Collaboration is a key enabler for the development of modern aircraft and its systems and components. Because of the highly complex and integrated nature of many aircraft systems, effective collaboration requires well-organized, multi-disciplinary, multi-engineer, and multi-organization development processes. These processes require data-driven and computer-supported tools and methodologies. Collaboration may seem as simple as working together, thereby adopting standards and tools, and freely sharing data, information, and knowledge. However, in the development of complex systems such as aircraft, collaboration is not that straightforward. For example, aircraft engineers across disciplines and organizations commonly face challenges such as firewalls, data and tool heterogeneity, and intellectual property protection. In this paper, we review the collaboration challenges, describe how the EU-funded research project AGILE 4.0 addresses these challenges, and detail the application of, and experiences with, AGILE 4.0’s collaboration-enabling technologies. @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