Note on the Application of Transient Wave Packets for Wave–Ice Interaction Experiments

Klein, Marco; Hartmann, Moritz; Polach, Franz von Bock und

doi:10.3390/w13121699

Cited by 8 publications

(7 citation statements)

References 38 publications

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“…with amplitude A i , wave number k i and random phase ϕ i ∈ [0, 2π]. The amplitudes are defined by a normalized Fourier spectrum (Klein et al, 2021)…”

Section: Data Generation -Dispersive Surface Wavesmentioning

confidence: 99%

Surface Similarity Parameter: A New Machine Learning Loss Metric for Oscillatory Spatio-Temporal Data

Wedler,

Stender,

Klein

et al. 2022

Preprint

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Supervised machine learning approaches require the formulation of a loss functional to be minimized in the training phase. Sequential data are ubiquitous across many fields of research, and are often treated with Euclidean distance-based loss functions that were designed for tabular data. For smooth oscillatory data, those conventional approaches lack the ability to penalize amplitude, frequency and phase prediction errors at the same time, and tend to be biased towards amplitude errors. We introduce the surface similarity parameter (SSP) as a novel loss function that is especially useful for training machine learning models on smooth oscillatory sequences. Our extensive experiments on chaotic spatio-temporal dynamical systems indicate that the SSP is beneficial for shaping gradients, thereby accelerating the training process, reducing the final prediction error, and implementing a stronger regularization effect compared to using classical loss functions. The results indicate the potential of the novel loss metric particularly for highly complex and chaotic data, such as data stemming from the nonlinear two-dimensional Kuramoto-Sivashinsky equation and the linear propagation of dispersive surface gravity waves in fluids.

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“…with amplitude A i , wave number k i and random phase ϕ i ∈ [0, 2π]. The amplitudes are defined by a normalized Fourier spectrum (Klein et al, 2021)…”

Section: Data Generation -Dispersive Surface Wavesmentioning

confidence: 99%

Surface Similarity Parameter: A New Machine Learning Loss Metric for Oscillatory Spatio-Temporal Data

Wedler,

Stender,

Klein

et al. 2022

Preprint

Self Cite

View full text Add to dashboard Cite

show abstract

“…Furthermore, the dispersion relation of the underlying wave components of the TWP are also obtained via one test run over a wide frequency range. Klein et al [47] have introduced the TWP technique to wave-ice interaction model test. They have shown that TWP's are not only suitable but very efficient for wave-ice interaction investigations.…”

Section: Experimental Program and Generated Wavesmentioning

confidence: 99%

“…They have shown that TWP's are not only suitable but very efficient for wave-ice interaction investigations. An overview over the TWP concept used in our experiments is found in Figure 8 and for further reading we refer to Klein et al [47].…”

Section: Experimental Program and Generated Wavesmentioning

confidence: 99%

A New Model Ice for Wave-Ice Interaction

Polach

Klein

Hartmann

2021

Water

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The interaction of waves and ice is of significant relevance for engineers, oceanographers and climate scientists. In-situ measurements are costly and bear uncertainties due to unknown boundary conditions. Therefore, physical laboratory experiments in ice tanks are an important alternative to validate theories or investigate particular effects of interest. Ice tanks use model ice which has down-scaled sea ice properties. This model ice in ice tanks holds disadvantages due to its low stiffness and non-linear behavior which is not in scale to sea ice, but is of particular relevance in wave-ice interactions. With decreasing stiffness steeper waves are required to reach critical stresses for ice breaking, while the non-linear, respectively non-elastic, deformation behavior is associated with high wave damping. Both are scale effects and do not allow the direct transfer of model scale test results to scenarios with sea ice. Therefore, the alternative modeling approach of Model Ice of Virtual Equivalent Thickness (MIVET) is introduced. Its performance is tested in physical experiments and compared to conventional model ice. The results show that the excessive damping of conventional model ice can be reduced successfully, while the scaling of the wave induced ice break-up still requires research and testing. In conclusion, the results obtained are considered a proof of concept of MIVET for wave-ice interaction problems.

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“…Laboratory-scale physical modelling is generating understanding of wave-ice interactions to complement field observations and theory. Wave basin experiments are used to investigate wave attenuation (and related wave transmission/reflection) due to plastic or wooden floes [Bennetts et al, 2015b, Bennetts and Williams, 2015, Toffoli et al, 2015a, Nelli et al, 2017, freshwater ice and model ice (saline or doped), with most up to 2020 listed by Parra et al [2020] and others since then [Alberello et al, 2021b, Klein et al, 2021. Few wave-basin experiments investigate wave-induced breakup.…”

Section: Introductionmentioning

confidence: 99%

“…To date, almost all physical models of wave-ice interactions use regular incident waves, thus obscuring nonlinear processes for irregular sea-states, such as ice breakup, floe-floe collisions and overwash. The only exception is Klein et al [2021], who use transient wave packets, in part to avoid ice breakup.…”

Section: Introductionmentioning

confidence: 99%

Interactions between Irregular Wave Fields and Sea Ice: A Physical Model for Wave Attenuation and Ice Breakup in an Ice Tank

Passerotti

Bennetts

Polach

et al. 2022

Journal of Physical Oceanography

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Irregular, unidirectional surface water waves incident on model ice in an ice tank are used as a physical model of ocean surface wave interactions with sea ice. Results are given for an experiment consisting of three tests, starting with a continuous ice cover and in which the incident wave steepness increases between tests. The incident waves range from causing no breakup of the ice cover to breakup of the full length of ice cover. Temporal evolution of the ice edge, breaking front and mean floe sizes are reported. Floe size distributions in the different tests are analysed. The evolution of the wave spectrum with distance into the ice-covered water is analysed in terms of changes of energy content, mean wave period and spectral bandwidth relative to their incident counterparts, and pronounced differences are found between the tests. Further, an empirical attenuation coefficient is derived from the measurements and shown to have a power-law dependence on frequency comparable to that found in field measurements. Links between wave properties and ice breakup are discussed.

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Note on the Application of Transient Wave Packets for Wave–Ice Interaction Experiments

Cited by 8 publications

References 38 publications

Surface Similarity Parameter: A New Machine Learning Loss Metric for Oscillatory Spatio-Temporal Data

Surface Similarity Parameter: A New Machine Learning Loss Metric for Oscillatory Spatio-Temporal Data

A New Model Ice for Wave-Ice Interaction

Interactions between Irregular Wave Fields and Sea Ice: A Physical Model for Wave Attenuation and Ice Breakup in an Ice Tank

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