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Complementing scale experiments of rivers and estuaries with numerically modelled hydrodynamics

机译：用数模建模流体动力补充河流和河口的规模实验

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Physical scale experiments enhance our understanding of fluvial, tidal and coastal processes. However, it has proven challenging to acquire accurate and continuous data on water depth and flow velocity due to limitations of the measuring equipment and necessary simplifications during post-processing. A novel means to augment measurements is to numerically model flow over the experimental digital elevation models. We investigated to what extent the numerical hydrodynamic model Nays2D can reproduce unsteady, nonuniform shallow flow in scale experiments and under which conditions a model is preferred to measurements. To this end, we tested Nays2D for one tidal and two fluvial scale experiments and extended Nays2D to allow for flume tilting, which is necessary to steer tidal flow. The modelled water depth and flow velocity closely resembled the measured data for locations where the quality of the measured data was most reliable, and model results may be improved by applying a spatially varying roughness. The implication of the experimental data–model integration is that conducting experiments requires fewer measurements and less post-processing in a simple, affordable and labour-inexpensive manner that results in continuous spatio-temporal data of better overall quality. Also, this integration will aid experimental design.
机译：物理规模实验提高了我们对河流，潮汐和沿海流程的理解。然而，由于测量设备的限制和后处理期间，由于测量设备的限制以及必要的简化，因此已经证明了对水深和流速的准确和连续数据有挑战性。一种增强测量的新颖手段是在实验数字高度模型上进行数字模型流量。我们研究了数值流体动力学模型Nays2D可以在多大程度上在规模实验中再现不稳定，不均匀的浅流，并且在该条件下是优选的测量。为此，我们测试了一个潮汐和两个河流尺度实验的Nays2D，并扩展了Nays2D以允许Flume倾斜，这是为了引导潮流所必需的。模型水深和流速非常类似于测量数据最可靠的位置的测量数据，并且可以通过施加空间变化的粗糙度来改善模型结果。实验数据模型集成的含义是，进行实验需要更少的测量和更少的后处理，以简单，实惠且劳动且廉价的方式，从而导致持续的整体质量的连续时空数据。此外，这种集成将帮助实验设计。

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