Multidisciplinary design space exploration: An electric fan thruster component design use case
Creators: name:  PETTER ANDERSSON 
name:   MARCUS LEJON 
name:   SARAVANAN CHIDAMBARANATHAN 
name:   SIVA DASARI 
name:   MASSIMO PANAROTTO 
name:   MAX JACOBSON 
Publisher: name: Proceedings of the Aerospace Europe Conference - EUCASS - CEAS - 2023
Year: 2023
Subjects: KBE
MULTIDISCIPLINARY DESIGN
DESIGN SPACE EXPLORATION
MACHINE LEARNING

description: The aerospace industry strives to explore new and, for the commercial aircraft industry, novel technology such as hydrogen, hybrid and fully electrical propulsion systems. Hence there is an increased need for engineering processes that can explore new and novel designs in an efficient way. This paper reports on work accomplished in one of the use cases in a European research project, Design Exploration Framework using AI for Notional Engines (DEF AINE). The project is in its third and final year and result from previous work has been reported in the ICAS conference in Stockholm 2022. A design space exploration process at GKN Aerospace Engines (GKN AE) is described in a generic form with the phases, \"Set up study\", \"create context models\", \"prepare for analysis\", \"run analysis\" and \"evaluate results\". Different disciplines operates in the phases performing specific task for that area. The effect of the methods and tools developed in the project are demonstrated on a use case targeting Key Performance Indicators (KPI:s) defined by the industry partners included in the project. Here, the prioritized KPI:s include \"design space dimensionality\", \"Lead-time for design update\", \"Design point quality\" and \"Design space sampling quality\". The use case describes a novel electric aircraft engine with a ducted fan under development at GKN AE. The Design Space Exploration (DSE) study include objectives and analysis from several disciplines, e.g. computational Fluid dynamical (CFD), acoustics, Strength Fatigue and producibility that drives cost estimation. The study objective is to investigate the preferred number of vanes or the trade between weight, cost and performance. To improve the KP! \"design space dimensionality\", a highly parametrized CAD model has been developed with the ability to include configurational changes in a Design Space Exploration study such as different types of vane fitting solutions and hub designs with an optional stiffness wall. Design automation and parametrization is enabled by applying Knowledge Based Engineering techniques targeting the KP! \"Lead-time for design update\". In order to perform a more relevant DSE study on a component level there is a need to provide appropriate boundary conditions for the component model. This need is satisfied by including a whole engine model. The whole engine model (WEM) is represented in three different contexts; Value Driven Whole Engine Model, Whole Engine Mechanical Model, as well as a CFD Whole Engine model, where attaching geometry of the component of interest is included to better understand the impact of the design variants. As the number of Fan Outlet Guide Vanes are changed, the position of the interfacing flanges moves and the boundary conditions change. Hence the requirement to have a WEM to supply the updated boundary conditions. The increased number of designs studied generates a vast amount of data that are analysed and managed using AI technologies. The paper concludes that the developed methods and tools within the DEF AINE framework has improved our capability to perform DSE and the results are described with reference to defined KP! :s. Copyright 2023 by Petter Andersson. Posted online by the EU CASS association with permission.