Fluid dynamics across the interface in gravel-bed rivers; quantification and numerical modelling of flow in the hyporheic zone

水在自然河流中的流动方式是环境中最复杂的现象之一，需要准确建模和预测。这对于发生在河床表面之下的流动（在称为“潜流”区的区域中）更是如此，在构成河底的鹅卵石和石头的空间之间。精确模拟这些水流的努力受到了阻碍，因为从卵石之间的微小空间获得水流速度的测量结果迄今为止被证明是一个无法解决的问题。但为什么科学家们会担心呢？首先，溪流生态学家现在认识到，潜流带是各种物种的重要栖息地。从河床上方流入地下的方式在很大程度上决定了为这个栖息地提供了多少氧气和营养物质。其次，渔业管理人员早就知道，产卵在河床中的鲑鱼卵孵化的可能性将取决于向产卵的砾石沉积物持续供应含氧水。第三，河流系统中的污染物（如重金属）往往附着在称为胶体的微观颗粒上，这些颗粒往往沿着流动路径流动。因此，了解水流在河床内的运动方式将对确定污染物的行为有很大帮助。因此，有广泛的高度重要的环境问题，需要详细预测水如何在河床内移动，但没有办法准确测量或建模。本提案采用开创性的新方法，力求应对这一挑战。第一个任务是精确测量床内的流动，这一重大问题将克服使用一种新的微型PIV（粒子成像测速）技术。该系统借用了为医疗应用开发的技术，采用了一个小型内窥镜数码相机，可以放置在实验河床内。通过用微小的反射颗粒播种水流，并提供激光的高强度照明，内窥镜摄像机可以记录它们如何在河床鹅卵石之间的小间隙中移动。使用一种特殊的处理器，这些数字图像可以转化为数字数据，精确记录水流如何穿过河床，然后进入河床。这样的测量以前是不可能的。该项目的第二阶段是利用这一独特数据集所带来的新认识来开发和测试一个三维数值模型，该模型可以精确预测水在河床表面上方和下方的流动方式。这将使用特别修改的计算流体动力学（CFD）模型来实现。这种模型代表了最先进的技术，但地下水流的问题已经证明太有问题，他们在这样的环境中应用。然而，我们的团队已经设计了一种方法，通过这种方法，鹅卵石可以被“消隐”，并预测它们周围的流动以及鹅卵石之间的相邻间隙。该项目将使用的测量和建模方法的进步代表了真实的突破，将解决从最具挑战性的自然环境中获取有用数据的固有问题。与此同时，开发一个可广泛应用的数值模型将确保这一新的认识能够得到应用和调整，以应对各种真实的世界环境挑战。

The way in which water flows within a natural river is one of the most complex phenomenon to model and predict accurately in the environment. This is even more so for the flow that occur just beneath the surface of the river bed (in a region termed the 'hyporheic' zone), between the spaces of pebbles and stones that make up the bottom of a river. Efforts to accurately model these flows have been hampered by the fact that obtaining measurements of water velocity from the tiny spaces between pebbles has so far proved an irresolvable problem. But why should this worry scientists? Firstly, stream ecologists now recognise that the hyporheic zone is an important habitat for a diverse range of species. The way flow from above the bed makes its way into the subsurface largely dictates how much oxygen and nutrients are supplied to this habitat. Secondly, fisheries managers have long understood that the probability of salmon eggs laid in river beds hatching will be dependent on a continuous supply of oxygenated water to the grevelly sediments in which they are laid. Thirdly, pollutants in river systems (such as heavy metals) often become attached to microscopic particles called colloids, which tend to follow flow pathways. An understanding of how flow moves within a river bed will thus go a long way to establishing pollutant behaviour. There are thus a broad range of highly important environmental issues that require detailed predictions of how water moves within a river bed, yet there is no way of measuring or modelling this accurately. Using pioneering new approaches this proposal seeks to meet this challenge. The first task is to accurately measure flow within the bed, this significant problem will be overcome using a new micro-PIV (particle imaging velocimetry) technique. This system borrows technology developed for medical applications by employing a small endoscopic digital camera which can be placed within an experimental river bed. By seeding the flow with tiny reflective particles, and providing high intensity illumination from a laser, the endoscopic camera can record how they move within the small gaps found between pebbles in the river bed. Using a special processor, these digital images can be turned into numerical data that accurately records how flow moves across and then into the river bed. Such measurements have never been possible before. The second phase of the project is to use the new understanding made possible by this unique dataset to develop and test a 3-D numerical model that can precisely predict how water will flow above and below the surface of a river bed. This will be achieved using a specially modified computational fluid dynamics (CFD) model. Such models represent the state-of-the-art, yet the issue of subsurface flow has proved too problematic for them to be applied in such environments. However, our team has devised a method whereby the pebbles can be 'blanked out' and the flow predicted around them and into adjacent gaps between pebbles. The advances in measurement and modelling approach that will be used in this project represent real breakthroughs that will unlock the inherent problem of gaining useful data from one of the most challenging of natural environments. Meanwhile, the development of a numerical model that can be widely applied will ensure that this new understanding can be applied and adapted to meet a variety of real world environmental challenges.