Buoyancy Driven Turbulence Beyond Self-Similar Equilibrium

超越自相似平衡的浮力驱动湍流

基本信息

项目摘要

0967672BanerjeeSelf similarity is an important concept that arises in the study of turbulent flows, in which an assumption is often made that memory of initial conditions (ICs) are lost at late time and the turbulence develops to a equilibrium (i.e. self similar) state. However, recent studies have indicated that only special turbulent flows are truly self similar. Experiments and computations in buoyancy driven turbulence have indicated that late time turbulence can be affected by ICs seeded into the flow and memory of these ICs are not lost. This presents a remarkable opportunity to predict and design late time buoyancy driven turbulence that leaves behind the fully developed equilibrium concept, and embrace late time turbulence through prescribed ICs that either enhance, or suppress, or maintain turbulence intensity/structure in these flows. The objective of the proposed research program is to evaluate late-time signatures of ICs that will result in a better understanding of mix dominated buoyancy problems with widespread applications in heat exchangers, chemical reactors, climate dynamics, pollutant dispersion, inertial confinement fusion, and, astrophysical flows. The research includes experiments and simulations. The experimental thrust will involve a novel two wheel high acceleration experiment that uses controlled ICs to experimentally study the late time turbulent material (molecular) mixing for RT flows. High fidelity diagnostics which include a combined fast-Particle Imaging Velocimetry/Planar Laser Induced Fluorescence (f-PIV/PLIF). This will provide detailed data of ICs in each experiment, and field measurements of flow velocities, statistical probability density functions of density, velocity fluctuations and associated turbulence correlations and cross correlations. In addition, implicit Large Eddy Simulations (ILES) are planned to cross-couple with the experiment via validation and verification (V&V) of the simulation and therefore provide additional detailed data sets to study the late-time effect of designed ICs. Intellectual Merits:The intellectual merits of the proposed work involve usage of the experimental and computational data sets to: (a) examine the role of ICs to control the molecular mixing dynamics; (b) provide valuable information on active control of turbulent mixing as occurs in various applications of such flows. In addition, the proposed research is expected to result in new discoveries about the mixing process which can also be used to extend turbulence theory beyond the traditional ideas of Kolmogorov which is related to the existence of inertial range dynamics and small scale universality. Broader Impacts:The proposed research extends well beyond our interest in buoyancy driven flows and incorporates the possibility of systematically designing initial and boundary conditions for passive control of late time turbulence. On the educational front, it will help support two graduate students and will introduce students to cutting edge research ideas in a graduate level course on Turbulent Flows. Undergraduate researchers will be included through the Missouri S&T-OURE (Opportunities for Undergraduate Research Experience) program. The students who will perform the bulk of this research will be actively recruited from underrepresented groups (women and minority) at Missouri S&T through the Women in Science & Engineering (WISE) program on campus. In addition, active collaboration with Los Alamos National Laboratory (LANL) will aid in the potential impact of this work and is expected to result in future research opportunities for graduate students upon completion of their degrees.
自相似是湍流研究中的一个重要概念,在湍流研究中,通常假定初始条件的记忆在后期丧失,湍流发展到一个平衡(即自相似)状态。然而,最近的研究表明,只有特殊的湍流才是真正的自相似。浮力驱动湍流的实验和计算表明,在流动中加入ICs可以影响后期湍流,并且这些ICs的记忆不会丢失。这为预测和设计后期浮力驱动的湍流提供了极好的机会,留下了充分发展的平衡概念,并通过规定的IC来拥抱后期湍流,这些IC要么增强,要么抑制,要么维持这些流动中的湍流强度/结构。拟议的研究计划的目标是评估IC的后期特征,这将导致更好地理解在热交换器、化学反应堆、气候动力学、污染物扩散、惯性约束聚变和天体物理流动中广泛应用的混合主导的浮力问题。研究内容包括实验和仿真两部分。实验推力将包括一个新颖的两轮高加速实验,该实验使用受控IC来实验研究RT流的晚期湍流物质(分子)混合。高保真诊断,包括快速粒子成像测速仪/平面激光诱导荧光(f-PIV/PLIF)。这将提供每个实验中ICs的详细数据,以及流动速度的现场测量、密度、速度波动的统计概率密度函数以及相关的湍流关联和交叉关联。此外,隐式大涡模拟(ILE)计划通过模拟的验证和验证(V&V)与实验交叉耦合,从而提供额外的详细数据集来研究所设计IC的后期效应。智力优势:拟议工作的智力优势涉及使用实验和计算数据集来:(A)检查ICs在控制分子混合动力学方面的作用;(B)提供关于在这类流动的各种应用中发生的湍流混合的主动控制的宝贵信息。此外,这项研究还有望带来关于混合过程的新发现,这些发现也可以用来扩展湍流理论,使之超越Kolmogorov的传统观点,后者与惯性范围动力学和小尺度普适性的存在有关。更广泛的影响:拟议的研究远远超出了我们对浮力驱动流动的兴趣,并纳入了系统地设计被动控制后期湍流的初始和边界条件的可能性。在教育方面,它将帮助支持两名研究生,并将在研究生水平的湍流课程中向学生介绍尖端研究想法。本科生研究人员将通过密苏里州S和T-oure(本科生研究经历机会)计划包括在内。将进行大部分研究的学生将通过校园女性科学与工程(WISE)项目从密苏里州S大学代表人数较少的群体(女性和少数族裔)中积极招募。此外,与洛斯阿拉莫斯国家实验室(LANL)的积极合作将有助于这项工作的潜在影响,并有望在研究生完成学位后为他们带来未来的研究机会。

项目成果

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Arindam Banerjee其他文献

Passive and reactive scalar measurements in a transient high-Schmidt-number Rayleigh–Taylor mixing layer
  • DOI:
    10.1007/s00348-012-1328-y
  • 发表时间:
    2012-06-05
  • 期刊:
  • 影响因子:
    2.500
  • 作者:
    Arindam Banerjee;Lakshmi Ayyappa Raghu Mutnuri
  • 通讯作者:
    Lakshmi Ayyappa Raghu Mutnuri
Integral Closure of Powers of Edge Ideals of Weighted Oriented Graphs
  • DOI:
    10.1007/s40306-024-00558-0
  • 发表时间:
    2024-10-17
  • 期刊:
  • 影响因子:
    0.300
  • 作者:
    Arindam Banerjee;Kanoy Kumar Das;Sirajul Haque
  • 通讯作者:
    Sirajul Haque
AmbientFlow: Invertible generative models from incomplete, noisy measurements
AmbientFlow:来自不完整、噪声测量的可逆生成模型
  • DOI:
  • 发表时间:
    2023
  • 期刊:
  • 影响因子:
    0
  • 作者:
    Varun A. Kelkar;Rucha Deshpande;Arindam Banerjee;M. Anastasio
  • 通讯作者:
    M. Anastasio
Technology acceptance model and customer engagement: mediating role of customer satisfaction
技术接受模型和客户参与:客户满意度的中介作用
Private equity in developing nations
  • DOI:
    10.1057/jam.2008.12
  • 发表时间:
    2008-06-23
  • 期刊:
  • 影响因子:
    1.400
  • 作者:
    Arindam Banerjee
  • 通讯作者:
    Arindam Banerjee

Arindam Banerjee的其他文献

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{{ truncateString('Arindam Banerjee', 18)}}的其他基金

NRT - Stakeholder Engaged Equitable Decarbonized Energy Futures
NRT - 利益相关者参与的公平脱碳能源期货
  • 批准号:
    2244162
  • 财政年份:
    2023
  • 资助金额:
    $ 28.56万
  • 项目类别:
    Standard Grant
Collaborative Research: Physics-Based Machine Learning for Sub-Seasonal Climate Forecasting
合作研究:基于物理的机器学习用于次季节气候预测
  • 批准号:
    2130835
  • 财政年份:
    2021
  • 资助金额:
    $ 28.56万
  • 项目类别:
    Continuing Grant
III: Small: Stochastic Algorithms for Large Scale Data Analysis
III:小型:大规模数据分析的随机算法
  • 批准号:
    2131335
  • 财政年份:
    2021
  • 资助金额:
    $ 28.56万
  • 项目类别:
    Continuing Grant
PFI-TT: Advancing the Technology Readiness of Pylon Fairings for Tidal Turbines
PFI-TT:推进潮汐涡轮机塔架整流罩的技术准备
  • 批准号:
    1919184
  • 财政年份:
    2019
  • 资助金额:
    $ 28.56万
  • 项目类别:
    Standard Grant
III: Small: Stochastic Algorithms for Large Scale Data Analysis
III:小型:大规模数据分析的随机算法
  • 批准号:
    1908104
  • 财政年份:
    2019
  • 资助金额:
    $ 28.56万
  • 项目类别:
    Continuing Grant
Collaborative Research: Physics-Based Machine Learning for Sub-Seasonal Climate Forecasting
合作研究:基于物理的机器学习用于次季节气候预测
  • 批准号:
    1934634
  • 财政年份:
    2019
  • 资助金额:
    $ 28.56万
  • 项目类别:
    Continuing Grant
Towards an improved understanding of tidal turbine dynamics in a turbulent marine environment
提高对湍流海洋环境中潮汐涡轮机动力学的理解
  • 批准号:
    1706358
  • 财政年份:
    2017
  • 资助金额:
    $ 28.56万
  • 项目类别:
    Standard Grant
III: Medium: Collaborative Research: Bayesian Modeling and Inference for Quantifying Terrestrial Ecosystem Functions
III:媒介:协作研究:量化陆地生态系统功能的贝叶斯建模和推理
  • 批准号:
    1563950
  • 财政年份:
    2016
  • 资助金额:
    $ 28.56万
  • 项目类别:
    Continuing Grant
CAREER: Transition to Turbulence and Mixing for Rayleigh Taylor Instability with Acceleration Reversal
职业生涯:加速反转的瑞利泰勒不稳定性过渡到湍流和混合
  • 批准号:
    1453056
  • 财政年份:
    2015
  • 资助金额:
    $ 28.56万
  • 项目类别:
    Standard Grant
BIGDATA: F: DKA: Collaborative Research: High-Dimensional Statistical Machine Learning for Spatio-Temporal Climate Data
BIGDATA:F:DKA:协作研究:时空气候数据的高维统计机器学习
  • 批准号:
    1447566
  • 财政年份:
    2014
  • 资助金额:
    $ 28.56万
  • 项目类别:
    Standard Grant

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