This paper presents the collaborative model-based design of a business jet family. In family design, a trade-off is made between aircraft performance, reducing fuel burn, and commonality, reducing manufacturing costs. The family is designed using Model-Based Systems Engineering (MBSE) methods developed in the AGILE 4.0 project. The EC-funded AGILE 4.0 project extends the scope of the preliminary aircraft design process to also include systems engineering phases and new design domains like manufacturing, maintenance, and certification. Stakeholders, needs, requirements, and architecture models of the business jet family are presented. Then, the collaborative Multidisciplinary Design Analysis and Optimization (MDAO) capabilities are used to integrate various aircraft design disciplines, including overall aircraft design, onboard systems design, wing structural sizing, tailplane sizing, mission analysis, and cost estimation. Decisions regarding the degree of commonality are implemented by optionally fixing the design of a shared component when sizing an aircraft.@en

View Video Presentation: https://doi-org.tudelft.idm.oclc.org/10.2514/6.2021-3078.vid

Decisions regarding the system architecture are important and taken early in the design process, however suffer from large design spaces and expert bias. Systematic design space exploration techniques, like optimization, can be applied to system architecting. Realistic engineering benchmark problems are needed to enable development of optimization algorithms that can successfully solve these black-box, hierarchical, mixed-discrete, multi-objective architecture optimization problems. Such benchmark problems support the development of more capable optimization algorithms, more suitable methods for modeling system architecture design space, and educating engineers and other stakeholders on system architecture optimization in general. In this paper, an engine architecting benchmark problem is presented that exhibits all this behavior and is based on the open-source simulation tools pyCycle and OpenMDAO. Next to thermodynamic cycle analysis, the proposed benchmark problem includes modules for the estimation of engine weight, length, diameter, noise and NOx emissions. The problem is defined using modular interfaces, allowing to tune the complexity of the problem, by varying the number of design variables, objectives and constraints. The benchmark problem is validated by comparing to pyCycle example cases and existing engine performance data, and demonstrated using both a simple and a realistic problem formulation, solved using the multi-objective NSGA-II algorithm. It is shown that realistic results can be obtained, even though the design space is subject to hidden constraints due to the engine evaluation not converging for all design points.@en