This paper proposes a novel methodology and its software implementation, called KAD-MOS (Knowledge-and graph-based Agile Design for Multidisciplinary Optimization System), which aims at increasing the agility of aircraft design teams that perform collaborative multidisciplinary design analysis and optimization (MDAO) by means of graph manipulation techniques. By agility, the ease and flexibility to assemble, adapt and adjust MDAO computational systems is intended here, as necessary to better fit the iterative nature of the aircraft design process. KADMOS has been developed on the notion that a formal specification of an MDAO system is required before its actual implementation, especially to be able to compose large and complex systems in multidisciplinary design teams. This specification system is under development as part of the EU project AGILE where a new generation of aircraft MDAO systems is investigated to support collaboration of heterogeneous teams of experts. KADMOS improves the agility of the design team in three ways: 1) reducing the setup time required to compose large and complex MDAO models, 2) enabling the systematic inspection and debugging of this model, and 3) manipulating the model for automated creation and reconfiguration of optimization strategies, including the accompanying executable workflow. This is achieved by means of a graph-based analysis system that combines different existing advantageous techniques for performing MDAO, such as the use of a single shared data schema containing a parametric representation of the aircraft, knowledge-based technologies, and simulation workflow (SWF) software packages. Two MDAO case studies will be presented in the paper. The first case study is based on a simple analytical problem, generally used in literature for MDAO benchmarking studies. The second case study concerns a detailed wing aerostructure design using a collection of wing design tools. While the simple and compact analytical problem is used in this paper to demonstrate the functionalities of the tool, the wing design case demonstrates the capability of KADMOS to support quick formulation, (re)configuration, and execution of MDAO workflows using distributed and heterogeneous sets of analysis tools.

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The AGILE project is developing the next generation of development processes, and deploying a collaborative MDO design system, called the AGILE development framework (ADF). Naturally, such a system contains a lot of implicit assumptions on how things should be done and how to exploit different existing technologies. This collection of assumptions and technologies is labeled the 'AGILE Paradigm'. The two main building blocks of this paradigm are the Collaborative Architecture and the Knowledge Architecture. In essence, these building blocks aim to support large, heterogeneous teams of experts in performing collaborative development in a streamlined and time-effective way. This paper has a focus on the definition of the Knowledge Architecture (KA) as a general conceptual framework which is independent of the aircraft-specific application in AGILE. The KA can be applied to perform collaborative automated design in large, heterogeneous teams for any complex system (e.g. aircraft, automobiles, wind farms). The KA is structured with a multi-level backbone: Development Process layer, Automated Design layer, Design Competence layer. A fourth transverse layer impacting all other layers is the Data & Schemas layer. Each layer has its own set of assumptions and technologies, but more importantly, interfaces between the levels have to be created in order to have a fully interconnected development process from each design competence up to the top-level business process. The hierarchical levels and interfaces are described in this paper as a generalized paradigm. In addition, four support platforms of the KA in the AGILE project are described in more detail: the development process environment, graph-based support in the design problem formulation, visualization of large, complex automated design processes, and design concepts formalizations. Finally, a use case from the AGILE project is mapped on this paradigm to demonstrate the use of the KA and its support platforms in a realistic design case.

A new system is presented that enables the visualization of large multidisciplinary design optimization (MDO) problems and their solution strategy. It was developed within the scope of the European project AGILE. In AGILE, collaborative MDO is performed in large, heterogeneous teams of experts by solving MDO problems using a collection of design and analysis tools. This paper focuses on the visualizations required to support the formulation phase of an MDO project. The Knowledge and graph-based AGILE Design for Multidisciplinary Optimization System (KADMOS), an open-source MDO support system developed by Delft University of Technology, uses graph-based analysis to formulate an MDO problem and its solution strategy, based on the disciplinary analyses available in a repository. The results of KADMOS are stored in the standardized format CMDOWS (Common MDO Workflow Schema), which comprises the entire information on an MDO system. Although, based on Extensible Markup Language, the readability of the CMDOWS file is quite poor also for MDO experts, especially for large MDO systems involving thousands of variables. Providing visualization capabilities to thoroughly inspect the outcome of the different MDO formulation steps becomes a key factor to enable the specification of large MDO systems in a heterogeneous team. Therefore, VISTOMS (VISualization TOol for MDO Systems), a dynamic visualization package, was developed by RWTH Aachen University to enable the visualization and inspection of the different MDO system specification steps, thereby removing one of the main hurdles for using MDO as a development process. The developed visualization capabilities are demonstrated by means of an aerostructural wing design optimization project. Keywords MDO · Visualization · KADMOS · CMDOWS · VISTOMS Abbreviations