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This paper presents a process for automated start value generation for gas turbine performance simulations using Kriging metamodels. The metamodels are trained on a small number of pre-selected operating points in the flight envelope. Predictions of the trained metamodels are used as initialization parameters for subsequent performance simulations of arbitrary operating points in order to increase robustness and computational speed of the numerical process. Different approaches for the selection of the training points are evaluated. A comparison to the classical approach of table-based initialization is carried out to highlight the advantages and disadvantages of the new methodology. Furthermore, the inclusion of supplementary operating points into the training sample of the metamodels is analyzed. Depending on certain criteria, such as the difference of prediction and simulation result, operating points are included into the sample and a retraining of the metamodels is performed. Simulations using the retrained models as guess value generators are compared to the previous Kriging approach. The advantages and disadvantages of the retraining approach are discussed.

Multidisciplinary preliminary engine design involves a large number of tools and participants. The highly iterative and multidisciplinary workflows make it difficult to evaluate the influence of individual design parameters on the final result. With high-dimensional parameter studies the design space of the system can be investigated. However, the evaluation of the results can rapidly become very challenging. The screening method of elementary effects according to Morris as a method of global sensitivity analysis (GSA) can provide useful insights from even a small number of evaluations. Especially a reduction of the dimensionality due to factor fixing of input parameters with low influence on the problem and factor prioritization of important parameters are addressed by GSA. The purpose of the paper is to demonstrate the application of the method by means of a coupled process which includes performance calculations of the entire engine system and preliminary design of a high-pressure compressor. The variation of input parameters of the different disciplines will be investigated and it will be shown that the Morris screening characteristics allow the evaluation of parameters within the design space in particular with regard to a classification of the significance for the result variables. This shows in the application example that in the comparison parameters with a clear influence emerge from both disciplines. This work is licensed under a Creative Commons Attribution-NonCommercia 4.0 International License CC-BY-NC 4.0.

An essential aspect of the collaborative multidisciplinary preliminary design process of aircraft engines is the involvement of a large number of experts from different disciplines and the usage of numerous tools and workflows. This is a major challenge, especially in the area of data management, as large amounts of data are generated that have to be exchanged and traced via a multitude of interfaces. The purpose of the paper is to present an approach of a central data model as a vehicle for data management in collaborative aircraft engine preliminary design. The first part describes the general structure and abstract definition of the developed data model utilizing modeling languages. Subsequently, a detailed description of the geometric parameterization concerning important engine components and additional data structures follows. Besides the methodology, the software implementation to support this approach is presented in detail, including data serialization and data identification, as well as automated capturing and storage of provenance data throughout the design process. The functionality of the approach is demonstrated by means of examples derived from the preliminary engine design.