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Multi-disciplinary conceptual design of future jet engine systems

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Multi-disciplinary conceptual design of future jet engine systems ( multi-disciplinary-conceptual-design-future-jet-engine-syste )

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Konstantinos G. Kyprianidis System uncertainty due to thermo-fluid modelling tends to give the illusion that the overall system calculation will not be affected much. In reality however, the error in the prediction of the compressor delivery temperature will affect significantly the accuracy of the fuel mass flow prediction - and therefore other important performance parameters such as Specific Fuel Consumption (SFC) and block fuel estimations. Furthermore, Kurzke [5] con- cludes that for achieving high temperatures one needs over-proportional amounts of fuel, and that is the reason for the maximum thermal efficiency being at a tem- perature much lower than the stoichiometric limit. The fact that fuel mass flow, and subsequently Fuel to Air Ratio (FAR), is not proportional to the combustor temperature increase can only be taken fully into account if rigorous fluid mod- elling is used in the combustor component calculations. Lee et al. [195] also come to similar conclusions for gas turbine configurations with multiple combustors. It can therefore be concluded that the fully rigorous approach should be used in all performance calculations, even within the educational procedure. 4.4 Computational time considerations Estimating the performance of a gas turbine engine at aircraft system level, for a long range mission, requires a relatively small amount of computing power. How- ever, the required computational time for design space exploration applications - through the use of multi-disciplinary tools such as TERA2020 - is not negli- gible and the fluid model must therefore be chosen carefully. Tables of caloric properties where seemingly found to be a good choice with respect to reducing computational speed. Computational speed decreased by some 10% if the Walsh and Fletcher 8th order polynomials were used and some 20% if CHEMKIN-II type of libraries of 4th order polynomials were used. For the latter choice, only five species were taken into account: N2, O2, Ar, H2O, and CO2. It can be ar- gued however, with high confidence, that these benchmarking results are highly dependent on programming practices and as a result on the quality of the code produced. A conclusion therefore cannot be drawn on whether using fluid prop- erties or polynomials is best practice with respect to computational speed. Nevertheless, following further analysis, it was safely concluded that when us- ing a CHEMKIN-II type of library (or equivalent) the computational speed is inversely proportional to the number of species taken into consideration. As a rule of thumb, doubling the number of species taken into consideration will 87

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