Feature modelling as a new architectural approach for the accelerated integration and early-stage development of hybrid-electric propulsion, MEA and MEE architectures
Sztykiel, Michal and Norman, Patrick and Galloway, Stuart and Burt, Graeme (2019) Feature modelling as a new architectural approach for the accelerated integration and early-stage development of hybrid-electric propulsion, MEA and MEE architectures. In: MEA2019 (More Electric Aircraft), 2019-02-06 - 2019-02-07, Diagora, Centre de Congrès et d’Exposition.
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Abstract
This paper presents a feature modelling philosophy and the developed tool, which can be used to support the conceptual design of future generation aircraft electrical architectures. The proposed feature model has been developed in FeatureIDE and consists of four sequential modelling stages, which are programmed to automatically narrow the selection of feasible design options in order to accelerate the complete system specification and documentation. The paper illustrates the feature modelling process, which starts from the identification of high-level aircraft and engine characteristics, considers medium-level electrical architecture layout options, before finally considering the detailed low-level characteristics of individual components.
ORCID iDs
Sztykiel, Michal ORCID: https://orcid.org/0000-0002-0210-313X, Norman, Patrick ORCID: https://orcid.org/0000-0001-5577-1281, Galloway, Stuart ORCID: https://orcid.org/0000-0003-1978-993X and Burt, Graeme ORCID: https://orcid.org/0000-0002-0315-5919;-
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Item type: Conference or Workshop Item(Paper) ID code: 67063 Dates: DateEvent6 February 2019Published12 October 2018AcceptedSubjects: Technology > Electrical engineering. Electronics Nuclear engineering Department: Faculty of Engineering > Electronic and Electrical Engineering
Strategic Research Themes > Measurement Science and Enabling Technologies
Strategic Research Themes > EnergyDepositing user: Pure Administrator Date deposited: 21 Feb 2019 15:20 Last modified: 11 Nov 2024 16:57 Related URLs: URI: https://strathprints.strath.ac.uk/id/eprint/67063