Stochastic Analysis and Probabilistic Prediction of Random Seas

Ochi, Michel K.

doi:10.1016/b978-0-12-021813-4.50010-x

Cited by 61 publications

(34 citation statements)

References 64 publications

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“…Analogous problems with log-normal-like probability density functions also appear for other extended systems with fractal (multifractal) behavior. These are (for example): the problem of large oceanic waves [19]; percolation in porous formations [20] and percolation of random resistor networks (backbones) [21], [22]. The simple hierarchical models (such as the Novikov-Stewart model for turbulence [1] and the de Arcangelis et al model for backbones of critical percolation clusters [21]) have analytically calculated multifractal asymptotics (i.e., definite D ∞ values) and, therefore, cannot be described in terms of lognormal-like PDFs (which cannot lead to definite D ∞ ).…”

Section: Discussionmentioning

confidence: 99%

Singularities in multifractal turbulence dissipation networks and their degeneration

Bershadskii¹,

Gibson²

1994

Physica A: Statistical Mechanics and its Applications

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We suggest that large-scale turbulence dissipation is concentrated along caustic networks (that appear due to vortex sheet instability in three-dimensional space), leading to an effective fractal dimension D eff = 5/3 of the network backbone and a turbulence intermittency exponent µ = 1/6. Actually, D ef f < 5/3 and µ > 1/6 due to singularities on these caustic networks. It is shown (using the theory of caustic singularities) that the strongest (however, stable on the backbone) singularities lead to D eff = 4/3 (an elastic backbone) and to µ = 1/3. Thus, there is a restriction of the network fractal variability: 4/3 < D eff < 5/3, and consequently: 1/6 < µ < 1/3.Degeneration of these networks into a system of smooth vortex filaments: D ef f = 1, leads to µ = 1/2. After degeneration, the strongest singularities of the dissipation field, ε, lose their power-law form, while the smoother field lnε takes it. It is shown (using the method of multifractal asymptotics) that the probability distribution of the dissipation changes its form from exponential-like to log-normal-like with this degeneration, and that the multifractal asymptote of the field lnε is related to the multifractal asymptote of the energy field.Finally, a phenomenon of acceleration of large-scale turbulent diffusion of passive scalar by the singularities is briefly discussed.All results are based on experimental data.

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Section: Discussionmentioning

confidence: 99%

Singularities in multifractal turbulence dissipation networks and their degeneration

Bershadskii¹,

Gibson²

1994

Physica A: Statistical Mechanics and its Applications

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“…Most wind wave problems address storm wave amplitudes or frequency spectra over considerable area or for considerable duration [Kinsman, 1965;Ochi, 1982]. Wind generates appreciable water waves slowly, in part because the density of the forcing fluid is almost 1000 times less than the density of the fluid forming the waves.…”

Section: Plume Shear Mechanismmentioning

confidence: 99%

Theoretical analysis of tsunami generation by pyroclastic flows

Watts¹,

Waythomas

2003

J. Geophys. Res.

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[1] Pyroclastic flows are a common product of explosive volcanism and have the potential to initiate tsunamis whenever thick, dense flows encounter bodies of water. We evaluate the process of tsunami generation by pyroclastic flow by decomposing the pyroclastic flow into two components, the dense underflow portion, which we term the pyroclastic debris flow, and the plume, which includes the surge and coignimbrite ash cloud parts of the flow. We consider five possible wave generation mechanisms. These mechanisms consist of steam explosion, pyroclastic debris flow, plume pressure, plume shear, and pressure impulse wave generation. Our theoretical analysis of tsunami generation by these mechanisms provides an estimate of tsunami features such as a characteristic wave amplitude and wavelength. We find that in most situations, tsunami generation is dominated by the pyroclastic debris flow component of a pyroclastic flow. This work presents information sufficient to construct tsunami sources for an arbitrary pyroclastic flow interacting with most bodies of water.

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“…Considerando que o mar se apresenta na superfície de forma irregular, a necessidade de poder prever o comportamento dos navios requer um estudo estatístico [26].…”

Section: Ondas De Superfícieunclassified

“…Isto de fato é considerado na teoria de ondas [36]. Uma partícula tem uma movimentação harmônica simples ao longo do eixo "X" quando sua elongação x é expressa pela equação 4.1 [26] ‫ݔ‬ = ‫ܣ‬ cos (߱ * ‫ݐ‬ + ߮ ) (4.1)…”

Section: Figura 4-1 Parâmetros De Ondaunclassified

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Efeito da corrosão do chapeamento do fundo do casco sobre a confiabilidade estrutural de navios.

Rodriguez¹,

Alberto²

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Stochastic Analysis and Probabilistic Prediction of Random Seas

Cited by 61 publications

References 64 publications

Singularities in multifractal turbulence dissipation networks and their degeneration

Singularities in multifractal turbulence dissipation networks and their degeneration

Theoretical analysis of tsunami generation by pyroclastic flows

Efeito da corrosão do chapeamento do fundo do casco sobre a confiabilidade estrutural de navios.

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