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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Framework Development Konstantinos G. Kyprianidis high pressure turbine stress, creep and fatigue analysis using material informa- tion from the TERA2020 common material properties library and the component geometry as designed upstream in the TERA sequence; cooling effectiveness, thermal barrier coating effects, and average T41 values for take-off, cruise, climb, descent, and reverse-thrust operation are considered. Weibull distributions are utilised to account for the uncertainty of other engine components failing, in- cluding the high pressure compressor, combustor, and life-limited parts. The module can also perform risk analysis, to account for uncertainty in various input parameters, but this is an extremely time consuming process. This poten- tial capability of TERA2020 is currently reserved for single engine designs, since it cannot yet be fully exploited for design space exploration because the code executes too slowly. This capability is expected to be further explored in future TERA2020 projects and eventually lead to the removal of the current TERA2020 deterministic analysis limitation and allow for robust design. 2.3.4 Engine design feasibility and optimisation In order to speed up the execution of individual engine designs, TERA2020 attempts to minimise internal iterations in the calculation sequence through the use of the explicit algorithm described in the previous sections. Aero-engine designs however are subject to a large number of constraints and these need to be considered during conceptual design. Constraints in TERA2020 are applied through the optimiser environment proce- dures at the end of the calculation sequence i.e. after the the economics module has been executed. During a numerical optimisation TERA2020 will select a new set of input design parameters for every iteration and the resulting com- bination of aircraft and engine will be assessed. Using user specified objective functions the optimiser will home in on the best engines, determining the ac- ceptability/feasibility of each engine design through the constraints set by the user. Infeasible designs will be ruled out, while non-optimum design values will result in engine designs with non-optimum values for the objective function se- lected. The optimiser will therefore avoid regions in the design pool that result in infeasible or non-optimum engine designs. Design constraints set by the user can include among others: 28

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