课题基金 / 基金详情

Measurement and Analysis of Unsteady Flows Using a Lagrangian Framework

Measurement and Analysis of Unsteady Flows Using a Lagrangian Framework
使用拉格朗日框架测量和分析非定常流动
批准号:
RGPIN-2016-03666
负责人:
Rival, David
金额:
$4.23万
依托单位:
依托单位国家:
加拿大
项目类别:
Discovery Grants Program - Individual
财政年份:
2019
资助国家:
加拿大
项目状态:
已结题
起止时间:
2019-01-01 至 2020-12-31

项目摘要

项目成果

Rival, David的其他基金

相似基金

相关文献

中文摘要
翻译
漩涡——强烈旋转的区域——通常被称为“流体运动的肌肉”,因为它们负责质量和动量的传输。此外,涡量的产生、重定向和湮灭的研究在自然界和工程中广泛存在于非定常气动、水动力和血流动力学问题中。无论是小尺度的扑翼飞行,还是大尺度的风力涡轮机叶片失速,了解涡度是如何在物体附近产生并重新定向到尾迹的,对于这类系统的优化和流动控制至关重要。幸运的是,由于目前在相机、激光和处理能力方面的进步,目前正在引入时间分辨、体积成像的新方法。这些新方法被称为“摇箱法”(STB)或层析粒子跟踪测速法(Tomo-PTV),有望打破实验室和现场实验中长期存在的障碍。与现有的基于欧拉的成像技术(如层析粒子图像测速(Tomo-PIV))不同,这些新的拉格朗日方法在超高的播种密度下提供详细的拉格朗日跟踪信息(长路径),在任何给定的时间步长都可以跟踪10万个粒子。这种新方法开辟了一个全新的分析技术范围,从稳定和运动物体附近的压力场提取到尾迹中的直接拉格朗日相干结构(LCS)识别。由于复杂流动的数值模拟方法的发展需要高质量的实验来验证和校准,因此这种新的拉格朗日实验技术有望为这些难以捉摸的问题提供急需的见解。这些具有挑战性的案例包括:具有大范围时空尺度的高雷诺数(湍流)流动;非定常气动与水动力与结构的相互作用;多相和过渡性流动如在心脏中发现;以及大气中的阵风特征。拟议的研究计划将探索这些新的基于拉格朗日的测量技术及其相关的后处理策略的使用,作为深入了解此类流的潜在物理的一种手段。该提案将首先描述Tomo-PTV测量的实施如何可能影响我们在分析这些问题时所采取的方向。特别是,我们将研究网络跟踪粒子及其加速度内容的新配方如何能够提取诸如压力之类的流动特征。对于涡度,我们将研究非定常体附近的测量如何提供涡度产生及其在尾迹中的重新定向,从而确定流动控制和优化的可能新途径。**
英文摘要
Vortices - regions of strong rotation - are often referred to as the 'sinews and muscle of fluid motions' for the very fact that they are responsible for the transport of mass and momentum. Furthermore, the study of vorticity production, reorientation and annihilation are ubiquitous to a broad range of unsteady aerodynamic, hydrodynamic and hemodynamic problems found both in nature and engineering. Whether at the small scales of flapping flight or at large scales of stall on wind-turbine blades, understanding how vorticity is generated near a body and reoriented into the wake is critical towards optimization and flow control in such systems. Fortunately, due to current advances in camera, laser and processing capabilities, new methodologies in time-resolved, volumetric imaging are currently being introduced. These new methods are referred to as 'Shake-The-Box' (STB) or Tomographic Particle Tracking Velocimetry (Tomo-PTV), and are slated to break down long-standing barriers in lab- and field-based experimentation. Unlike existing Eulerian-based imaging techniques such as Tomographic Particle Image Velocimetry (Tomo-PIV), these new Lagrangian approaches provide detailed Lagrangian tracking information (long pathlines) at ultra-high seeding densities, on the order of 100,000 tracked particles at any given time step. This new approach opens up a whole new gamut of analysis techniques from pressure-field extraction near steady and moving bodies to direct Lagrangian Coherent Structure (LCS) identification in wakes. ***Since the development of numerical modelling approaches for complex flows demands high-quality experimentation for their validation and calibration, such new Lagrangian experimental techniques are expected to provide much needed insight into these elusive problems. Examples of such challenging cases include: high Reynolds number (turbulent) flows with a large range of temporal and spatial scales; unsteady aerodynamic and hydrodynamic interactions with structures; multiphase and transitional flows such as are found in the heart; and gust characterization in the atmosphere. The proposed research program will explore the use of these new Lagrangian-based measurement techniques and their associated post-processing strategies as a means to offer insight into the underlying physics of such flows. The proposal will begin by describing how the implementation of Tomo-PTV measurements is likely to shape the direction we take when analyzing these problems. In particular, we will look at how new formulations for networking tracked particles and their acceleration content will enable extraction of flow features such as pressure. With vorticity, we will investigate how measurements near unsteady bodies will provide insight into vorticity production and its reorientation in the wake, thus identifying possible new avenues for flow control and optimization. **
期刊论文(0)
专著(0)
科研奖励(0)
会议论文
Measurement and Analysis of Unsteady Flows Using a Lagrangian Framework
  • 批准号:
    RGPIN-2016-03666
  • 项目类别:
    Discovery Grants Program - Individual
  • 资助金额:
    $4.23万
  • 财政年份:
    2022
  • 负责人:
    Rival, David
  • 依托单位:
Measurement and Analysis of Unsteady Flows Using a Lagrangian Framework
  • 批准号:
    RGPIN-2016-03666
  • 项目类别:
    Discovery Grants Program - Individual
  • 资助金额:
    $4.23万
  • 财政年份:
    2021
  • 负责人:
    Rival, David
  • 依托单位:
I2I Phase I: Testing of real-time aerodynamics sensor system under realistic conditions
  • 批准号:
    567659-2021
  • 项目类别:
    Idea to Innovation
  • 资助金额:
    $9.11万
  • 财政年份:
    2021
  • 负责人:
    Rival, David
  • 依托单位:
Characterizing the dynamics of transition on turbofan blades
  • 批准号:
    523776-2018
  • 项目类别:
    Collaborative Research and Development Grants
  • 资助金额:
    $8.74万
  • 财政年份:
    2021
  • 负责人:
    Rival, David
  • 依托单位:
国内基金
海外基金
Scalable Learning and Optimization: High-dimensional Models and Online Decision-Making Strategies for Big Data Analysis
Intelligent Patent Analysis for Optimized Technology Stack Selection:Blockchain BusinessRegistry Case Demonstration
  • 批准号:
    --
  • 项目类别:
    外国学者研究基金项目
  • 资助金额:
    --
  • 批准年份:
    2024
  • 负责人:
    USHARANI HAREESH GOVINDARA JAN
  • 依托单位:
基于Meta-analysis的新疆棉花灌水增产模型研究
  • 批准号:
    41601604
  • 项目类别:
    青年科学基金项目
  • 资助金额:
    22.0万元
  • 批准年份:
    2016
  • 负责人:
    赵爱琴
  • 依托单位:
大规模微阵列数据组的meta-analysis方法研究
  • 批准号:
    31100958
  • 项目类别:
    青年科学基金项目
  • 资助金额:
    20.0万元
  • 批准年份:
    2011
  • 负责人:
    赵洪雅
  • 依托单位: