Digital multidisciplinary analysis and design optimization platform for aeroderivative gas turbines

燃气涡轮机的设计是一个具有挑战性的任务，因为固有的系统的复杂性。对于航改燃气涡轮（AGT），设计过程相当分散，并且强烈依赖于从以前的发动机获得的历史数据。Siemens Canada AGT是一家领先的工业燃气涡轮机原始设备制造商 **（OEM），它已确定，目前的划分设计活动导致设计不理想和不稳健 **，而设计工程师将大量时间用于非增值任务。因此，** 它制定了一项技术战略，旨在支持高度集成的并行设计流程，** 以提高产品的性能和稳健性，从而最大限度地提高其竞争力，同时 ** 最大限度地降低其财务风险。**该项目的目标是构建一个数字平台，用于模型和数据管理，并提供一套集成的分析，设计和优化工具。拟议研究的新颖之处之一是适应软件工程中的工具集成最佳实践和更改传播技术，以构建计算环境的坚实基础，该计算环境集成了功能数据管理，模型版本控制，分析工具和优化算法，可以使用机器学习技术定制不同工程团队的特定工作流程。数字平台的分析、设计和优化模块 ** 将包括关键静态和旋转部件的参数化设计模型、** 与生命周期相关的量化技术、多学科设计优化（MDO）方法和稳健的 ** 设计工具。

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.