Towards simulation of 3D nonlinear high-speed vessels motion

A numerical simulation algorithm based on the finite volume discretisation is presented for analyzing ship motions. The algorithm employs a fractional step method to deal with the coupling between the pressure and velocity fields. The free surface capturing is fulfilled by using a volume of fluid method in which the interface between the liquid (water) and gas (air) phases are computed by solving a scalar transport equation for the volume fraction of the liquid phase. The computed velocity field is employed to evaluate the acting forces and moments on the vessel. Using the strategy of boundary-fitted body-attached mesh and calculating all six degrees-of-freedom of motion in each time step, time history of ship motions including displacements, velocities and accelerations are evaluated.To verify the proposed algorithm, a series of verification tests are conducted. First, a two-dimensional asymmetrical wedge slamming is simulated as a simple type of a common case for high-speed vessels. Then, the steady-state forward motion of a high-speed planing catamaran is investigated. Results of both test cases show good agreement with experimental data. It is concluded that the proposed algorithm can be a promising strategy for both performance prediction and design of high-speed vessels.     

A Unified Computational Method for Simulating Dynamic Behavior of Planing Vessels

High speed planing hulls have complex hydrodynamic behaviors. The trim angle and drafts are very sensitive to speed and location of the center of gravity. Therefore, motion simulation for such vessels needs a strong coupling between rigid body motions and hydrodynamic analysis. In addition, free surface should be predicted with good accuracy for each time step. In this paper, velocity and pressure fields are coupled by use of the fractional step method. On the basis of integration of the two-phase viscous f...     

A probabilistic framework for floodplain mapping using hydrological modeling and unsteady hydraulic modeling

ABSTRACT

Prediction of design hydrographs is key in floodplain mapping using hydraulic models, which are either steady state or unsteady. The former, which require only an input peak, substantially overestimate the volume of water entering the floodplain compared to the more realistic dynamic case simulated by the unsteady models that require the full hydrograph. Past efforts to account for the uncertainty of boundary conditions using unsteady hydraulic modeling have been based largely on a joint flood frequency–shape analysis, with only a very limited number of studies using hydrological modeling to produce the design hydrographs. This study therefore presents a generic probabilistic framework that couples a hydrological model with an unsteady hydraulic model to estimate the uncertainty of flood characteristics. The framework is demonstrated on the Swannanoa River watershed in North Carolina, USA. Given its flexibility, the framework can be applied to study other sources of uncertainty in other hydrological models and watersheds.     

Assessment of As and Cd contamination in topsoils of Northern Ghorveh (Western Iran): role of parent material, land use and soil properties

The aim of this study was to investigate the influences of land use, parent materials (rock types) and soil properties on total arsenic and cadmium concentrations in the agricultural soils. A total of 87 surface (0–20 cm) soil samples were collected from four types of land use: irrigated farming, rangeland, dry farming and orchard. The average concentrations of the analyzed elements in topsoil were 84.426 mg As/kg and 3.289 mg Cd/kg. In addition, the pH, organic matter (OM), cation exchange capacity (CEC), soil grain sizes and CaCO₃ were measured for each sample. The results indicated that land use had no significant effect on As and Cd concentrations. Our findings indicated that the Cd concentrations were influenced by bedrock composition, but for As there were no significant differences between various soil parent materials (bedrocks). Soil pollution was assessed on the basis of pollution index (PI), comprehensive pollution index (P _n ) and geoaccumulation index (I _geo). Calculated indices showed high-pollution levels for As and low- to moderate-pollution levels for Cd.     

Explicit data-driven models for prediction of pressure fluctuations occur during turbulent flows on sloping channels

Stochastic Environmental Research and Risk Assessment - Pressure fluctuations are among the favorite topics for hydraulic engineers due to their critical role in the design and safe operation of...     

Least-squares cross-wavelet analysis and its applications in geophysical time series

The least-squares wavelet analysis, an alternative to the classical wavelet analysis, was introduced in order to analyze unequally spaced and non-stationary time series exhibiting components with variable amplitude and frequency over time. There are a few methods such as cross-wavelet transform and wavelet coherence that can analyze two time series together. However, these methods cannot generally be used to analyze unequally spaced and non-stationary time series with associated covariance matrices that may have trends and/or datum shifts. A new method of analyzing two time series together, namely the least-squares cross-wavelet analysis, is developed and applied to study the disturbances in the gravitational gradients observed by GOCE satellite that arise from plasma flow in the ionosphere represented by Poynting flux. The proposed method also shows its outstanding performance on the Westford–Wettzell very long baseline interferometry baseline length and temperature series.     

Least-Squares Wavelet Analysis of Unequally Spaced and Non-stationary Time Series and Its Applications

Least-squares spectral analysis, an alternative to the classical Fourier transform, is a method of analyzing unequally spaced and non-stationary time series in their first and second statistical moments. However, when a time series has components with low or high amplitude and frequency variability over time, it is not appropriate to use either the least-squares spectral analysis or Fourier transform. On the other hand, the classical short-time Fourier transform and the continuous wavelet transform do not consider the covariance matrix associated with a time series nor do they consider trends or datum shifts. Moreover, they are not defined for unequally spaced time series. A new method of analyzing time series, namely, the least-squares wavelet analysis is introduced, which is a natural extension of the least-squares spectral analysis. This method decomposes a time series to the time–frequency domain and obtains its spectrogram. In addition, the probability distribution function of the spectrogram is derived that identifies statistically significant peaks. The least-squares wavelet analysis can analyze any non-stationary and unequally spaced time series with components of low or high amplitude and frequency variability, including datum shifts, trends, and constituents of known forms, by taking into account the covariance matrix associated with the time series. The outstanding performance of the proposed method on synthetic time series and a very long baseline interferometry series is demonstrated, and the results are compared with the weighted wavelet Z-transform.