Digital multidisciplinary analysis and design optimization platform for aeroderivative gas turbines

航改燃气轮机数字化多学科分析与设计优化平台

• 批准号: 513922-2017
• 负责人: Kokkolaras, Michael
• 金额: $ 6.16万
• 依托单位: McGill University
• 依托单位国家: 加拿大
• 项目类别: Collaborative Research and Development Grants
• 财政年份: 2021
• 资助国家: 加拿大
• 起止时间: 2021-01-01 至 2022-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=719343
• 关键词: Digital; multidisciplinary; analysis; design; optimization

Gas turbine design is a challenging task because of inherent system complexity. For aero-derivative gasturbines (AGT) the design process is quite fragmented and relies strongly on historical data obtained fromprevious 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-robustdesigns 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 inorder to improve performance and robustness of its products, maximizing thus its competitiveness whileminimizing its financial risks.The objective of this project is to build a digital platform for models and data management with an integratedsuite of analysis, design, and optimization tools. One of the novelties of the proposed research is to adapt toolintegration best practices and change propagation techniques from software engineering in order to build thesolid foundation of a computational environment that integrates functional data management, model versioncontrol, analysis tools and optimization algorithms in a way that is customizable to the specific workflows ofdifferent engineering teams using machine learning techniques. The analysis, design and optimization modulesof the digital platform will include parametric design models for critical static and rotary components,lifecycle-related quantification techniques, multidisciplinary design optimization (MDO) methods, and robustdesign tools.

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。对于航改燃气涡轮发动机（AGT），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公司是一家领先的工业燃气涡轮机原始设备制造商（OEM），该公司已确定，目前划分的设计活动导致次优和非稳健设计，而设计工程师将大量时间花在非增值任务上。因此，该公司制定了一项技术战略，旨在支持高度集成的并行设计过程，以提高产品的性能和鲁棒性，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数字化平台，用于模型和数据管理，并集成了一套分析，设计和优化工具。所提出的研究的新颖性之一是适应工具集成最佳实践和软件工程的变化传播技术，以建立一个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，其方式是可定制的，以适应不同工程团队的特定工作流程，使用机器学习技术。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、与寿命相关的量化技术、多学科设计优化（MDO）方法和稳健设计工具。