Digital technologies open up numerous possibilities and create new business models covering all areas of engineering. The collaborative multi-disciplinary design of aircraft engines is a highly complex and highly iterative process handled by a large number of individuals, tools and workflows. The continuously optimized simulation and workflow techniques allow more and more sophisticated phenomena to be analyzed, but lead to increasing complexity of data structures and growing amount of generated data. Consequently, understanding where a piece of data originates from and how it is connected to other datasets becomes progressively essential. In addition, the question arises as to whether the evaluation of this information can contribute to improving the design process itself.

Data provenance or data lineage refers to the description of origin or source of data and is in computational terms a historical record of the derivation of data that assists in understanding the current record. The information can be used to discover the data flow from its source through various changes, including knowledge about how the data gets transformed along the way, how representation and parameters changed, and who was involved in the change. Further, during process modeling and execution, data provenance can also capture information about process successes, failures, and complications. Standard data provenance models can be used to capture and store provenance data generated in the collaborative multi-disciplinary design process. Subsequently, this data can be analyzed using the ontology and inference mechanisms it provides, allowing implicit information to be identified. In this context, data standardization can be beneficial for recording data processing information as well as for data change propagation. It ensures that a data set can always be compared with other datasets. Furthermore, efficient methods for data identification are indispensable to combine datasets and provenance data.

The purpose of this paper is to examine challenges arising from modeling and execution of collaborative multi-disciplinary workflows in context of aero-engine design and present a methodology for creating and using data provenance models in workflow creation, execution, and result analysis. In addition, an approach for data standardization and data identification based on a central data model as part of the digital representation of the aircraft engine system is presented, including geometric description of relevant components as well as additional data structures such as thermodynamics, aerodynamics, and structural characteristics. Besides the methodology, the software implementation to support the approach is presented in detail, including automated capturing and storage of provenance data, as well as methods for data investigation. In addition the evaluation of the presented methodology is given by means of practical examples.


Usage of Data Provenance Models in Collaborative Multi-Disciplinary Aero-Engine Design

Division / Sponsor:
International Gas Turbine Institute (IGTI)

Paper Proceedings: https://asmedigitalcollection.asme.org/GT/GT2020/volume/84058

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