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.

现在，提高流体系统效率的需求至关重要。在实验空气动力学中，最困难的测量之一是精确确定表面摩擦力。我们需要准确地预测它对于高效系统的设计至关重要。雷诺数相似性是描述湍流壁面流动基本属性的基本概念。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期地减小，因为表面附近的小尺度运动直接受到任何雷诺数下的粘度影响。因此，雷诺数相似性在设计中非常重要，并且对于工程师来说是一个重要的工具，工程师根据直接数值模拟或风洞测试（或两者）的信息，很可能必须推断出几个数量级，以便估计诸如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壁面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，无论表面边界条件或形式的性质如何，该区域都存在在与实际应用相关的壁限流中，流动是湍流且雷诺数很高，流体动量和能量的传输和损失尚不清楚。因此，大多数预测和建模方法都依赖于各种假设。最关键的两个是墙定律（对数定律）和汤森局部均衡假说。这两个假设都隐含地假设流动中的大尺度很弱，并且它们的功能独立于小尺度。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态，反之亦然。这些相互作用还没有被很好地理解，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临时手段实现的。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，并且肯定会影响雷诺平均纳维斯托克斯 (RANS) 计算方法、大涡模拟 (LES) 和混合 RANS-LES 模型的开发。它还可用于开发控制壁湍流的模型，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。在实际应用中典型的真实雷诺数下进行预测和控制的准确模型必须解决 IOI 问题。研究内部粗糙壁流、粗糙壁边界层和自由流湍流对边界层影响的特定领域的研究人员也将从这项基础工作中受益。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，探索石油和天然气管道、船体以及自然和城市地形等实际应用的建模策略的研究人员会发现从粗糙度实验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会发现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

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

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.

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