Systematic modeling of architecture design spaces is needed when architecting complex systems, to support experts in making less biased decisions, and to formulate the optimization problem needed to explore the large combinatorial design space. Existing methods do not offer enough compatibility with the Model-Based Systems Engineering (MBSE) approaches, cannot model all needed design scenarios, or are not flexible enough when it comes to architecture evaluation. A new method is presented that provides a semantic representation of the architecture design space, modeled as the Architecture Design Space Graph (ADSG). The ADSG represents three types of architectural decisions: function-component mapping, component characterization, and component connection. The ADSG is constructed from a design space definition, and discrete architectural decisions are automatically inserted according to specified rules. Once decisions and metrics have been defined, the hierarchical, mixed-integer, multi-objective optimization problem can be formulated: decisions are mapped to design variables, and performance metrics are mapped to objectives or constraints. An application of the method to the Apollo mission architecting problem is presented. Nomenclature

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In this paper, a new application for collaborative Multidisciplinary Design Analysis and Optimization (MDAO) workflow modeling is presented. The MDAO Workflow Design Accelerator, short MDAx, enables workflow integrators and disciplinary experts to model, inspect, and explore workflow components and their relationships, and export workflow configurations for execution on integration platforms. The necessity for such an MDAO design environment stems from the inherent complexity in aircraft design, in which the segregation of disciplines on technical and managerial scale result in time intensive workflow integration efforts. In practice, it can be observed that the integration of simulation tools to solve real-life MDAO problems produces large, interconnected workflow systems that lead to a loss in oversight of the application network, lack in transparency due to the many participants, and consistency issues with the resulting models. To facilitate a more effective collaboration among disciplinary experts, MDAx provides an intuitive workflow modeling environment using an expansion of the XDSM (eXtended Design Structure Matrix) format with additional design rules. Various functionalities to automate repetitive design tasks to resolve ambiguities and inconsistencies in complex workflows are provided. Importance is given to fearless workflow design through continuous feedback and user guidance without requiring expert knowledge in MDAO architecting, which shows considerable effects on the removal of barriers in the adoption of existing MDAO paradigms in collaborative teams. This paper introduces MDAx, its founding methodology and implementation, its user interface and effects on usability, and a case study demonstrating its impact on the coordination and communication among collaborators in a realistic design problem. Nomenclature

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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 multiorganization development processes. These processes require data-driven and computersupported 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.

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 multiobjective 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.

Optimization of system architectures can help deal with finding better system architectures in a large design space plagued by combinatorial explosion of alternatives. To enable architecture optimization, the design space should therefore be formalized into a numerical optimization problem, and it should be possible to quantitatively evaluate architecture alternatives. This paper presents a methodology for generating and modeling architecture design spaces using the Architecture Design Space Graph (ADSG), and using collaborative Multidisciplinary Design Analysis and Optimization (MDAO) techniques to evaluate architectures. Collaborative MDAO leverages disciplinary expertise while ensuring that analysis tools exchange data consistently and correctly using a central data schema. The problem solved in this paper is the missing link between architecture optimization and collaborative MDAO: the reflection of generated architectures in the central data schema. It is solved by the authors by mapping architecture components and Quantities of Interest (QOIs) to the central data schema using Data Schema Operations (DSOs). Such a mapping also assists the user in identifying missing or unnecessary disciplinary analysis tools. Three web-based software tools implementing the methodology are presented. Finally, the methodology and tools are demonstrated using the design of a supersonic business jet as an example.

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