Digital multidisciplinary analysis and design optimization platform for aeroderivative gas turbines

由于燃气轮机系统固有的复杂性，燃气轮机设计是一项具有挑战性的任务。对于航空衍生燃气轮机（AGT）的设计过程是相当分散的，并且强烈依赖于从以前的发动机获得的历史数据。西门子加拿大AGT是一家领先的工业燃气轮机原始设备制造商**(OEM)，该公司已经确定，目前划分的设计活动导致次优和非鲁棒性**设计，而设计工程师将大量时间花在非增值任务上。因此，**开发了一种技术策略，旨在支持**的高度集成并发设计过程，以提高其产品的性能和稳健性，从而最大化其竞争力，同时**将其财务风险降至最低。**该项目的目标是建立一个模型和数据管理的数字平台，并集成**套件的分析、设计和优化工具。本研究的一个新颖之处在于，采用软件工程中的工具集成最佳实践和变更传播技术，以构建一个计算环境的坚实基础，该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，并可使用机器学习技术根据不同工程团队的特定工作流进行定制。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）方法和鲁棒设计工具。

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.