Graph-based algorithms and data-driven documents for formulation and visualization of large MDO systems

Abstract

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

CMDOWS: a proposed new standard to store and exchange MDO systems

“…A visualization package to inspect and communicate the workflow schema files produced by the MDO system formulation applications described above can also contribute to decreasing the setup time and definitely increases the trust of the design team in the large, complex automated analysis chain that is being built. Many different forms of visualizations suitable to MDO developments exist, as discussed by Aigner et al [37], including the well-known XDSM (Extended Design Structure Matrix) [38]. Although both KADMOS and InFoRMA provide some static MDO visualization capabilities, in AGILE a dedicated web-based visualization tool, called VISTOMS (VISualization TOol for MDO Systems) has been developed, which can provide various dynamic and scalable visualizations [39] via web pages.…”

“…Many different forms of visualizations suitable to MDO developments exist, as discussed by Aigner et al [37], including the well-known XDSM (Extended Design Structure Matrix) [38]. Although both KADMOS and InFoRMA provide some static MDO visualization capabilities, in AGILE a dedicated web-based visualization tool, called VISTOMS (VISualization TOol for MDO Systems) has been developed, which can provide various dynamic and scalable visualizations [39] via web pages. Collaborative workflows These workflows are the executable instances of the MDO solution strategy produced during the MDO system formulation phase (Fig.…”

A clustered and surrogate-based MDA use case for MDO scenarios in AGILE project

“…The full AGILE knowledge architecture is discussed in [16]. Here, three elements of the architecture have been used to support the creation of the different surrogate models and optimization workflows: the graph-based MDO formulation system KADMOS (Knowledge-and graph-based Agile Design for Multidisciplinary Optimization System) [26], the visualization tool for MDO systems VISTOMS (VISualization TOol for MDO Systems) [27], and the data standard to store multidisciplinary system formulations CMDOWS (Common MDO Workflow Schema) [28].…”

“…Mission performance Cluster This cluster contains the Mission performance tool and uses as inputs, the wing design parameters, the operational weights and the Aerodynamic look up tables to run the full mission and provides the fuel weight. Figure 11 provides the enriched graph [16,27] of the four clusters. One can notice that, each cluster can contain design competences of various partners that will be called through AGILE framework.…”

“…For the IDF formulation, that requires coupling variables as design variables, a range had to be defined in line with the one used in the cluster DOEs. Starting from [27] with KADMOS [26]. Eventually, CMDOWS [28] files of the MDO solution strategy (see Figure 8) were automatically created.…”