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Numerical Models for Submerged Breakwaters: Coastal Hydrodynamics and Morphodynamics discusses the practice of submerged breakwaters, an increasingly popular tool used as a coastal defense system because of their amenity and aesthetics as compared to common emerged beach protection measures.The book is the perfect guide for experienced.

Since the mid-1990s, a series of FES (finite element solution) global ocean tidal atlases has been produced and released with the primary objective to provide altimetry missions with tidal de-aliasing correction at the best possible accuracy. We describe the underlying hydrodynamic and data assimilation design and accuracy assessments for the latest FES2014 release (finalized in early 2016), especially for the altimetry de-aliasing purposes. The FES2014 atlas shows extremely significant improvements compared to the standard FES2004 and (intermediary) FES2012 atlases, in all ocean compartments, especially in shelf and coastal seas, thanks to the unstructured grid flexible resolution, recent progress in the (prior to assimilation) hydrodynamic tidal solutions, and use of ensemble data assimilation technique. Compared to earlier releases, the available tidal constituent's spectrum has been significantly extended, the overall resolution has been augmented, and additional scientific byproducts such as loading and self-attraction, energy diagnostics, or lowest astronomical tides have been derived from the atlas and are available. Compared to the other available global ocean tidal atlases, FES2014 clearly shows improved de-aliasing performance in most of the global ocean areas and has consequently been integrated in satellite altimetry geophysical data records (GDRs) and gravimetric data processing and adopted in recently renewed ITRF standards (International Terrestrial Reference System, 2020). It also provides very accurate open-boundary tidal conditions for regional and coastal modelling.

It was only a matter of time before deep neural networks (DNNs) – deep learning – made their mark in turbulence modelling, or more broadly, in the general area of high-dimensional, complex dynamical systems. In the last decade, DNNs have become a dominant data mining tool for big data applications. Although neural networks have been applied previously to complex fluid flows, the article featured here (Ling et al., J. Fluid Mech., vol. 807, 2016, pp. 155–166) is the first to apply a true DNN architecture, specifically to Reynolds averaged Navier Stokes turbulence models. As one often expects with modern DNNs, performance gains are achieved over competing state-of-the-art methods, suggesting that DNNs may play a critically enabling role in the future of modelling complex flows.

An experimental investigation of pressure fluctuations generated by a single-stream compressible jet is carried out in an anechoic wind tunnel. Measurements are performed using a linear array of microphones installed in the near region of the jet and a polar arc of microphones in the far field. The main focus of the paper is on the analysis of the pressure fluctuations in the near field. Three novel signal processing techniques are presented to provide the decomposition of the near-field pressure into hydrodynamic and acoustic components. The procedures are all based on the application of the wavelet transform to the measured pressure data and possess the distinctive property of requiring a very simple arrangement to obtain the desired results (one or two microphones at most). The hydrodynamic and acoustic pressures are characterized separately in terms of their spectral and statistical quantities and a direct link between the acoustic pressure extracted from the near field and the actual noise in the far field is established. The analysis of the separated pressure components sheds light on the nearly Gaussian nature/intermittent behaviour of the acoustic/hydrodynamic pressure. The higher sensitivity of the acoustic component to the Mach number variation has been highlighted as well as the different propagation velocities of the two pressure components. The achieved outcomes are validated through the application to the same data of existing separation procedures evidencing the advantages and limitations of the new methods.

Glacial lake outburst floods (GLOFs) are widely recognised as one of the most devastating natural hazards in the Himalayas, with catastrophic consequences, including substantial loss of life. To effectively mitigate these risks and enhance regional resilience, it is imperative to conduct an objective and holistic assessment of GLOF hazards and their potential impacts over a large spatial scale. However, this is challenged by the limited availability of data and the inaccessibility to most of the glacial lakes in high-altitude areas. The data challenge is exacerbated when dealing with multiple lakes across an expansive spatial area. This study aims to exploit remote sensing techniques, well-established Bayesian regression models for estimating glacial lake conditions, cutting-edge flood modelling technology, and open data from various sources to innovate a framework for assessing the national exposure and impact of GLOFs. In the innovative framework, multi-temporal imagery is utilised with a random forest model to extract glacial lake water surfaces. Bayesian models are employed to estimate a plausible range of glacial lake water volumes and the associated GLOF peak discharges while accounting for the uncertainty stemming from the limited sizes of the available data and outliers within the data. A significant number of GLOF scenarios is subsequently generated based on this estimated plausible range of peak discharges. A graphics processing unit (GPU)-based hydrodynamic model is then adopted to simulate the resulting flood hydrodynamics in different GLOF scenarios. Necessary socio-economic information is collected and processed from multiple sources, including OpenStreetMap, Google Earth, local archives, and global data products, to support exposure analysis. Established depth–damage curves are used to assess the GLOF damage extents for different exposures. The evaluation framework is applied to 21 glacial lakes identified as potentially dangerous in the Nepalese Himalayas. The results indicate that, in the scenario of a complete breach of dam height across 21 lakes, Tsho Rolpa Lake, Thulagi Lake, and Lower Barun Lake bear the most serious impacts of GLOFs on buildings, roads, and agricultural areas, while Thulagi Lake could influence existing hydropower facilities. One unnamed lake in the Trishuli River basin, two unnamed lakes in the Tamor River basin, and three unnamed lakes in the Dudh River basin have the potential to impact more than 200 buildings. Moreover, the unnamed lake in the Trishuli River basin has the potential to inundate existing hydropower facilities.

Numerical accuracy and computational efficiency are the two key factors for flash flood simulation. In this paper, a two-dimensional fully hydrodynamic model is presented for the simulation of flash floods in mountain watersheds. A robust finite volume scheme is adopted to accurately simulate the overland flow with wet/dry fronts on highly irregular topography. A graphics processing unit-based parallel method using OpenACC is adopted to realize high-performance computing and then improve the computational efficiency. Since the finite volume scheme is explicit which involves many computationally intensive loop structures without data dependence, the parallel flash flood model can be easily realized by using OpenACC directives in an incremental developing way based on the serial model codes, except that data structure and transportation should be optimized for parallel algorithm. Model accuracy is validated by benchmark cases with exact solutions and experimental data. To further analyze the performance of the model, we considered a real flash flooding-prone area in China using a NVIDIA Tesla K20c card and three grid division schemes with different resolution. Results show that the proposed model can fast simulate the rainfall−runoff process related to the rapid mountain watersheds response, and a higher speedup ratio can be achieved for finer grids resolution. The proposed model can be used for real-time prediction of large-scale flash flood on high-resolution grids and thus has bright application prospects.

Flocking birds, fish schools, and insect swarms are familiar examples of collective motion that plays a role in a range of problems, such as spreading of diseases. Models have provided a qualitative understanding of the collective motion, but progress has been hindered by the lack of detailed experimental data. Here we report simultaneous measurements of the positions, velocities, and orientations as a function of time for up to a thousand wild-type Bacillus subtilis bacteria in a colony. The bacteria spontaneously form closely packed dynamic clusters within which they move cooperatively. The number of bacteria in a cluster exhibits a power-law distribution truncated by an exponential tail. The probability of finding clusters with large numbers of bacteria grows markedly as the bacterial density increases. The number of bacteria per unit area exhibits fluctuations far larger than those for populations in thermal equilibrium. Such \"giant number fluctuations\" have been found in models and in experiments on inert systems but not observed previously in a biological system. Our results demonstrate that bacteria are an excellent system to study the general phenomenon of collective motion.

Machine Learning (ML) is becoming increasingly popular in fluid dynamics. Powerful ML algorithms such as neural networks or ensemble methods are notoriously difficult to interpret. Here, we introduce the novel Shapley additive explanations (SHAP) algorithm (Lundberg & Lee, Advances in Neural Information Processing Systems, 2017, pp. 4765–4774), a game-theoretic approach that explains the output of a given ML model in the fluid dynamics context. We give a proof of concept concerning SHAP as an explainable artificial intelligence method providing useful and human-interpretable insight for fluid dynamics. To show that the feature importance ranking provided by SHAP can be interpreted physically, we first consider data from an established low-dimensional model based on the self-sustaining process (SSP) in wall-bounded shear flows, where each data feature has a clear physical and dynamical interpretation in terms of known representative features of the near-wall dynamics, i.e. streamwise vortices, streaks and linear streak instabilities. SHAP determines consistently that only the laminar profile, the streamwise vortex and a specific streak instability play a major role in the prediction. We demonstrate that the method can be applied to larger fluid dynamics datasets by a SHAP evaluation on plane Couette flow in a minimal flow unit focussing on the relevance of streaks and their instabilities for the prediction of relaminarisation events. Here, we find that the prediction is based on proxies for streak modulations corresponding to linear streak instabilities within the SSP. That is, the SHAP analysis suggests that the break-up of the self-sustaining cycle is connected with a suppression of streak instabilities.

Modelling forms a vital part of all engineering design, yet many hydraulic engineers are not fully aware of the assumptions they make. These assumptions can have important consequences when choosing the best model to inform design decisions. Considering the advantages and limitations of both physical and mathematical methods, this book will help you identify the most appropriate form of analysis for the hydraulic engineering application in question. All models require the knowledge of their background, good data and careful interpretation and so this book also provides guidance on the range of accuracy to be expected of the model simulations and how they should be related to the prototype. Applications for models include: Open channel systems; Closed conduit flows; Storm drainage systems; Estuaries; Coastal and nearshore structures; Hydraulic structures. An invaluable guide for students and professionals.