Low-dimensional representation and sensor based estimation of bluff body wakes and vortex induced vibrations

钝体尾流和涡激振动的低维表示和基于传感器的估计

• 批准号: RGPIN-2022-03848
• 负责人: Morton, Christopher
• 金额: $ 3.35万
• 依托单位: McMaster University
• 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=759769
• 关键词: Low; dimensional; representation; sensor; based

This proposal is focused on the development of low-dimensional models of turbulent wakes and vortex induced vibrations which are amenable to sensor-based training/estimation for monitoring applications. Major applications of this research include: (i) maintaining the integrity of civil structures and mechanical engineering devices which may undergo significant unsteady cross-flow loading, (ii) monitoring and control of unsteady flows in industrial applications, (iii) the harvesting of unsteady fluid kinetic energy wind and water flows through exploiting flow-induced vibrations. These are of emerging importance in engineering and science due to the need for safe infrastructure and sustainable energy sources. The long-term objective of this research program (10+ years) is to develop a generalizable methodology which enables multi-sensor systems to be trained for real-time predictions and robust deployments. The research program can be divided into two phases. Phase I research is focused on advanced multi-scale decomposition techniques including the time-domain Spectral Proper Orthogonal Decomposition (SPOD) and multiscale Proper Orthogonal Decomposition (mPOD) for turbulent wakes and vortex induced vibrations. The essential hypothesis in these techniques is that it is possible and desirable to control the spectral nature of the data reduction such that dynamically relevant multi-scale phenomena may be represented in distinct modes, enabling an improved interpretation of the physical processes occurring within the flow. Moreover, these approaches may improve the separation of coherent structures from incoherent fluctuations. The goal of Phase I is to experimentally test the aforementioned hypothesis through relevant and practical engineering problems. Phase II research involves sensor-based estimation of turbulent wakes and vortex induced vibrations. The low-dimensional subspace established through SPOD and mPOD within Phase I will serve as the estimation basis. Several techniques including Linear and Quadratic Stochastic Estimation, and more modern approaches using Artificial Neural Networks will be utilized in developing mapping functions between the sensor signals and the desired modes to be estimated. The significant experimental component of the research program will be carried out in the applicants operational research water tunnel facilities which are equipped with state-of-the-art flow diagnostic equipment. The research program will contribute fundamental advancement in physical interpretation and modeling of turbulent wakes and vortex induced vibrations in practical engineering applications and support the development of sensor-based estimation techniques for these flows. The demonstrated generalizability of the developed flow modeling and sensor-based estimation techniques will be a catalyst for industry partners in Canada to implement these techniques for multi-sensor system monitoring (safety, integrity) and control.

本建议的重点是发展湍流尾迹和涡激振动的低维模型，这些模型适用于基于传感器的训练/估计，用于监测应用。本研究的主要应用包括：(1)维护可能承受重大非定常横流载荷的土木结构和机械工程装置的完整性；(2)在工业应用中监测和控制非定常流动；(3)利用流致振动收集非定常流体动能风和水流。由于需要安全的基础设施和可持续的能源，这些在工程和科学中越来越重要。该研究计划的长期目标（10年以上）是开发一种可推广的方法，使多传感器系统能够进行实时预测和稳健部署的训练。研究计划可分为两个阶段。第一阶段主要研究湍流尾迹和涡激振动的时域谱固有正交分解（SPOD）和多尺度固有正交分解（mPOD）等先进的多尺度分解技术。这些技术的基本假设是，控制数据缩减的频谱性质是可能的，也是可取的，这样动态相关的多尺度现象就可以用不同的模式来表示，从而能够更好地解释流中发生的物理过程。此外，这些方法可以改善相干结构与非相干波动的分离。第一阶段的目标是通过相关的实际工程问题对上述假设进行实验验证。第二阶段的研究包括基于传感器的湍流尾迹和涡激振动估计。一期通过SPOD和mPOD建立的低维子空间将作为估算依据。包括线性和二次随机估计在内的几种技术，以及使用人工神经网络的更现代的方法，将用于开发传感器信号与待估计的期望模式之间的映射函数。该研究计划的重要实验部分将在申请人的运营研究水洞设施中进行，该设施配备了最先进的流量诊断设备。该研究项目将在实际工程应用中对湍流尾迹和涡激振动的物理解释和建模做出根本性的贡献，并支持基于传感器的流估计技术的发展。开发的流量建模和基于传感器的估计技术的通用性将成为加拿大行业合作伙伴实施这些技术的催化剂，用于多传感器系统监测（安全性、完整性）和控制。