Improvement of double-peaked spectra: Revisiting the combination of the Gaussian and the JONSWAP models

Akbari, Hassan; Panahi, Roozbeh; Amani, Leila

doi:10.1016/j.oceaneng.2020.106965

Cited by 17 publications

(6 citation statements)

References 10 publications

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“…Secondly, all the measurements were carried out at very short distances of less than 1500 m from the shoreline. Physical conditions where highquality wave data were previously obtained at short fetches (see, e.g., [28,[33][34][35][36]95]) differ dramatically from those of the northeastern Black Sea nearshore. The mountainous relief with a height of up to 550 m and cliff on the coast modify the wind flow significantly.…”

Section: Discussionmentioning

confidence: 99%

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Wave Buoy Measurements at Short Fetches in the Black Sea Nearshore: Mixed Sea and Energy Fluxes

Rybalko

Myslenkov

Badulin

2023

Water

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Wave buoy measurements were carried out near the northeastern Black Sea coast at the natural reserve Utrish in 2020–2021. In total, about 11 months of data records were collected during two stages of the experiment at 600 and 1500 m offshore and depths of 18 and 42 m. The measured waves propagate almost exclusively from the seaward directions. Generally, the waves do not follow the local wind directions, thus, implying a mixed sea state. Nevertheless, dimensionless wave heights and periods appears to be quite close to the previously established empirical laws for the wind-driven seas. The results of the wave turbulence theory are applied for estimates of spectral energy fluxes and their correspondence to the energy flux from the turbulent wind pulsations. These estimates are consistent with today’s understanding of wind–wave interaction. It is shown that the main fraction of the wind energy flux is sent to the direct Kolmogorov–Zakharov cascade to high wave frequencies and then dissipates in small amounts. Less than 1% of the wind energy flux is directed to the low frequency band (the so-called inverse Kolmogorov–Zakharov cascade), thus, providing wave energy growth.

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Section: Discussionmentioning

confidence: 99%

“…Results of extensive wave buoy measurements in a close vicinity to the Iranian coast have been presented recently [33][34][35][36]. These experiments fixed two essential features.…”

Section: Introductionmentioning

confidence: 96%

Wave Buoy Measurements at Short Fetches in the Black Sea Nearshore: Mixed Sea and Energy Fluxes

Rybalko

Myslenkov

Badulin

2023

Water

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“…where A 1 and A 2 represent the maximum intensities of the trap and exciton peaks, respectively, while B 1 and B 2 denote the energies shifts associated with the trap and exciton peaks. This approach was chosen because it effectively captures 50 the characteristic features of the spectroscopic data.…”

Section: Dataset Processing and Forecastingmentioning

confidence: 99%

Exploring Temperature-Dependent Photoluminescence Dynamics in Colloidal CdSe Nanoplatelets using Machine Learning Approach

Malashin,

Daibagya,

Tynchenko

et al. 2024

Preprint

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The study explore machine learning techniques to predict temperature-dependent photoluminescence spectra in colloidal CdSe nanoplatelets, leveraging polynomial regression models trained on experimental data from 85 to 270 K spanning temperatures to forecast photoluminescence spectra backward to 0 K and forward to 300 K. 6th-degree polynomial models with Tweedie regression were optimal for band energy B1 predictions up to 300 K, while 9th-degree models with LassoLars and Linear Regression regressors were suitable for backward predictions to 0 K. For exciton energy B2, the Lasso model of degree 5 and the Ridge model of degree 4 performed well up to 300 K, while the Tweedie model of degree 2 and Theil-Sen model of degree 2 showed promise for predictions to 0 K. Furthermore, we utilize a GA-based approach to fit experimental data to theoretical model of Fan and Varshni equations, facilitating a comparative analysis with the ML-predicted curves.

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“…Torsethaugen [29,30], for instance, proposed a method using two JONSWAP spectra to compose a bimodal spectrum and validated the effectiveness of the simplified model through measured data from the Norwegian continental shelf. Another approach involves using a Gaussian spectrum to describe the primary component and JONSWAP to describe the secondary component [31]. The widely used bimodal spectrum is the Ochi-Hubble spectrum with six parameters, capable of separately describing swell spectra from distant sources and wind wave spectra generated by local wind fields, effectively capturing wave conditions during typhoon storm surges [32][33][34].…”

Section: Introductionmentioning

confidence: 99%

Analysis of Unidirectional Wave Spectral Characteristics in the Northeastern Waters of Taiwan

Chao,

Lin,

et al. 2023

JMSE

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Marine energy development has been actively promoted in recent years. Analyzing wave data in the surrounding sea areas is crucial for improving wave power generator efficiency. This study collected unidirectional wave spectrum data from the northeast coastal area of Taiwan (e.g., Pengjia Islet, Fugui Cape, and NTOU). It aimed to analyze the main wave types (i.e., wind waves, swell, or mixed sea) and their proportions during the northeast and non-northeast monsoon periods. The results indicate that swell waves dominate at the NTOU buoy, while wind waves dominate at the other two stations. In addition, the analysis of wave spectra ratios revealed a low percentage of unimodal spectra (only about 0.5%), and bimodal and unformed spectra presented as predominant. Furthermore, this study also introduced a brand new JW-J wave spectrum, which combines the JW and JONSWAP spectra to describe low-frequency (swell) and high-frequency (wind wave) systems, showing superior performance compared to the Torsethaugen spectrum. Finally, this research analyzed the JW-J spectrum parameters from the Pengjia Islet and Fugui Cape stations and applied these results to the NTOU bouy for effectively describing wind/swell wave variations.

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Improvement of double-peaked spectra: Revisiting the combination of the Gaussian and the JONSWAP models

Cited by 17 publications

References 10 publications

Wave Buoy Measurements at Short Fetches in the Black Sea Nearshore: Mixed Sea and Energy Fluxes

Wave Buoy Measurements at Short Fetches in the Black Sea Nearshore: Mixed Sea and Energy Fluxes

Exploring Temperature-Dependent Photoluminescence Dynamics in Colloidal CdSe Nanoplatelets using Machine Learning Approach

Analysis of Unidirectional Wave Spectral Characteristics in the Northeastern Waters of Taiwan

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