Reference number : GC-2017-Q4-00939, amendment number : , agreement type : G, recipient legal name : Kokkolaras, Michael (McGill University)|Kokkolaras, Michael (Université McGill), recipient country : CA, recipient province : nan, recipient city : Quebec|Québec, recipient postal code : nan, program name english : Collaborative Research and Development Grants - Project, program name french : Subventions de recherche et développement coopérative - projet, program purpose english : Gas turbine design is a challenging task because of inherent system complexity. For aero-derivative gas_x000D_
turbines (AGT) the design process is quite fragmented and relies strongly on historical data obtained from_x000D_
previous engines. Siemens Canada AGT, a leading industrial gas turbine original equipment manufacturer_x000D_
(OEM), has determined that currently compartmentalized design activities lead to sub-optimal and non-robust_x000D_
designs while design engineers spend a considerable amount of their time on non-added value tasks. Therefore,_x000D_
it has developed a technology strategy that aims at supporting a highly integrated concurrent design process in_x000D_
order to improve performance and robustness of its products, maximizing thus its competitiveness while_x000D_
minimizing its financial risks._x000D_
The objective of this project is to build a digital platform for models and data management with an integrated_x000D_
suite of analysis, design, and optimization tools. One of the novelties of the proposed research is to adapt tool_x000D_
integration best practices and change propagation techniques from software engineering in order to build the_x000D_
solid foundation of a computational environment that integrates functional data management, model version_x000D_
control, analysis tools and optimization algorithms in a way that is customizable to the specific workflows of_x000D_
different engineering teams using machine learning techniques. The analysis, design and optimization modules_x000D_
of the digital platform will include parametric design models for critical static and rotary components,_x000D_
lifecycle-related quantification techniques, multidisciplinary design optimization (MDO) methods, and robust_x000D_
design tools., program purpose french : Gas turbine design is a challenging task because of inherent system complexity. For aero-derivative gas_x000D_
turbines (AGT) the design process is quite fragmented and relies strongly on historical data obtained from_x000D_
previous engines. Siemens Canada AGT, a leading industrial gas turbine original equipment manufacturer_x000D_
(OEM), has determined that currently compartmentalized design activities lead to sub-optimal and non-robust_x000D_
designs while design engineers spend a considerable amount of their time on non-added value tasks. Therefore,_x000D_
it has developed a technology strategy that aims at supporting a highly integrated concurrent design process in_x000D_
order to improve performance and robustness of its products, maximizing thus its competitiveness while_x000D_
minimizing its financial risks._x000D_
The objective of this project is to build a digital platform for models and data management with an integrated_x000D_
suite of analysis, design, and optimization tools. One of the novelties of the proposed research is to adapt tool_x000D_
integration best practices and change propagation techniques from software engineering in order to build the_x000D_
solid foundation of a computational environment that integrates functional data management, model version_x000D_
control, analysis tools and optimization algorithms in a way that is customizable to the specific workflows of_x000D_
different engineering teams using machine learning techniques. The analysis, design and optimization modules_x000D_
of the digital platform will include parametric design models for critical static and rotary components,_x000D_
lifecycle-related quantification techniques, multidisciplinary design optimization (MDO) methods, and robust_x000D_
design tools., agreement title english : Digital multidisciplinary analysis and design optimization platform for aeroderivative gas turbines, agreement title french : Digital multidisciplinary analysis and design optimization platform for aeroderivative gas turbines, agreement number : CRDPJ, agreement value : 785000.0, agreement start date : 2018-03-07, agreement end date : nan, description english : nan, description french : nan, expected results english : nan, expected results french : nan, additional information english : Grant or Award spanning more than one fiscal year (2017-2018 to 2022-2023)., additional information french : Subvention ou bourse octroyée s'appliquant à plus d'un exercice financier (2017-2018 à 2022-2023)., owner organization : nserc-crsng, owner organization title : Natural Sciences and Engineering Research Council of Canada | Conseil de recherches en sciences naturelles et en génie du Canada