DNS and Visual Analysis of Superstructures in Turbulent Channels with Mixing by Parallel Injection

并行注入混合湍流通道中上层建筑的 DNS 和可视化分析

基本信息

项目摘要

In order to analyze the occurrence and the impact of superstructures onturbulent mixing in channels at high Reynolds numbers with a parallel injection,a combination of Direct Numerical Simulation (DNS), vortexdefinition and identification, and feature-based visualization, isproposed. Standard, off-the-shelf solutions are not available for thispurpose.Concerning DNS, the central issue is to access high Reynolds numberswith excellent efficiency on HPC systems. Additionally, suitablemodels must be included to describe numerically all fluidproperties relevant for mixing.The main challenge in vortex extraction is three-fold. Firstly,high-intensity turbulence excludes standard vortex definitions that arebased on a local analysis of the flow derivatives. Instead, global,Lagrangian, or hierarchical vortex definitions are necessary that arebased on filtering operations on the flow map instead of the velocityfield. Secondly, vortex definitions and parameter tuning has to beadapted such that it does not focus on upstream vorticesclose to injection but tackles the less obvious, noisierand more unsteady vortex structures downstream. Thirdly, in terms ofvisual analysis, the main challenge is associated with the sheer size ofthe data sets: DNS typically delivers data sets that cannot becompletely stored during the simulation. Hence, on-the-fly solutions forthe visual analysis are necessary.To analyze the phenomena, DNS, vortex extraction and visualization haveto be combined into a feedback cycle in a computational steering sense.While a multi-scale POD along with an automatic vortex extraction is carried outon-the-fly, the resulting vortices are later visually analyzed in an interactive manner,allowing adaptation of both the visualization parameters and further simulationparameters. This efficient combination of DNS, POD, and visual analysis shall allow the identification of superstructures and help explain their impact on transport processes.
为了分析高雷诺数平行喷射通道中上部结构的发生及其对湍流混合的影响,提出了直接数值模拟(DNS)、涡定义和识别以及基于特征的可视化相结合的方法。标准的、现成的解决方案无法用于此目的。关于DNS,核心问题是在高性能计算系统上以优异的效率访问高雷诺数。此外,必须包括合适的模型来数值描述与混合有关的所有流体特性。涡旋提取的主要挑战有三个方面。首先,高强度湍流排除了基于流动导数局部分析的标准涡定义。相反,全局的、拉格朗日的或分层的涡定义是必要的,它们基于流图上的过滤操作,而不是速度场。其次,涡定义和参数调整必须适应,这样它就不会集中在靠近注入的上游涡,而是处理下游不太明显、噪音更大、更不稳定的涡结构。第三,在可视化分析方面,主要的挑战与数据集的大小有关:DNS通常提供的数据集在模拟期间不能完全存储。因此,为可视化分析提供实时解决方案是必要的。为了分析这些现象,必须将DNS、漩涡提取和可视化结合到一个计算导向意义上的反馈循环中。在实时进行多尺度POD和自动涡提取的同时,随后以交互式方式对所得到的涡进行可视化分析,从而允许对可视化参数和进一步的仿真参数进行调整。DNS、POD和可视化分析的有效结合将有助于识别上层建筑,并帮助解释它们对运输过程的影响。

项目成果

期刊论文数量(0)
专著数量(0)
科研奖励数量(0)
会议论文数量(0)
专利数量(0)

数据更新时间:{{ journalArticles.updateTime }}

{{ item.title }}
{{ item.translation_title }}
  • DOI:
    {{ item.doi }}
  • 发表时间:
    {{ item.publish_year }}
  • 期刊:
  • 影响因子:
    {{ item.factor }}
  • 作者:
    {{ item.authors }}
  • 通讯作者:
    {{ item.author }}

数据更新时间:{{ journalArticles.updateTime }}

{{ item.title }}
  • 作者:
    {{ item.author }}

数据更新时间:{{ monograph.updateTime }}

{{ item.title }}
  • 作者:
    {{ item.author }}

数据更新时间:{{ sciAawards.updateTime }}

{{ item.title }}
  • 作者:
    {{ item.author }}

数据更新时间:{{ conferencePapers.updateTime }}

{{ item.title }}
  • 作者:
    {{ item.author }}

数据更新时间:{{ patent.updateTime }}

Professor Dr.-Ing. Holger Theisel其他文献

Professor Dr.-Ing. Holger Theisel的其他文献

{{ item.title }}
{{ item.translation_title }}
  • DOI:
    {{ item.doi }}
  • 发表时间:
    {{ item.publish_year }}
  • 期刊:
  • 影响因子:
    {{ item.factor }}
  • 作者:
    {{ item.authors }}
  • 通讯作者:
    {{ item.author }}

{{ truncateString('Professor Dr.-Ing. Holger Theisel', 18)}}的其他基金

Gradient-Preserving Cuts for Scalar Representations of Vector Fields
矢量场标量表示的梯度保持切割
  • 批准号:
    418328199
  • 财政年份:
    2018
  • 资助金额:
    --
  • 项目类别:
    Research Grants
Steadification of Unsteady Vector Fields for Flow Visualization
用于流可视化的不稳定矢量场的稳定化
  • 批准号:
    309227598
  • 财政年份:
    2016
  • 资助金额:
    --
  • 项目类别:
    Research Grants
Multitype Multifield Visualization
多类型多场可视化
  • 批准号:
    271732629
  • 财政年份:
    2015
  • 资助金额:
    --
  • 项目类别:
    Research Grants
On-the-fly postprocessing and feature extraction of flame and flow properties obtained by Direct Numerical Simulations
通过直接数值模拟获得的火焰和流动特性的动态后处理和特征提取
  • 批准号:
    250921653
  • 财政年份:
    2014
  • 资助金额:
    --
  • 项目类别:
    Research Grants
Sharp Ridge Structures in Flow Visualization
流动可视化中的尖锐脊结构
  • 批准号:
    224905231
  • 财政年份:
    2013
  • 资助金额:
    --
  • 项目类别:
    Research Grants
Scaling Invariant Multidimensional Projections for Visualization
缩放不变多维投影以实现可视化
  • 批准号:
    509315541
  • 财政年份:
  • 资助金额:
    --
  • 项目类别:
    Research Grants

相似国自然基金

基于多幅图象的Visual Hull重构及表面属性建模算法研究
  • 批准号:
    60373031
  • 批准年份:
    2003
  • 资助金额:
    23.0 万元
  • 项目类别:
    面上项目

相似海外基金

Visual analysis system to detect and predict the signs of anxiety in healthcare
用于检测和预测医疗保健中焦虑迹象的视觉分析系统
  • 批准号:
    2902083
  • 财政年份:
    2024
  • 资助金额:
    --
  • 项目类别:
    Studentship
Functional analysis of an LGN-based visual prosthesis
基于 LGN 的视觉假体的功能分析
  • 批准号:
    10582766
  • 财政年份:
    2023
  • 资助金额:
    --
  • 项目类别:
Development of data generation and analysis methods for quantitative evaluation of visual search behavior in soccer
开发用于定量评估足球视觉搜索行为的数据生成和分析方法
  • 批准号:
    23K10626
  • 财政年份:
    2023
  • 资助金额:
    --
  • 项目类别:
    Grant-in-Aid for Scientific Research (C)
Development of a New Automatic Visual Acuity Testing Device Using Eye-Tracking Analysis for Infants
利用婴儿眼动追踪分析开发新型自动视力测试设备
  • 批准号:
    23K02275
  • 财政年份:
    2023
  • 资助金额:
    --
  • 项目类别:
    Grant-in-Aid for Scientific Research (C)
Investigation of the cause of computer elbow that occurs in visual display terminals workers using a three-dimensional motion analysis device
利用三维运动分析装置调查视觉显示终端工人电脑肘的原因
  • 批准号:
    23K03750
  • 财政年份:
    2023
  • 资助金额:
    --
  • 项目类别:
    Grant-in-Aid for Scientific Research (C)
Collaborative Research: HCC: Medium: Modeling and Mitigating Confirmation Bias in Visual Data Analysis
合作研究:HCC:媒介:可视化数据分析中的建模和减轻确认偏差
  • 批准号:
    2311575
  • 财政年份:
    2023
  • 资助金额:
    --
  • 项目类别:
    Standard Grant
Collaborative Research: HCC: Medium: Modeling and Mitigating Confirmation Bias in Visual Data Analysis
合作研究:HCC:媒介:可视化数据分析中的建模和减轻确认偏差
  • 批准号:
    2311574
  • 财政年份:
    2023
  • 资助金额:
    --
  • 项目类别:
    Standard Grant
Precise analysis of eye movement and development of vision training method for improving dynamic visual acuity
精准分析眼动并开发提高动态视力的视觉训练方法
  • 批准号:
    23K10668
  • 财政年份:
    2023
  • 资助金额:
    --
  • 项目类别:
    Grant-in-Aid for Scientific Research (C)
Integrated functional and structural analysis of an entire column in mouse primary visual cortex
小鼠初级视觉皮层整个柱的综合功能和结构分析
  • 批准号:
    10505417
  • 财政年份:
    2022
  • 资助金额:
    --
  • 项目类别:
A multimodal semiotic analysis of online "prepper" communities through visual grounded theory methodology, combined with a quantitative hierarchal clu
通过视觉扎根理论方法,结合定量层次分析,对在线“末日准备者”社区进行多模态符号学分析
  • 批准号:
    2750561
  • 财政年份:
    2022
  • 资助金额:
    --
  • 项目类别:
    Studentship
{{ showInfoDetail.title }}

作者:{{ showInfoDetail.author }}

知道了