Experimental and Numerical Modelling of Air Entrainment in Eco-hydraulics

生态液压中空气夹带的实验和数值模拟

• 批准号: RGPIN-2018-03994
• 负责人: Balachandar, Ram
• 金额: $ 7.58万
• 依托单位: University of Windsor
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2022
• 资助国家: 加拿大
• 起止时间: 2022-01-01 至 2023-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=749021
• 关键词: Experimental; Numerical; Modelling; Air; Entrainment

Air entrainment in free-surface flows is ubiquitous in eco-hydraulics. The entrained air influences the dissolved oxygen level and is very useful in water treatment to sustain microorganisms for water purification and in rivers and streams for sustaining a healthy aquatic life. However, excessive air entrainment in fish passages can result in gas-bubble disease and adversely affect fish migration. Aeration also occurs in oceans during the wave breaking process. Wave energy is said to be the largest source of renewable energy and could contribute 10% of the global electricity demand by 2050. Methods to extract the energy from the wave breaking process needs to be explored. These examples highlight the practical relevance of studying air-water flows. Self-aeration is an uncontrolled process and the dynamics of air-water flows is complex. Researchers have largely studied air-water flows experimentally and to a limited extent using numerical tools. While the studies have brought forth the complexity in the flows, the internal turbulence mechanism is not fully understood. This is due to the limitations in the commonly used single-phase flow instruments, which need to be improved or modified for use in air-water flows. The Achilles heel in the computational study of air-water flows is the air-entrainment model. To achieve success, an improved understanding of the physical processes that govern the flow is required. With the availability of improved instrumentation and enhanced computational facilities, it is now possible to begin a research program to develop practical tools that are required to predict the supersaturation of gases in rivers, to prevent fish-kill and to reduce odour in urban sewer systems.A novel experimental and numerical research program is proposed to predict/control turbulence and air entrainment in free-surface flows. We will focus on flow fields that represent most of the complexities that one encounters in practical situations. We have chosen 3 flow fields: Hydraulic Jumps, Fish Passages and Breaking Waves. These flows have common features: high turbulence, free-surface breakup, air entrainment followed by bubble generated turbulence. Experimental studies using Bubble Image Velocimetry supported by complementary numerical modeling will be used.The major goals include: (i) Improve our understanding of aerated flows by conducting experimental studies using state-of-the-art instrumentation and (ii) Develop computational fluid dynamics based models suitable for air-water flows. The physical processes that are responsible for aeration, air bubble interactions and breakup will be analyzed in the flow fields. The study will aid to improve the design of drop shafts, fish passages, spillways and urban hydraulic systems. The new tools will help to reduce future reliance on expensive physical model testing. Over the 5-year cycle, 14 HQP (4 PhD, 5 MSc, 5 BSc) will be trained.

自由面水流中的空气卷吸在生态水力学中是普遍存在的。夹带的空气影响溶解氧水平，并且在水处理中非常有用，以维持用于水净化的微生物，并且在河流和溪流中非常有用，以维持健康的水生生物。 然而，鱼道中的过量空气夹带可导致气泡病，并对鱼类洄游产生不利影响。在波浪破碎过程中，海洋中也会发生曝气。波浪能是最大的可再生能源，到2050年可能占全球电力需求的10%。 需要探索从波浪破碎过程中提取能量的方法。这些例子突出了研究空气-水流动的实际意义。自曝气是一个不受控制的过程，空气-水流动的动力学是复杂的。研究人员在很大程度上通过实验研究了空气-水流动，并在有限程度上使用数值工具。虽然研究工作已经揭示了流动的复杂性，但内部湍流机制还没有完全弄清楚。这是由于常用的单相流仪器的局限性，需要改进或修改以用于空气-水流动。空气-水流动计算研究中的致命弱点是空气卷吸模型。为了取得成功，需要更好地理解控制流动的物理过程。 随着仪器设备的改进和计算能力的增强，开始一项研究计划，以开发预测河流中气体过饱和度、防止鱼类死亡和减少城市下水道系统气味所需的实用工具。我们将集中讨论在实际情况中遇到的最复杂的流场。我们选择了3个流场：水跃，鱼通道和破碎波。这些流动具有共同的特征：高湍流度、自由表面破碎、空气卷吸以及气泡产生的湍流。实验研究将使用气泡图像测速技术，并辅以数值模拟。主要目标包括：（i）通过使用最先进的仪器进行实验研究，提高我们对掺气流动的理解;（ii）开发适合空气-水流动的基于计算流体动力学的模型。 负责曝气，气泡相互作用和破碎的物理过程将在流场中进行分析。这项研究将有助于改善竖井、鱼道、溢洪道和城市水力系统的设计。新工具将有助于减少未来对昂贵的物理模型测试的依赖。在5年周期内，将培训14名HQP（4名博士，5名硕士，5名理学士）。