Abstract

To create potential solutions for the increasing demands of the community on future air vehicle configurations, a systematic and parallel consideration of a large amount of disciplines is required. To foster collaboration between heterogeneous knowledge bearers, the German Aerospace Center (DLR) has continuously increased its effort in the digitalization and automation of engineering knowledge over the last decade. In pursuing the goal of developing enhanced virtual aircraft configurations, the underlying design and integration methods have been matured and extensively tested, nowadays resulting in loosely-coupled multidisciplinary simulation workflows encompassing a steadily increasing number of tools. Within the dedicated, decentralized network of competences, these tools stem from within DLR and across company borders. Although successfully applied to combine the required knowledge within a series of projects both within DLR and within EU-wide consortia, further improvement opportunities have been identified. This paper provides a general overview of the developments and encountered pitfalls concerning collaborative multidisciplinary simulation for aircraft preliminary design thus far. Based on the gained experience, opportunities for increasing the transparency, further flexibilization and time-reduction of the design process are identified and discussed. Although a link to the current development of model-based systems engineering (MBSE) techniques for streamlining the orchestration of the design process will be shown, this paper focuses on the development of simulation processes for multidisciplinary design and optimization (MDO). To enable the interconnection of established software tools of the heterogeneous specialists within the design process, these are wrapped to a central data exchange format and made batch-executable. The DLR-established data format “Common Parametric Aircraft Configuration Schema (CPACS)” provides the common language for standardized parameter exchange and allows for a considerable reduction in the number of modeled interfaces between the tools. The automated tools are provided as engineering services, hosted on dedicated servers at the respective organizations. These are integrated into executable simulation workflows within the Remote Component Environment (RCE), a process integration software. Setting-up such workflows has initially been done using manually executed methods from the field of systems engineering. An example is the manual creation of design structure matrices to identify engineering service dependencies. Using the latest developments, workflow creation is automated using the MDAO workflow Design Accelerator software (MDAx), utilizing techniques for obtaining an efficient routing of parameters through the engineering services. Additionally, the network of competences has been considerably extended by enabling the automated, cross-company exchange of information within the aircraft design community. Currently, multi-tier design processes, in which knowledge models are shared as black-boxes or on the form of response surface models, are established to create and analyze promising technologies to be embedded in future aircraft system architectures. Currently, the simulation of future hybrid-electric configurations using well-organized simulation workflow orchestration techniques is at the heart of the design studies performed. What will future design methodologies look like? Increased effort is put in developing methods to create novel knowledge-based engineering services, which can be flexibly adjusted according to the needs of the design team. To achieve this, a framework is constructed which combines ontology-, inference- and rule-based modeling techniques. Next to improving the way in which engineering services are created, an increased amount of result data is being generated. Furthermore, the availability of real-life physics-based data through the adoption of digital twins is close to realization. Research effort is invested in finding ways to cope with the increased amount of data. Can a proper combination of data driven analysis with physics-based models provide a solid basis for improving the decision-making process?