Digital multidisciplinary analysis and design optimization platform for aeroderivative gas turbines

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Gas turbine design is a challenging task because of inherent system complexity. For aero-derivative gasturbines (AGT) the design process is quite fragmented and relies strongly on historical data obtained fromprevious engines. Siemens Canada AGT, a leading industrial gas turbine original equipment manufacturer(OEM), has determined that currently compartmentalized design activities lead to sub-optimal and non-robustdesigns while design engineers spend a considerable amount of their time on non-added value tasks. Therefore,it has developed a technology strategy that aims at supporting a highly integrated concurrent design process inorder to improve performance and robustness of its products, maximizing thus its competitiveness whileminimizing its financial risks.The objective of this project is to build a digital platform for models and data management with an integratedsuite of analysis, design, and optimization tools. One of the novelties of the proposed research is to adapt toolintegration best practices and change propagation techniques from software engineering in order to build thesolid foundation of a computational environment that integrates functional data management, model versioncontrol, analysis tools and optimization algorithms in a way that is customizable to the specific workflows ofdifferent engineering teams using machine learning techniques. The analysis, design and optimization modulesof the digital platform will include parametric design models for critical static and rotary components,lifecycle-related quantification techniques, multidisciplinary design optimization (MDO) methods, and robustdesign tools.