Automation & integration of secondary air system workflow for multidisciplinary design optimization of gas turbines

Abstract

This thesis presents the automation and integration of Secondary Air System (SAS) tools into a Design & Analysis (D&A) platform developed in a Multidisciplinary Design Optimization (MDO) context. As technology increasingly requires high precision, and therefore meticulous work in multi-expertise fields, gas turbine engineers and analysts suffer from non-value-added tasks. Among these tasks stand data management, poor data transmission between software, and fastidious pre and post-processing; all of which greatly reduce analysis time and consequently the quality of the final product. The objective of this project was to regroup all gas turbine software into a single platform to allow for automation. Tools are now run in batch mode and the platform is linked to a data management system both of which improve efficiency of the engineering workflow. This platform has been designed and tested for SAS engineers, but can be applied to other disciplines as well. The SAS extracts air from the main gas path in the compressors of gas turbines, which is then used for sealing and cooling in addition to influencing the load on the thrust bearings. SASs are thus necessary for gas turbines to reach high powers. To design a SAS specific platform a careful analysis of the workflow was performed and a list of eligible tasks for (semi-)automation was established and prioritized. The following objectives were achieved: pre and post -processing were semi-automated, and processing was fully automated. The complete automation of the process allowed an entire power-curve calculation to be generated in a single run. Whereas the automation of the post-processing allowed for simplified readouts using a task-dependent synthesis page and for graphs to be automatically created reducing manual plotting. The desirable amounts of design iterations do not occur because of the complex and lengthy calculations. In order to perform iterations and later MDO, the SAS module integrates a feedback loop to the performance module with the integration of SAS bleeds into the performance model to ensure model alignments. The aforementioned functionalities have shown great potential. Thus far, non-value-added tasks have been drastically reduced; consequently, time for analysis has been lengthened. In addition, result accuracy will greatly improve thanks to multi-iteration.

Date	17 Sept 2019
Original language	American English
Awarding Institution	École de technologie supérieure
Supervisor	Saïd Hany Moustapha (Supervisor) & François Garnier (Co-supervisor)