Browsing by Author "Hao, Xuanting"

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    A Song of Atmosphere and Ocean: Modeling and Simulation-Based Study of Nonlinear Waves
    (2019-08) Hao, Xuanting
    Ocean waves play a critical role in connecting the atmosphere to the ocean and shaping the global climate. Existing statistics wave models are inappropriate for the mechanistic study of wave dynamics with nonlinearity and irregularity. We present a systematic study on nonlinear waves using deterministic models, with a focus on the fundamental momentum and energy transfer processes between waves and motions in the atmosphere and ocean. Numerical experiments are first carried out for turbulent wind blowing over a wave field with various wind speeds considered. The results show that the waves have a significant impact on the wind turbulent energy spectrum. By computing the universal constant in a wave-growth law proposed in the literature, we substantiate the scaling of wind--wave growth based on intrinsic wave properties. Another group of numerical experiments are conducted to investigate the momentum flux between wind and waves in the coastal region. We quantify the form drag and find it considerably larger in the coastal region case than in the open sea case. Our findings provide the direct computational evidence in support of the critical role wave shoaling plays in the increased coastal wind--wave momentum flux. In the final part, we investigate the oceanic internal wave impact on the surface wave. We directly capture the surface roughness signature with a deterministic two-layer model to avoid the singularity encountered in traditional ray theory. The surface signature characterized by a rough region followed by a smooth region traveling with a nondispersive internal wave is revealed by the local wave geometry variation in the physical space and the wave energy change in the spectral space. Our findings show that the formation of the surface signature is essentially an energy conservative process and justify the use of the wave-phase-resolved two-layer model.
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    Supporting Data for "A novel machine learning method for accelerated modeling of the downwelling irradiance field in the upper ocean"
    (2022-04-27) Hao, Xuanting; Shen, Lian; haoxx081@umn.edu; Hao, Xuanting; University of Minnesota Fluid Mechanics Lab
    The training data are generated from the Monte Carlo simulation of oceanic irradiance field. They can be used for training a neural network that significantly accelerates the prediction of irradiance in the upper ocean.
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    Supporting data for A Model Sensitivity Study of Ocean Surface Wave Modulation Induced by Internal Waves
    (2022-11-03) Wu, Jie; Ortiz-Suslow, David G; Hao, Xuanting; Wang, Qing; Shen, Lian; wuxx1417@umn.edu; Wu, Jie; University of Minnesota Fluid Mechanics Lab
    The dataset is generated using the two-layer fluid model to simulate internal and surface wave interaction. It can be used to analyze the impact of internal wave and surface wave properties on internal wave-induced surface wave modulation.
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    Supporting Data for Mechanistic study of shoaling effect on momentum transfer between turbulent flow and traveling wave using large-eddy simulation
    (2019-11-04) Hao, Xuanting; Cao, Tao; Shen, Lian; haoxx081@umn.edu; Xuanting, Hao; University of Minnesota St. Anthony Falls Laboratory
    The data are results of the large-eddy simulation of wind turbulence over monochromatic waves propagating in coastal and oceanic waters, described in the paper by Hao, Cao, and Shen "Mechanistic study of shoaling effect on momentum transfer between turbulent flow and traveling wave using large-eddy simulation " (published in Physical Review Fluids).