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 gas turbines (AGT) the design process is quite fragmented and relies strongly on historical data obtained from previous 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-robust designs 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 in order to improve performance and robustness of its products, maximizing thus its competitiveness while minimizing its financial risks. The objective of this project is to build a digital platform for models and data management with an integrated suite of analysis, design, and optimization tools. One of the novelties of the proposed research is to adapt tool integration best practices and change propagation techniques from software engineering in order to build the solid foundation of a computational environment that integrates functional data management, model version control, analysis tools and optimization algorithms in a way that is customizable to the specific workflows of different engineering teams using machine learning techniques. The analysis, design and optimization modules of the digital platform will include parametric design models for critical static and rotary components, lifecycle-related quantification techniques, multidisciplinary design optimization (MDO) methods, and robust design tools.