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。用于航空衍生气体 涡轮机（AGT）的设计过程是相当分散的，并严重依赖于历史数据， 以前的引擎西门子加拿大AGT，领先的工业燃气涡轮机原始设备制造商 (OEM)已经确定，目前划分的设计活动导致次优和非稳健 设计工程师将大量时间花在非增值任务上。因此，我们认为， 它制定了一项技术战略，旨在支持高度集成的并行设计过程， 以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， 最大限度地降低金融风险。 该项目的目标是建立一个模型和数据管理的数字平台， 一套分析、设计和优化工具。拟议研究的新颖之处之一是调整工具 集成最佳实践和变更传播技术，以构建 计算环境的坚实基础，集成了功能数据管理、模型版本 控制，分析工具和优化算法，以可定制的方式，以特定的工作流程， 不同的工程团队使用机器学习技术。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， 生命周期相关的量化技术，多学科设计优化（MDO）方法，和鲁棒性 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.