Bulletin of the American Physical Society
76th Annual Meeting of the Division of Fluid Dynamics
Sunday–Tuesday, November 19–21, 2023; Washington, DC
Session J44: Waves: Surface Waves III |
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Chair: Kianoosh Yousefi, University of Texas at Dallas Room: 208AB |
Sunday, November 19, 2023 4:35PM - 4:48PM |
J44.00001: Investigation of Wind Forcing on the Energy Dissipation of Solitary Waves in a Storm Surge Hunter Boswell, Guirong Yan, Wouter D Mostert Despite being a key aspect of the energy transfer between the ocean and atmosphere, the detailed dynamics of wave breaking, especially under the influence of wind, are not yet fully understood. We present direct numerical simulations of two-dimensional solitary waves that shoal and break in shallow water on a uniform beach slope with an inundated inshore storm surge area, under low to medium wind loading conditions. The primary parameters are the beach slope, wave amplitude and wind speed. The wave overturning is fully modeled, and both plunging and spilling breakers are observed. The wave form drag, surface pressure amplitude and phase shift are reported and compared with the results from prior numerical and experimental studies. We explore the wind loading effect on the breaker properties and the energy dissipation of the wave during breaking; and compare breaking energy dissipation rates with current inertial models which were developed without consideration of wind. |
Sunday, November 19, 2023 4:48PM - 5:01PM |
J44.00002: Wind-Ocean Surface Wave Interactions: Growth of Bound and Free Waves under Wind Forcing Clara Martín Blanco, Luc Deike, Jiarong Wu We present an analysis of wind-ocean surface wave interactions using direct numerical simulations with the Basilisk Solver, following the work from Wu, Popinet, and Deike (2022). Surface waves are initialized as third-order Stokes waves with steepness (ak), ranging from 0.15 to 0.3, and are forced by a turbulent boundary layer characterized by the ratio of the wind friction velocity and the phase speed of the carrier wave (u∗/c), ranging from 0.1 to 0.5. While Wu et al. (2022) analyzed the growth of the carrier wave, we delve into the multi-scale behaviors of surface waves, including millimeter-sized capillary waves. |
Sunday, November 19, 2023 5:01PM - 5:14PM |
J44.00003: On the turbulence and wave-induced stresses imposed by the airflow above surface waves Peisen Tan, Ivan Savelyev, Janina Tenhaus, Marc Buckley, Sydney Wray, Nathan Laxague, Milan Curcic, David Wang, Siliva Matt, Brian Haus, Sanchit Mehta When wind blows over waves, the wave's windward side shelters the leeward side, inducing a windward high pressure and a leeward low pressure. Buckley & Veron (2019) reported wave's phase-locked turbulent kinetic energy and intermittent flow separation in the air from Particle Image Velocimetry results. They suggested the waves' amplitude, frequency and wind speed affect the structure of the airflow and wind stress above the wave, which in turn fuel the wave growth. However, systematic research on the airflow structure above waves with different amplitudes and frequency has been scarce. Therefore, we tested a wide range of wind-wave conditions in the wave tank facility at the University of Miami accompanied by vertical profilings of air pressure with Elliot probes and wind speed with hotfilm anemometer above the waves with high acquisition frequency. We report different wave growth mechanisms: under low & moderate wind forcing, the waves grow from a pressure work "suction" effect on the crests due to the combination of pressure and airflow. Under strong wind forcing, however, a high form stress caused by pressure dipoles at waves' windward / leeward dominates the wave growth. This is accompanied by airflow separation and vortices on the leeward side. Eventually, this study will provide insights on future wave models' wind input parameterizations. |
Sunday, November 19, 2023 5:14PM - 5:27PM |
J44.00004: Predictable Zone of Phase Resolved Wave Reconstruction Using Adjoint Method Ali Barzegari, Jie Wu, Lian Shen Accurate prediction of wave propagation is one of the long-lasting problems in fluid mechanics and physical oceanography. Its importance lies in the safety of ocean vehicles and the control of wave energy converters. Gaining insight into reliable spatial and temporal prediction zones obtained using reconstruction method is highly important in applications. In this study, we investigate the predictable zone of the wave reconstruction problem using the adjoint method and compare it with linear predictable zone theory. The reconstruction is conducted to obtain the optimal initial condition that minimizes the discrepancy between measured and modeled data. We consider different configurations of the wave elevation measurements, including single measurement of unidirectional waves, multiple measurement of unidirectional waves, and measurement of multidirectional waves, to compare the real predictable zone with the linear predictable zone. The results provide insights into the reliable spatial and temporal zones for forecasting waves under different wave scenarios. |
Sunday, November 19, 2023 5:27PM - 5:40PM |
J44.00005: What leads to Stokes drift? Anirban Guha, Akanksha Gupta Here we answer a fundamental question - "What leads to Stokes drift?" Although overwhelmingly understood for water waves, Stokes drift is a generic mechanism occurring in any non-transverse wave in fluids. To further clarify this point, we undertake a fully Lagrangian approach and put the pathline equation of sound (1D) and water (2D) waves into perspective. We show that the 2D pathline equation of water waves is reducible to 1D when expressed in terms of the Lagrangian phase θ. Therefore we posit that the pathline equation is essentially 1D for all kinds of waves in fluids. We solve the respective pathline equation for sound and water waves using asymptotic methods to obtain a parametric representation of particle position x(θ) and elapsed time t(θ). The parametric description has allowed us to show that Stokes drift is a consequence of wave kinematics and arises because a particle in a linear wave field spends more time, undergoes greater horizontal displacement, and travels at a faster average horizontal velocity in the crest phase in comparison to the trough phase. Finite amplitude waves may add nuances, however, the above-mentioned understanding is generally valid. We substantiate all our arguments with second-order-accurate quantitative estimates. |
Sunday, November 19, 2023 5:40PM - 5:53PM |
J44.00006: Investigation of wake patterns of moving disturbances using convolutional neural networks Xuanting Hao Kelvin waves result from the interaction between ship and surface waves and represent a distinctive phenomenon. Understanding their patterns is of utmost importance for coastal protection, as these waves can persist for extended periods. The characteristics of Kelvin waves are influenced by the speed and lengthscales of the ship, which have been extensively studied through hydrodynamic simulations. However, recent advancements in machine learning offer an alternative approach to explore their underlying physics. In this presentation, I will share a preliminary investigation into the wave patterns generated by moving disturbances, utilizing convolutional neural networks (CNNs). To simulate this physical system, we employ a high order spectral method that solves the Zakharov equations. To create a comprehensive dataset, we consider a wide range of values for the length scales, distributions, and propagating velocities of the moving pressure. Subsequently, we train a residual neural network (ResNet) using this dataset, and the ResNet is employed to predict the speed of the pressure disturbance. The results demonstrate the ResNet's prediction ability with impressive accuracy and robustness against random noise, highlighting the significant potential of CNNs in advancing our understanding of Kelvin waves and their dynamics. |
Sunday, November 19, 2023 5:53PM - 6:06PM |
J44.00007: Geometric effects in water waves theory Rouslan Krechetnikov In this talk, general rethinking of the mathematical foundations of water waves in shallow and deep water limits is followed by the discussion of the distinctions between planar and cylindrical waves. Different asymptotic regimes are explored and lead to new equations governing weakly nonlinear wave evolution. |
Sunday, November 19, 2023 6:06PM - 6:19PM |
J44.00008: Simultaneous Nonlinear Wave and Ship Motion Forecast via Data Assimilation Guangyao Wang, Yulin Pan A reliable near-future phase-resolved ocean wave forecast plays a crucial role in marine operations. With the development of the remote sensing and computational technologies, it is now possible to reconstruct the initial phase-resolved ocean surface from radar measurements and launch a nonlinear wave model such as the high-order spectral (HOS) method to predict the wave evolution in real time. However, due to the unavoidable errors in model configurations (e.g., initial conditions and physical parameters) and the chaotic nature of the nonlinear wave equations, the prediction by HOS can deviate quickly from the true dynamics. Recent studies, including those of the authors, have shown that this dilemma can be eased to some extent by incorporating the wave observational data into models via data assimilation methods such as ensemble Kalman filter (EnKF). In this work, we aim at the further improvement of wave prediction accuracy and simultaneous ship motion forecast. This is realized by coupling HOS, EnKF, and a Cummins-equation-based ship model, and including the observed ship motion as one additional data source. Through numerical testing, it is shown that the new integrated approach not only provides accurate ship motion forecast, but also uplifts the wave prediction accuracy compared to the state-of-the-art single-source (wave) data methods. |
Sunday, November 19, 2023 6:19PM - 6:32PM |
J44.00009: Reconstruction of skin friction drags for surface waves using convolutional neural network Kianoosh Yousefi, Gurpreet Singh Hora, Hongshuo Yang, Fabrice Veron, Marco G Giometto To improve the predictive abilities of weather and climate models, it is essential to understand the behavior of wind stress at the ocean surface. Wind stress is contingent on small-scale interfacial dynamics typically not directly resolved in numerical models. Although skin friction contributes considerably to the total stress up to moderate wind speeds, it is notoriously challenging to measure and predict using physics-based approaches. This work proposes a supervised machine learning (ML) model that estimates the spatial distributions of the skin friction drag over wind waves from wave profiles and 10 m wind speeds, which are relatively easy to acquire. The input-output pairs are high-resolution wave profiles and their corresponding surface viscous stresses collected from laboratory experiments. The ML model is built upon a convolutional neural network architecture that incorporates the Mish non-linearity as its activation function. Results show that the model can accurately predict the overall distribution of viscous stresses; it captures the peak of viscous stress at/near the crest and its dramatic drop to almost null just past the crest, which can be an indicator of airflow separation. The predicted area-aggregate skin friction is also in excellent agreement with the corresponding measurements. The proposed method offers a fruitful pathway for estimating both local and area-aggregate skin friction and can be easily integrated into existing numerical models for air-sea interaction. |
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