Development of loss and noise models for fan design based on high fidelity simulations

基于高保真度仿真的风扇设计损耗和噪声模型的开发

• 批准号: RGPIN-2020-05919
• 负责人: Sanjose, Marlene
• 金额: $ 1.97万
• 依托单位: École de technologie supérieure
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2022
• 资助国家: 加拿大
• 起止时间: 2022-01-01 至 2023-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=754506
• 关键词: Development; loss; noise; models; fan

The main objective of this research proposal is to advance knowledge in the design and installation of turbomachines, to increase their efficiency and lower their acoustic emissions. Widely used in the fields of energy production, aircraft propulsion, ventilation and processes, turbomachines are the key components of many engineering solutions developed to adapt to future challenges involving sustainability and mobility. Canada is undergoing major changes with digital technologies driving innovative concepts in the Industry 4.0 framework. A rapid evolution towards more efficient and versatile rotating machines is needed to equip future light flying vehicles and to respond to on-demand operation and various loads in ventilation or energy production. Social acceptability is also a challenge for new concepts and in particular noise emissions in urban and commercial areas are to be considered at early stages of their development. The flow in a turbomachine is intrinsically unsteady, characterized by strong interactions of secondary flow features with the main stream flow and with the fixed and rotating components of the machine. These interaction mechanisms yield substantial performance losses and noise emissions, that must be taken into account in advanced design tools. The planned research activities will focus on the mechanisms related to (i) boundary layer development on the rotor blades and (ii) the tip leakage vortical structures in the tip clearance. Several configurations with increasing complexity from isolated fixed airfoil to rotating turbofan blade will be simulated using a high-fidelity computational approach. This approach will make it possible to capture the complex turbulent flow structures both in time and space with a minimal use of turbulent closure modelling. The projected results will provide unique and large databases for the development of wall-pressure fluctuation and tip clearance entropic loss models. They will also provide detailed insights as to the interaction mechanisms in the tip gap responsible for both instability and the noise generation. To build improved models, modern machine learning techniques will be used. In the long term, the knowledge acquired will improve classical pre-design tools for turbomachines to extend their range of validity, provide quantitative and parametric performance predictions and reduce noise emissions.

该研究计划的主要目标是增进涡轮机设计和安装方面的知识，提高其效率并降低声发射。涡轮机广泛应用于能源生产、飞机推进、通风和工艺领域，是许多工程解决方案的关键组成部分，这些解决方案是为适应涉及可持续性和流动性的未来挑战而开发的。加拿大正在经历重大变革，数字技术推动了工业 4.0 框架中的创新概念。需要向更高效和多功能的旋转机器快速发展，以装备未来的轻型飞行器并响应按需运行和通风或能源生产中的各种负载。社会可接受性也是新概念的一个挑战，特别是在其发展的早期阶段应考虑城市和商业区的噪音排放。涡轮机中的流动本质上是不稳定的，其特征是二次流动特征与主流流动以及与机器的固定和旋转部件的强烈相互作用。这些相互作用机制会产生大量的性能损失和噪声排放，在先进的设计工具中必须考虑到这一点。计划的研究活动将集中于与（i）转子叶片上的边界层发展和（ii）叶尖间隙中的叶尖泄漏涡流结构相关的机制。从孤立的固定翼型到旋转涡轮风扇叶片，几种复杂性不断增加的配置将使用高保真计算方法进行模拟。这种方法将能够以最少的湍流闭合建模的方式捕获时间和空间上的复杂湍流结构。预计结果将为壁压波动和叶尖间隙熵损失模型的开发提供独特的大型数据库。他们还将提供有关尖端间隙中导致不稳定和噪声产生的相互作用机制的详细见解。为了构建改进的模型，将使用现代机器学习技术。从长远来看，所获得的知识将改进涡轮机的经典预设计工具，以扩大其有效性范围，提供定量和参数性能预测并减少噪声排放。