Coastal Engineering最新文献

The combined action of waves and currents can lead to the generation of freespans that have significant influence on both pipeline on-bottom stability and structural integrity. Several studies have been carried out since the 80’s to analyse the onset of the scour process and the related phenomena in the presence of either waves or currents, while only a few studies regard the combination of waves and currents. Moreover, the main empirical expressions coming from such studies cannot adequately represent the physics of the phenomena and lead to non-conservative results. For that purpose, a reanalysis of existing datasets on the onset of scour due to waves and/or currents has been carried out to obtain a coherent method for the evaluation of the critical embedment associated to such mixed flow conditions. In addition, an analytical study has been used to quantify the Keulegan–Carpenter parameter when waves are combined with currents, for a more correct application of such empirical formulas. These results will be integrated into a probabilistic model for the long-time evolution of freespans for the evaluation of the structural stability of pipelines in the marine environment, although the present approach can be exploited for a broader range of offshore applications that deal with the combined action of waves and currents.

Improving ocean wave energy capture in a cost-effective manner is a challenging task. Multi-chamber oscillating water column (OWC) devices are gaining favor due to their potentially efficient characteristics. This research experimentally investigated the hydrodynamic performance of a land-based OWC wave energy capture system with multiple chambers (ranging from 1 to 5). The free surface elevation, air pressure fluctuations, hydrodynamic efficiency, and reflection coefficient are considered. The hydrodynamic efficiencies of the overall, and sub-chambers in the OWC system with varying numbers of chambers are graphically presented. The influence of geometrical design parameters and wave conditions are also considered in evaluating the performance of the multi-chamber OWC system. It is found that the multi-chamber arrangement improves the hydrodynamic energy extraction characteristics compared to the traditional single-chamber setup. The sloshing mode of the chamber water column is utilized to effectively capture wave energy. As the number of chambers increases, the wave attenuation capability of the system improves for long waves. Yet, the overall efficiency declines when the number of OWC chambers exceeds 3. The chamber draft has the most substantial impact on the wave energy capturing capability, while the effects of opening ratio and wave height are attenuated due to multiple water-column interactions inside the chambers. The isometric sub-chamber structure demonstrates overall efficiency and wave attenuation advantages for short waves. The paper aims to guide the design and optimization of a multi-chamber OWC system.

The beach slope $\beta$ is a key component characterizing the coastal response to wave forcing. Here we investigate the rapid adaptation of the upper beach slope to a given wave forcing, for the case of a lower flat platform. Such types of morphology are found on coral and rocky reef beaches and low tide terrace environments. The influence of the lower platform on this rapid equilibrium beach state is shown to be significant depending on the breaking wave regime. In particular, the width of the platform and its water level can affect the wave dissipation along the inner surf and thus the wave structure entering the swash. This paper provides a classification of the beach slope equilibrium values as a function of the Dean number on a short time scale (individual wave action), based on both offshore and swash wave conditions. A decreasing trend of the beach slope with increasing offshore Dean number (${\Omega }_0$) is found for ${\Omega }_0\lesssim 2.7$. For ${\Omega }_0\gtrsim 2.7$ it is observed that the beach slope gradient is strongly controlled by the surf zone dissipation and it becomes necessary to define the swash Dean number (${\Omega }_{sw}$) to classify the slope. Finally, a semi-empirical formula for the beach slope evolution in the case of a low tide platform is introduced and tested on two natural low-tide terrace beaches.

With the increasing impact of climate change and relative sea level rise, low-lying coastal communities face growing risks from recurrent nuisance flooding and storm tides. Thus, timely and reliable predictions of coastal water levels are critical to resilience in vulnerable coastal areas. Over the past decade, there has been increasing interest in utilizing machine learning (ML) based models for emulation and prediction of coastal water levels. However, flood advisory systems still rely on running computationally demanding hydrodynamic models. To alleviate the computational burden, these physics-based models are either run at small scales with high resolution or at large scales with low resolution. While ML-based models are very fast, they face challenges in terms of ensuring reliability and ability to capture any surge levels. In this paper, we develop a deep neural network for spatiotemporal prediction of water levels in coastal areas of the Chesapeake Bay in the U.S. Our model relies on data from numerical weather prediction models as the atmospheric input and astronomical tide levels, while its outputs are time series of predicted water levels at several tide gauge locations across the Chesapeake Bay. We utilized a CNN-LSTM setting as the architecture of the model. The CNN part extracts the features from a sequence of gridded wind fields and fuses its output to several independent LSTM units. The LSTM units concatenate the atmospheric features with respective astronomical tide levels and produce water level time series. The novel contribution of the present work is in spatiotemporality and in prioritization of the physical relationships in the model to maintain a high analogy to hydrodynamic modeling, either in the network architecture or in the selection of predictors and predictands. The results show that this setting yields a strong performance in predicting coastal water levels that cause flooding from minor to major levels. We also show that the model stands up successfully to the rigorous comparison with a high-fidelity ADCIRC model, yielding mean RMSE and correlation coefficient of 14.3 cm and 0.94, respectively, in two extreme cases, versus 12.30 cm and 0.96 for the ADCIRC model. The results highlight the practical feasibility of employing fast yet inexpensive data-driven models for resilient coastal management.

In the Coastal Hazards System's (CHS) Probabilistic Coastal Hazard Analysis (PCHA) framework developed by the United States Army Corps of Engineers (USACE), historical records of tropical cyclone parameters have been used as data sources for statistical analysis, including fitting marginal distributions and measuring correlations between storm parameters. One limitation of the available historical databases is that observations of central pressure and radius of maximum winds are not available for a large number of storms. This may adversely affect the results of statistical analyses used to develop hazard curves. This study uses machine learning techniques to develop a data imputation method to “fill in” missing storm parameter records in historical datasets used for Joint Probability Method (JPM)-based coastal hazard analysis such as the USACE's CHS-PCHA. Specifically, Gaussian process regression (GPR) and artificial neural network (ANN) models are investigated as candidate machine learning-derived data imputation models, and the performance of different model parameterizations is assessed. Candidate imputation models are compared against existing statistical relationships. The effect of the data imputation process on statistical analyses (marginal distributions and correlation measures) is also evaluated for a series of example coastal reference locations.

Deep-sea tsunami detection relies on Deep-ocean Assessment and Reporting of Tsunamis (DART), GNSS buoys, and cabled Ocean-Bottom Pressure (OBP) gauges, which are very expensive and difficult to maintain, and often suffer from vandalism or negligent damage. Here, we exploit the potential of establishing a less expensive and more robust island-based geodetic network for tsunami detecting, source reconstruction and warning. The network locates at the coastline of islands and uses a new technique: GNSS Interferometric Reflectometry (GNSS-IR). GNSS-IR retrieves sea levels from combination of the direct and reflected signals from the sea surface sent by satellites. To test the feasibility and efficiency of such a new geodetic network, we use the South China Sea region as an example, and compare its performance in reconciling the variable slip distribution on the Manila megathrust with the previously designed deep-sea monitoring system, i.e., DARTs and planned cable-based OBP gauges. We find that the newly designed GNSS-IR network could work equally well as the cabled OBP network in detecting tsunamis if the stations are built in strategically chosen locations. Combining GNSS-IR with a Kalman filter approach, we demonstrate that carefully situated coastal GNSS stations at global remote deep-ocean islands could function similarly to conventional tide gauges but with advantages of simultaneously measuring relative sea-level and land-height changes, meanwhile suffering lower risk from damaging sea-level events and potential vandalism.

Coastal wave forecasting over large spatial scales is essential for many applications (e.g., coastal safety assessments, coastal management and developments, etc.). This demand explains the necessity for accurate yet effective models. A well-known efficient modelling approach is the quadratic approach (often referred to as frequency-domain models, nonlinear mild-slope models, amplitude models, etc.). The efficiency of this approach stems from a significant modelling reduction of the original governing equations (e.g., Euler equations). Most significantly, the description of wave nonlinearity essentially collapses into a single mode coupling term determined by the quadratic interaction coefficients. As a result, it is expected that the efficiency achieved by the quadratic approach is accompanied by a decrease in prediction accuracy. In order to gain further insight into the predictive capabilities of this modelling approach, this study examines six different quadratic formulations, three of which are of the Boussinesq type and the other three are referred to as fully dispersive. It is found that while the Boussinesq formulations reliably predict the evolution of coastal waves, the predictions by the fully dispersive formulations tend to be affected by false developments of modulational instability. Consequently, the predicted wave fields by the fully dispersive formulations are characterized by unexpectedly strong modulations of the sea-swell part and associated unexpected infragravity response. The impact of the modulational instability on wave prediction based on the quadratic approach is further demonstrated using existing laboratory results of bichromatic and irregular wave conditions.

Storm surge and waves are responsible for a substantial portion of tropical and extratropical cyclones-related damages. While high-fidelity numerical models have significantly advanced the simulation accuracy of storm surge and waves, they are not practical to be employed for probabilistic analysis, risk assessment or rapid prediction due to their high computational demands. In this study, a novel hybrid model combining dimensionality reduction and data-driven techniques is developed for rapid assessment of waves and storm surge responses over an extended coastal region. Specifically, the hybrid model simultaneously identifies a low-dimensional representation of the high-dimensional spatial system based on a deep autoencoder (DAE) while mapping the storm parameters to the obtained low-dimensional latent space using a deep neural network (DNN). To train the hybrid model, a combined weighted loss function is designed to encourage a balance between DAE and DNN training and achieve the best accuracy. The performance of the hybrid model is evaluated through a case study using the synthetic data from the North Atlantic Comprehensive Coastal Study (NACCS) covering critical regions within New York and New Jersey. In addition, the proposed approach is compared with two decoupled models where the regression model is based on DNN and the reduction techniques are either principal component analysis (PCA) or DAE which are trained separately from the DNN model. High accuracy and computational efficiency are observed for the hybrid model which could be readily implemented as part of early warning systems or probabilistic risk assessment of waves and storm surge.

Projections of high rates of sea level rise have stimulated proposals for adaptation strategies with increasingly high nourishment volumes along sandy beaches. An underlying assumption is that coastal profiles respond rapidly to nourishments by redistributing sediments towards a (new) equilibrium shape. However, this perception may not be valid when high volumes of nourishment are applied, as the profile shape may then undergo significant deformation. Current state-of-the-art modelling techniques often concentrate on a single spatio-temporal scale, either lacking the necessary temporal horizon or failing to provide the required level of cross-shore detail. This article introduces Crocodile, a diffusion based cross-shore model designed to bridge the gap between short- and long-term nourishment modelling. The model simulates the effects of nourishment strategies on coastal volume, coastline position and beach width over a decadal timeframe. It incorporates different elements which compute cross-shore diffusion, sediment exchange with the dune and longshore sediment losses. To test the model performance, a series of idealized nourishment scenarios are examined, along with three case studies along the Dutch coast with different nourishment strategies over the past few decades. The modelled coastal volume, shoreline position and beach width strongly resemble the observations with only a 12% overestimation in profile volume and 13% underestimation in beach width. Averaged over selected periods of nourishment, trends and trend reversals between different strategies are well replicated with slight overestimation for coastal volume trends by $1.5m^3/m/yr\left(10\%\right)$, while beach width trends are underestimated by $0.2m/yr$ (15%). Given that the added nourishment volumes are typically in the order of $100m^3/m$, these model errors are considered sufficiently low to conclude that Crocodile effectively simulates variations in coastal volume, coastline position and beach width over a decadal timeframe in response to different nourishment strategies. Therefore, Crocodile can facilitate the evaluation of future nourishment strategies.

Physical experiments were conducted to examine the influence of adverse currents on the propagation of shallower water waves and their impact on the evolution of nonlinear wave shapes. Irregular waves, characterized by varying initial peak periods and amplitudes, were generated in a physical wave flume equipped with a bottom slope of 1/20. Three groups of spatially varying opposing currents were generated in the flume and interacted with the aforementioned wave trains. Experimental results confirm that strong opposing currents can significantly intensify local wave nonlinearity, thereby further influencing the deformation characteristics of waves. In contrast, a weak opposing current (without wave-blocking) has a negligible effect. Bicoherence analysis revealed that the degree of phase coupling among triads of waves increased with an elevated current velocity before wave-blocking. Nevertheless, during partial wave-blocking, the degree of phase coupling was seemingly weakened by an increase in opposing current. Moreover, the influence of a co-existing current in the formation of extreme waves was recognized. The study confirms that adverse currents notably affect extreme wave steepness and extreme wave skewness, with a negligible impact on extreme wave asymmetry. As a result, empirical formulas modified by current effects are presented, describing certain key nonlinear wave shapes as functions of the local Ursell number.