Real Wave Research Articles

Wave-triggered breakup in the marginal ice zone generates lognormal floe size distributions: a simulation study

Abstract. Fragmentation of the sea ice cover by ocean waves is an important mechanism impacting ice evolution. Fractured ice is more sensitive to melt, leading to a local reduction in ice concentration, facilitating wave propagation. A positive feedback loop, accelerating sea ice retreat, is then introduced. Despite recent efforts to incorporate this process and the resulting floe size distribution (FSD) into the sea ice components of global climate models (GCMs), the physics governing ice breakup under wave action remains poorly understood and its parametrisation highly simplified. We propose a two-dimensional numerical model of wave-induced sea ice breakup to estimate the FSD resulting from repeated fracture events. This model, based on linear water wave theory and visco-elastic sea ice rheology, solves for the scattering of an incoming time-harmonic wave by the ice cover and derives the corresponding strain field. Fracture occurs when the strain exceeds an empirical threshold. The geometry is then updated for the next iteration of the breakup procedure. The resulting FSD is analysed for both monochromatic and polychromatic forcings. For the latter results, FSDs obtained for discrete frequencies are combined following a prescribed wave spectrum. We find that under realistic wave forcing, lognormal FSDs emerge consistently in a large variety of model configurations. Care is taken to evaluate the statistical significance of this finding. This result contrasts with the power law FSD behaviour often assumed by modellers. We discuss the properties of these modelled distributions with respect to the ice rheological properties and the forcing waves. The projected output can be used to improve empirical parametrisations used to couple sea ice and ocean wave GCM components.

Landau damping of ion‐acoustic waves with simultaneous effects of non‐extensivity and non‐thermality in the presence of hybrid Cairns‐Tsallis distributed electrons

AbstractThe dispersion properties and Landau damping rate of ion‐acoustic waves (IAWs) with the hybrid Cairns‐Tsallis distributed (CTD) electrons and Maxwellian ions are investigated using the plasma kinetic model based on Vlasov‐Poisson's equations. For both super‐extensive (q < 1) and sub‐extensive (q > 1) plasmas, the dielectric response function, real frequency, and Landau damping rate of IAWs are derived. By taking the effect of θi, e (ion‐to‐electron temperature ratio) into account, it is found that with the increase of ion temperature, the real frequency and wave dispersion effects increase as well (for both super‐extensive and sub‐extensive cases). Exploring the properties of the Landau damping rate of IAWs with the simultaneous presence of non‐thermal parameter α and non‐extensive parameter q, a comparison of numerical and analytical results is presented. It is found that in different ranges of θe, i (electron‐to‐ion temperature ratio), on decreasing the values of the non‐extensive parameter and increasing values of the non‐thermal parameter, the weak damping rate is observed (vice versa) in super‐extensive or super‐thermal plasma, although the trend of the damping rate in sub‐thermal plasma is similar (as in the case of super‐thermal plasma) but is less weak. It is further revealed that the damping rate of IAWs in thermal plasmas (Maxwellian) is stronger than the damping rate of IAWs in the case of non‐thermal plasmas (CTD). The current study is applicable to provide deep insight and further allow the exploration of electrostatic plasma modes in different space and laboratory plasma environments where the hybrid CTD plasma exists.

Accelerating multimodel Bayesian inference, model selection, and systematic studies for gravitational wave astronomy

Gravitational wave models are used to infer the properties of black holes in merging binaries from the observed gravitational wave signals through Bayesian inference. Although we have access to a large collection of signal models that are sufficiently accurate to infer the properties of black holes, for some signals, small discrepancies in the models lead to systematic differences in the inferred properties. In order to provide a single estimate for the properties of the black holes, it is preferable to marginalize over the model uncertainty. Bayesian model averaging is a commonly used technique to marginalize over multiple models, however, it is computationally expensive. An elegant solution is to simultaneously infer the model and model properties in a joint Bayesian analysis. In this work we demonstrate that a joint Bayesian analysis can not only accelerate but also account for model-dependent systematic differences in the inferred black hole properties. We verify this technique by analysing 100 randomly chosen simulated signals and also the real gravitational wave signal GW200129_065458. We find that not only do we infer statistically identical properties as those obtained using Bayesian model averaging, but we can sample over a set of three models on average $2.5\times$ faster. In other words, a joint Bayesian analysis that marginalizes over three models takes on average only $20\%$ more time than a single model analysis. We then demonstrate that this technique can be used to accurately and efficiently quantify the support for one model over another, thereby assisting in Bayesian model selection.

The Multi-Channel Maximum-Likelihood (MCML) method for infrasound detection and wave parameter estimation: Evaluation, implementation, and first applications

The detection capability of the International Monitoring System (IMS) infrasound network is a key concern since ubiquitous coherent noise interfere with the identification of explosive events. The need to distinguish between incoherent wind noise and real coherent infrasonic wave signals has motivated the development of a new parametric stochastic model. We establish the expressions of both the Generalized Likelihood Ratio Test (GLRT) and the Maximum Likelihood Estimator (MLE). The major results are the expression of the asymptotic distribution of the GLRT under null hypothesis, leading to the p-value computation, and the expression of the asymptotic covariance. The Multi-Channel Maximum-Likelihood (MCML) detection is implemented in the time-frequency domain in order to discriminate between interfering signals. Extensive simulations with synthetic signals show that MCML outperforms the state-of-the-art multi-channel correlation detector algorithms in poor signal-to-noise ratio scenarios. We illustrate the performance of MCML on real IMS data, highlighting its capability to characterize the coherent ocean ambient infrasound noise in high resolution through comparisons with theoretical state-of-the-art numerical wave models as implemented at the LOPS research unit of IFREMER in the DATARMOR HPC center.

An Improved Time Domain Approach for Analysis of Floating Bridges Based on Dynamic Finite Element Method and State-Space Model

The floating bridge bears the dead weight and live load with buoyancy, and has wide application prospect in deep-water transportation infrastructure. The structural analysis of floating bridge is challenging due to the complicated fluid-solid coupling effects of wind and wave. In this research, a novel time domain approach combining dynamic finite element method and state-space model (SSM) is established for the refined analysis of floating bridges. The dynamic coupled effects induced by wave excitation load, radiation load and buffeting load are carefully simulated. High-precision fitted SSMs for pontoons are established to enhance the calculation efficiency of hydrodynamic radiation forces in time domain. The dispersion relation is also introduced in the analysis model to appropriately consider the phase differences of wave loads on pontoons. The proposed approach is then employed to simulate the dynamic responses of a scaled floating bridge model which has been tested under real wind and wave loads in laboratory. The numerical results are found to agree well with the test data regarding the structural responses of floating bridge under the considered environmental conditions. The proposed time domain approach is considered to be accurate and effective in simulating the structural behaviors of floating bridge under typical environmental conditions.

The Impact of Representations of Realistic Topography on Parameterized Oceanic Lee Wave Energy Flux

AbstractOceanic lee waves are generated when quasi‐steady flows interact with rough topography at the bottom of the ocean, providing an important sink of energy and momentum from the mean flow and a source of turbulent kinetic energy. Linear theory with a spectral representation of topography is typically used to inform parameterizations of lee wave generation. Here, we use a realistic wave resolving simulation of the Drake Passage, a hot‐spot of lee wave generation, to investigate the utility of such parameterizations for areas of complex large scale topography. The flow is often blocked and split by large amplitude topographic features, creating an “effective topography,” and calling into question the spectral representation of small scale topography for lee wave generation. By comparing the resolved modeled wavefield to parameterizations employing various representations of topography, we show that spectral methods may not be appropriate in areas of rough topography. We develop a simple topographic representation consisting of an ensemble of topographic peaks, which allows physical treatment of flow blocking at finite amplitude topography. This method effectively predicts bottom vertical velocities and lee wave energy flux, whereas spectral methods overestimate the energy flux by approximately 4 times. Our results also imply that the nature of lee waves in such regions can be misrepresented by a spectral approach to topographic representation. This leads to both an overestimate of wave energy flux and an underestimate of wave nonlinearity, with implications for the mechanisms by which lee waves break and mix in the abyssal ocean.

Gravitational Wave-Signal Recognition Model Based on Fourier Transform and Convolutional Neural Network

The recent detection of gravitational waves is a remarkable milestone in the history of astrophysics. With the further development of gravitational wave detection technology, traditional filter-matching methods no longer meet the needs of signal recognition. Thus, it is imperative that we develop new methods. In this study, we apply a gravitational wave signal recognition model based on Fourier transformation and a convolutional neural network (CNN). The gravitational wave time-domain signal is transformed into a 2D frequency-domain signal graph for feature recognition using a CNN model. Experimental results reveal that the frequency-domain signal graph provides a better feature description of the gravitational wave signal than that provided by the time-domain signal. Our method takes advantage of the CNN's convolution computation to improve the accuracy of signal recognition. The impact of the training set size and image filtering on the performance of the developed model is also evaluated. Additionally, the Resnet101 model, developed on the Baidu EasyDL platform, is adopted as a comparative model. Our average recognition accuracy performs approximately 4% better than the Resnet101 model. Based on the excellent performance of convolutional neural network in the field of image recognition, this paper studies the characteristics of gravitational wave signals and obtains a more appropriate recognition model after training and tuning, in order to achieve the purpose of automatic recognition of whether the signal data contain real gravitational wave signals.

Evaluating Long-Term Variability of the Arctic Stratospheric Polar Vortex Simulated by CMIP6 Models

The Arctic stratospheric polar vortex is a key component of the climate system, which has significant impacts on surface temperatures in the mid-latitudes and polar regions. Therefore, understanding polar vortex variability is helpful for extended-range weather forecasting. The present study evaluates long-term changes in the position and strength of the polar vortex in the Arctic lower stratosphere during the winters from 1980/81 to 2013/14. Simulations of the Coupled Model Intercomparison Project Phase 6 (CMIP6) models are compared with Modern-Era Retrospective analysis for Research and Applications Version 2 (MERRA2) reanalysis dataset. Overall, the CMIP6 models well capture the spatial characteristics of the polar vortex with spatial correlation coefficients between the potential vorticity (PV) in the lower stratosphere from simulations and MERRA2 products generally greater than 0.85 for all CMIP6 models during winter. There is a good agreement in the position and shape of the polar vortex between the CMIP6 multi-model mean and MERRA2, although there exist differences between simulations of individual CMIP6 models. However, most CMIP6 models underestimate the strength of polar vortex in the lower stratosphere, with the largest negative bias up to about −20%. The present study further reveals that there is an anticorrelation between the polar vortex strength bias and area bias simulated by CMIP6 models. In addition, there is a positive correlation between the trend of EP-flux divergence for wavenumber one accumulated in early winter and the trend in zonal mean zonal wind averaged in late winter. As for the long-term change in polar vortex position, CanESM5, IPSL-CM5A2-INCA, UKESM1-0-LL, and IPSL-CM6A-LR well capture the persistent shift of polar vortex towards the Eurasian continent and away from North America in February, which has been reported in observations. These models reproduce the positive trend of wavenumber-1 planetary waves since the 1980s seen in the MERRA2 dataset. This suggests that realistic wave activity processes in CMIP6 models play a key role not only in the simulation of the strength of the stratospheric polar vortex but also in the simulation of the polar vortex position shift.

Hazard and Risk-Based Tsunami Early Warning Algorithms for Ocean Bottom Sensor S-Net System in Tohoku, Japan, Using Sequential Multiple Linear Regression

This study presents robust algorithms for tsunami early warning using synthetic tsunami wave data at ocean bottom sensor (OBS) arrays with sequential multiple linear regression. The study focuses on the Tohoku region of Japan, where an S-net OBS system (150 pressure sensors) has been deployed. To calibrate the tsunami early warning system using realistic tsunami wave profiles at the S-net stations, 4000 stochastic tsunami simulations are employed. Forecasting models are built using multiple linear regression together with sequential feature selection based on Akaike Information Criterion and knee-point method to identify sensors that improve the accuracy most significantly. The study considers tsunami wave amplitude at a nearshore location and regional tsunami loss for buildings to develop hazard-based and risk-based tsunami warning algorithms. The models identify an optimal configuration of OBS stations and waiting time for issuing tsunami warnings. The model performance is compared against a base model, which only uses the earthquake magnitude and epicenter location. The result indicates that estimating the tsunami amplitude and loss via S-net improves accuracy. For the hazard-based forecasting, adding six sensors from the S-net improves the accuracy of the estimation most significantly with an optimal waiting time of 3 min. For the risk-based forecasting, a longer waiting time between 5 and 10 min is suitable.

Spectral (FT-IR, FT-Raman, NMR, UV–vis), electronic structure (DFT, TD-DFT), and molecular docking investigations on 1-((1H-benzo[d]imidazol-1-yl)methyl)urea – A bioactive Mannich base system

A synthesized 1-((1H-benzo[d]imidazol-1-yl)methyl)urea (BFU) Mannich base has been extensively studied using experimental and theoretical techniques. Rotamerism, geometry, tautomerism and mechanistic aspects of tautomerism were investigated. Spectral studies were carried out which includes FT-IR, FT-Raman, NMR, and UV–vis spectral characterization. Electronic structure properties such as frontier molecular orbital analysis, molecular electrostatic potential, atomic charges, natural bond orbital characterization and NLO properties enhance insight into the chemical physics nature of BFU. Real space wave function analyses edify the nature of intramolecular interactions. Molecular docking analysis has been performed against a fungal species called C. glabrata whose protein ID is 5B6K with BFU.

Quantum simulation for topological Euler insulators

Although recent studies have established a powerful framework to search for and classify topological phases based on symmetry indicators, there exists a large class of fragile topology beyond the description. The Euler class characterizing the topology of two-dimensional real wave functions is an archetypal fragile topology underlying some important properties. However, as a minimum model of fragile topology, the two-dimensional topological Euler insulator consisting of three bands remains a significant challenge to be implemented in experiments. Here, we experimentally realize a three-band Hamiltonian to simulate a topological Euler insulator with a trapped-ion quantum simulator. Through quantum state tomography, we successfully evaluate the Euler class, Wilson loop flow, entanglement spectra and Berry phases to show the topological properties of the Hamiltonian. The flexibility of the trapped-ion quantum simulator further allows us to probe dynamical topological features including skyrmion-antiskyrmion pairs and Hopf links in momentum-time space from quench dynamics.

Long-time prediction of sea wave trains by LSTM machine learning method

The present work proposes a novel way of long-time accurate prediction of sea wave trains using the Long Short-Term Memory (LSTM) method. Typical kinds of regular waves (e.g. the Stokes wave), irregular waves (produced from the PM wave spectrum by either the equal frequency method or the equal energy method) and real sea wave trains are considered. The historical data of wave elevations are used in the training process of the LSTM model, based on which future wave evolutions are predicted. To improve the accuracy of long-time estimation of irregular waves, a multi-shot prediction method is proposed. The validity of the proposed approach is proved by comparing the performance of the single-shot prediction method and the multi-shot prediction method in multiple cases of irregular waves. In the meantime, the effects of the input and output lengths are investigated. Test results demonstrate that, although the standard LSTM scheme with the single-shot method presents appealing accuracy in forecasting regular waves, the multi-shot method is an essential component in enhancing the accuracy of long-time prediction of irregular waves.

An Improved Hydraulic Power Take-Off Unit Based on Dual Fluid Energy Storage for Reducing the Power Fluctuation Problem in the Wave Energy Conversion System

The power take-off (PTO) stability is one of the most important concerns for wave energy converters (WECs). The PTO unit converts the mechanical energy produced by the wave absorber (WA) unit into useful electrical energy. Due to the drastic input energy variation of real wave motions, the generated electrical power from the PTO unit significantly fluctuates and is potentially harmful to electrical and electronic appliances. This paper proposes an improved hydraulic PTO (HPTO) for the WECs. An improved HPTO unit comprises a dual high-pressure accumulator (HPA) module and fluid energy control (FEC) module, which significantly enhances the generated electrical power from the generator under irregular wave circumstances. A complete model of wave absorber device with conventional and improved HPTO units was built in MATLAB/Simulink using a Simscape fluids toolbox. The parameters of the FEC control strategy were optimized using a genetic algorithm. The improved HPTO unit model was simulated with five irregular wave inputs to evaluate its performance in irregular conditions. The effects of the HPA pressure constraints on the improved HPTO unit performance were also investigated. Overall, the simulation results indicate that the improved HPTO unit was able to generate a stable power up to 87.3% of WECs in an irregular sea state.

Learning frequency‐aware convolutional neural network for spatio‐temporal super‐resolution water surface waves

AbstractAs a usual component in virtual scenes, water surface plays an important role in various graphical applications, including special effects, video games, and virtual reality. Although recent years have witnessed significant progress based on Navier–Stokes equations and simplified water models, large‐scale water surface waves with high‐frequency visual details remain computationally expensive for interactive applications. This article proposes a novel frequency‐aware neural network to synthesize consistent and detailed water surface waves from low‐resolution input. At its core, our approach leverage the wavelet transformation theory over space, frequency and direction, and incremental supervision to decompose the 4D amplitude function into multiple smaller subproblems. Specifically, we first customize four subnetworks and corresponding loss functions for super‐resolution of spatial resolution, temporal evolution, wave direction subdivision, and wave number, respectively. Then, to enforce the upsampling along each dimension orthogonal to each other, we introduce a cooperative training scheme to fine‐tune and integrate the proposed subnetworks with carefully designed training dataset. Our method can visually enhance high‐resolution spatial details, temporal coherence, interactions with complex boundaries, and various wave patterns with flexible control along multiple dimensions. Through extensive experiments, our method arrives at 13 speedup for 32 upsampling of various simulation scenarios. We also validate the effectiveness and robustness of our method to produce realistic water surface waves toward artistic innovation.

An improved wavemaker velocity boundary condition for generating realistic waves in the numerical wave tank

Wavemaker velocity boundary condition method is one of the most common methods for wave generation in numerical wave tanks (NWTs). Traditional wavemaker velocity boundary condition (TWVBC) makes mass flux at the wavemaker boundary nonconservation, and produces a net mass transport flow in the NWT. In this paper, an improved wavemaker velocity boundary condition (IWVBC) is proposed by adding a reverse current at the wavemaker boundary. Through theoretical analysis, the phenomenon of mass nonconservation of regular and random waves is studied, and different modes of the net mass transport are found. Then, the IWVBC and TWVBC are applied to a computational fluid dynamics (CFD) model and the corresponding results are compared with reference data. The results show that the IWVBC can effectively maintain the mass conservation of the NWT and avoid the occurrence of net mass transport and the rise of mean water level. Furthermore, CFD results are basically consistent with the experimental law of wave profile, velocity and mass transport, which indicates that the IWVBC can reproduce realistic wave propagation characteristics in a NWT.

Impacts of mechanical energy storage on power generation in wave energy converters for future integration with offshore wind turbine

While offshore wind energy uptake is growing steadily, offshore wave energy remains largely untapped, despite having significant potential globally. The high cost and complexity of wave energy conversion systems are the main challenges to their commercial development globally, while they can be offset if they are integrated into the well-developed offshore structure of wind turbines. Since the source of the wave energy is associated with wind resources, these two ocean renewable resources have strong synergies that are yet to be combined effectively. Therefore, this paper aims to explore the idea of introducing wave energy converters into the offshore wind system as a hybrid system with a specific focus on wave energy converter (WEC) systems from their power characteristic perspective. In this paper, wave power fluctuations characteristics have been analysed and compared with wind power and two mechanical energy storage strategies, added inertia and gas accumulators, are investigated at the power take-off (PTO) system level on a typical point absorbed WEC. A sensitivity study of the impact of various mechanical storage systems on WEC performance was conducted under a set of real wave data. In addition, the significance of mechanical storage systems at the WEC PTO level in a DC-linked hybrid system is discussed along with enabling a better understanding of a future combined system. The results indicate that both mechanical storage options can effectively enhance energy production, reduce the power variations in the WEC system, and lead to the feasibility of integrating wave energy with the well-known offshore wind energy turbine system.

Sea spray volume flux estimation using joint statistics of wind and waves

This article provides estimates of the sea spray volume flux using joint statistics of wind and waves. This is achieved by combining the sea spray volume flux parameterization proposed by Xu et al. (2021) with the joint statistics of wind and waves provided by Bitner-Gregersen (2015). Both the sea spray volume flux formula and the joint statistics of wind and waves represent conditions for wind waves from the North-West Shelf of Western Australia. The expected value and the variance of the sea spray volume flux for a range of realistic wind and wave conditions are presented, as well as an illustrative example. Comparison is also made with data from Xu et al. (2021) showing a reasonable agreement for the relevant subset of the data.

Model of charge transfer collisions between C60 and slow ions.

A semiclassical model describing the charge transfer collisions of C60 fullerene with different slow ions has been developed to analyze available observations. These data reveal multiple Breit-Wigner-like peaks in the cross sections, with subsequent peaks of reactive cross sections decreasing in magnitude. Calculations of charge transfer probabilities, quasi-resonant cross sections, and cross sections for reactive collisions have been performed using semiempirical interaction potentials between fullerenes and ion projectiles. All computations have been carried out with realistic wave functions for C60's valence electrons derived from the simplified jellium model. The quality of these electron wave functions has been successfully verified by comparing theoretical calculations and experimental data on the small angle cross sections of resonant C60+C60 + collisions. Using the semiempirical potentials to describe resonant scattering phenomena in C60 collisions with ions and Landau-Zener charge transfer theory, we calculated theoretical cross sections for various C60 charge transfer and fragmentation reactions which agree with experiments.

Nonlocal elasticity of Klein–Gordon type: Fundamentals and wave propagation

A nonlocal elasticity theory with nonlocality in space and time is developed by considering nonlocal constitutive equations with a dynamical scalar nonlocal kernel function. The proposed theory is specified to isotropic nonlocal elasticity of Klein–Gordon type, which is an extension of nonlocal elasticity of Helmholtz type, and it possesses one characteristic internal length scale parameter as well as one characteristic internal time scale parameter in addition to the elastic material parameters. Dynamical nonlocal kernels are analytically derived as retarded Green functions of the Klein–Gordon operator. The dispersion relations of the considered isotropic nonlocal elasticity model of Klein–Gordon type are analytically determined. The obtained results reveal the advantage of the proposed nonlocal elasticity model, that is, its ability to predict for the first time in the framework of nonlocal elasticity in addition to the acoustic modes (low-frequency modes), optic modes (high-frequency modes) as well as frequency band-gaps between the acoustic and optic modes. A qualitative examination of the phase and group velocities for all four modes (acoustic and optic branches of longitudinal and transverse waves) shows that the proposed model allows for physically realistic dispersive wave propagation.

Measuring a Rogue? An Investigation into an Apparent Giant Wave

Abstract An apparent giant wave event having a maximum trough-to-crest height of 21 m and a maximum zero-upcrossing period of 27 s was recorded by a wave buoy at a nearshore location off the southwestern coast of Australia. It appears as a group of waves that are significantly larger both in height and in period than the waves preceding and following them. This paper reports a multifaceted analysis into the plausibility of the event. We first examine the statistics of the event in relation to the rest of the record, where we look at quantities such as maximum-to-significant wave height ratios, ordered crest–trough statistics, and average wave profiles. We then investigate the kinematics of the buoy, where we look at the relationship between the horizontal and vertical displacements of the buoy, and also attempt to numerically reconstruct the giant event using Boussinesq and nonlinear shallow water equations. Additional analyses are performed on other sea states where at least one of the buoy’s accelerometers reached its maximum limit. Our analysis reveals incompatibilities of the event with known behavior of real waves, leading us to conclude that it was not a real wave event. Wave events similar to the one reported in our study have been reported elsewhere and have sometimes been accepted as real occurrences. Our methods of forensically analyzing the giant wave event should be potentially useful for identifying false rogue wave events in these cases.