Digital multidisciplinary analysis and design optimization platform for aeroderivative gas turbines

由于系统固有的复杂性，燃气轮机设计是一项具有挑战性的任务。用于航改气体 涡轮机（AGT）的设计过程相当分散，并且强烈依赖于从以下来源获得的历史数据 以前的发动机。西门子加拿大 AGT，领先的工业燃气轮机原始设备制造商 （OEM）已确定，当前划分的设计活动会导致次优且不稳健 而设计工程师则将大量时间花在非增值任务上。因此， 它开发了一项技术战略，旨在支持高度集成的并发设计流程 为了提高其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。 该项目的目标是建立一个具有集成功能的模型和数据管理数字平台 分析、设计和优化工具套件。拟议研究的新颖之处之一是调整工具 集成软件工程的最佳实践和变更传播技术，以便构建 集成功能数据管理、模型版本的计算环境的坚实基础 控制、分析工具和优化算法，可根据特定工作流程进行定制 使用机器学习技术的不同工程团队。分析、设计和优化模块 该数字平台将包括关键静态和旋转部件的参数化设计模型， 生命周期相关的量化技术、多学科设计优化 (MDO) 方法以及稳健的 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.