented with exergetic indices. In the last stage of the thesis, both performance parameters and exergy parameters are modelled by regression analysis method depending on the database obtained from the analyses. The R2 value has been improved from 0.9549 to 0.9995 by trying different regression equations in Mach and altitude dependent SFC modelling. On the other hand, the R2 value of the SFC model depending on the BPR and CCET was increased from 0.7947 to 0.9706. According to these results, it is observed that engine performance parameters can be modelled with low error values depending on internal and external variables.