Scale Interactions in Wall Turbulence: Old Challenges Tackled with New Perspectives

壁湍流中的尺度相互作用：用新视角应对旧挑战

• 批准号: EP/I037938/1
• 负责人: Jonathan Morrison
• 金额: $ 52.58万
• 依托单位: Imperial College London
• 依托单位国家: 英国
• 项目类别: Research Grant
• 财政年份: 2012
• 资助国家: 英国
• 起止时间: 2012 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=EP%2FI037938%2F1
• 关键词: Scale; Interactions; Wall; Turbulence; Old

The need to improve the efficiency of fluid-based systems is now of paramount importance. In experimental aerodynamics, one of the most difficult measurements is an accurate determination of surface friction. Our need to predict it accurately is fundamentally important to the design of efficient systems. Reynolds number similarity is an essential concept in describing the fundamental properties of turbulent wall-bounded flow. Unlike the drag coefficient for bluff bodies, that for a turbulent boundary layer continues to decrease indefinitely with increasing Reynolds number because the small-scale motion near the surface is directly affected by viscosity at any Reynolds number. Therefore Reynolds number similarity is very important in design and is a vital tool for the engineer, who, plied with information from either direct numerical simulations or wind-tunnel tests (or both), may well have to extrapolate over several orders of magnitude in order to estimate quantities such as drag at engineering or even meteorological Reynolds numbers. Perhaps the most well-known example of Reynolds number similarity is the region of log velocity variation (the log law) found in wall-bounded flows which, at sufficiently high Reynolds numbers, exists regardless of the nature of the surface boundary condition or the form of the outer imposed length scale.In wall-bounded flows relevant to practical applications, where the flow is turbulent and the Reynolds number is high, the transport and loss of fluid momentum and energy is not well understood. Consequently, most predictive and modelling methods rely on a variety of assumptions. The two most critical ones are the Law of the Wall (the log law) and Townsend's local-equilibrium hypothesis. Both assumptions implicitly assume that large scales in the flow are weak and that they function independently of the small scales. However, this is clearly not true, especially in flows of engineering importance, such as when the surface is rough or when the flow is not in equilibrium. In fact, there is a multiscale interaction, referred to here as an inner-outer interaction (IOI), where the large scales influence the dynamics of the small scales and vice-versa. These interactions are not well understood and therefore any corrections to the predictive models to include these interactions are essentially achieved through ad-hoc means.A better understanding of IOI will help explain the apparent non-universality of the constants in the log law and will certainly influence the development of models for both Reynolds-Averaged Navier-Stokes (RANS) calculation methods, Large-Eddy Simulations (LES) and hybrid RANS-LES. It will also be useful in the development of models for the control of wall turbulence, complementing knowledge from Direct Numerical Simulations which, we believe, are inherently incomplete owing to the restriction to low Reynolds numbers. Accurate models for prediction and control at realistic Reynolds numbers typical of practical applications will have to address IOI. Researchers working in specific areas of internal rough-wall flows, rough-wall boundary layers and freestream turbulence effects on boundary layers will also benefit from this fundamental work. All these aspects are abundantly present in a variety of practical applications and natural systems. For example, researchers exploring modelling strategies for practical applications such as oil and natural-gas pipelines, ship hulls and the natural and urban terrains will find the the data obtained from the roughness experiments to be very useful for validation exercises. Similarly, researchers in the area of turbomachinery will find the data from the roughness and freestream turbulence experiments extremely useful.

提高流体基系统的效率是当务之急。在实验空气动力学中，最困难的测量之一是精确测定表面摩擦力。我们需要准确地预测它，这对设计高效的系统至关重要。雷诺数相似度是描述紊流壁界流动基本性质的一个重要概念。与钝体的阻力系数不同，湍流边界层的阻力系数随着雷诺数的增加而无限减小，因为在任何雷诺数下，近表面的小尺度运动都直接受到粘度的影响。因此，雷诺数相似度在设计中非常重要，对于工程师来说是一个至关重要的工具，工程师在使用直接数值模拟或风洞试验（或两者都有）的信息时，很可能不得不外推几个数量级，以便估计工程阻力甚至气象雷诺数等量。也许雷诺数相似性最著名的例子是在有壁流动中发现的对数速度变化区域（对数定律），在足够高的雷诺数下，无论表面边界条件的性质或外部施加长度尺度的形式如何，都存在。在与实际应用相关的壁面流动中，湍流和雷诺数较高，流体动量和能量的输运和损失尚未得到很好的理解。因此，大多数预测和建模方法依赖于各种假设。其中最关键的两个是Wall定律（对数定律）和Townsend的局部平衡假设。这两种假设都隐含地假设了大尺度的流动是弱的，并且它们独立于小尺度而起作用。然而，这显然是不正确的，特别是在具有工程重要性的流动中，例如当表面粗糙或流动不平衡时。事实上，存在一种多尺度相互作用，这里称为内外相互作用（IOI），其中大尺度影响小尺度的动力学，反之亦然。这些相互作用没有被很好地理解，因此对预测模型的任何修正以包括这些相互作用基本上都是通过特别的方法实现的。更好地理解IOI将有助于解释对数定律中常数的明显非普遍性，并且肯定会影响reynolds - average Navier-Stokes （RANS）计算方法、大涡模拟（LES）和混合ranss -LES模型的发展。它还将有助于开发壁面湍流控制模型，补充直接数值模拟的知识，我们认为，由于低雷诺数的限制，直接数值模拟本质上是不完整的。在实际应用中，预测和控制实际雷诺数的精确模型必须解决IOI问题。在内部粗壁流动、粗壁边界层和自由流湍流对边界层的影响等特定领域工作的研究人员也将受益于这项基础工作。所有这些方面都大量存在于各种实际应用和自然系统中。例如，研究人员探索实际应用的建模策略，如石油和天然气管道，船体以及自然和城市地形，将发现从粗糙度实验中获得的数据对于验证练习非常有用。同样，涡轮机械领域的研究人员将发现来自粗糙度和自由流湍流实验的数据非常有用。

项目成果

Denoising of time-resolved PIV for accurate measurement of turbulence spectra and reduced error in derivatives

对时间分辨 PIV 进行去噪，以精确测量湍流谱并减少导数误差

• DOI: 10.1007/s00348-012-1375-4
• 发表时间: 2012
• 期刊: Experiments in Fluids
• 影响因子: 2.4
• 作者: Oxlade A

Adaptive Kagome Lattices for Near Wall Turbulence Suppression

用于近壁湍流抑制的自适应 Kagome 晶格

• DOI: 10.2514/6.2015-0270
• 发表时间: 2015
• 作者: Bird J

Reynolds-number dependence of the Townsend-Perry 'constant' in wall turbulence

壁湍流中汤森-佩里“常数”的雷诺数依赖性

• 发表时间: 2019
• 期刊: 11th International Symposium on Turbulence and Shear Flow Phenomena, TSFP 2019
• 作者: Diwan S.S.

Intermediate scaling and logarithmic invariance in turbulent pipe flow

湍流管流中的中间标度和对数不变性

• DOI: 10.1017/jfm.2021.71
• 发表时间: 2021
• 期刊: Journal of Fluid Mechanics
• 影响因子: 3.7
• 作者: Diwan S

Experimental Control of Turbulent Boundary Layers with In-plane Travelling Waves.

• DOI: 10.1007/s10494-018-9926-2
• 发表时间: 2018
• 期刊: Flow, turbulence and combustion
• 作者: Bird J;Santer M;Morrison JF
• 通讯作者: Morrison JF