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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 Framework Development The long term overall ambition is that the continuous refinement of TERA2020 algorithms will lead to an independent research tool that can help quantify risks and assess the impact of gas turbine design on the environment, by comparing and helping to rank future technologies and design concepts for civil aviation on a formal and consistent basis. TERA2020 will rely on the developers of new technologies providing data from realistic assessments of their capabilities and attributes, so that the tool can evaluate the costs and benefits at whole engine and whole aircraft level. 2.2.2 Origins of the TERA concept Work in Cranfield University on the development and adaptation of TERA mod- els for mechanical systems can be traced back to the early 90’s. TERA-oriented developments to consider drag and weight were initiated by Vicente [34] in an attempt to study the effect of bypass ratio on commercial aero engines designed for long-range subsonic aircraft. Around the same period, Dilosquer [35] initiated a study on the relationship between long range engines and atmospheric pollution. The full spectrum of this work [36–40], essentially took the TERA approach a step further by introducing the influence of environmental impact and flight routes, in aero engine design and analysis. The research interest soon spread to industrial gas turbine systems. Gayraud [41] identified issues in gas turbine selection for power generation and attempted to address them through techno-economic assessments. His later work [42], focusing on more complex systems, set the base for a decision support system for combined cycle schemes. During the further development of TERA for aero applications, environmental impact assessment continued to remain a key element, as described by Whellens and Singh [43]. Further work on genetic algorithms [44] introduced multidisci- plinary optimization in the TERA armory of available tools and methods. These developments served as the foundation for demonstrating how a TERA approach could assist in the transition from the traditional, human-based conceptual de- sign process to a more automated methodology [45]. 15

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