Alok K. Jha

Anna Palau

Tamio Endo

Paolo Mele

Kosmas Prassides

Chiara Tarantini

Petre Badica

This book provides readers with a comprehensive overview of the science of superconducting materials. It serves as a fundamental information source on the actual techniques and methodologies involved in superconducting materials growth, characterization and processing. This book includes coverage of several categories of medium and high-temperature superconducting materials: cuprate oxides, borides, and iron-based chalcogenides and pnictides. Provides a single-source reference on superconducting materials growth, characterization and processing; Bridges the gap between materials science and applications of superconductors; Discusses several categories of superconducting materials such as cuprate oxides, borides, and iron-based chalcogenides and pnictides; Covers synthesis, characterization, and processing of superconducting materials, as well as the nanoengineering approach to tailor the properties of the used materials at the nanoscale level.

From Materials Science to Practical Applications

Superconductivity

"Second-order nonlinear models have been increasingly used in recent years model nonlinear processes in offshore engineering. We develop convenient analytic formulae to predict the nonlinearities in waves and to predict the crest height distribution in a specified wave condition. We apply such models to study the properties of random ocean waves. These include measured waves both in wave tanks and in field. Statistics comparison between model and measurements include: moment comparisons, comparison of distributions of wave elevations, crest heights, wave heights, and conditional distributions of local wave parameters, for example, crest height given wave heights, wave periods given crest heights, among others. We find the second-order model predictions to agree quite closely with the field measurements, while the wave tank statistics seem to be underpredicted by the second-order model. Finally, we solve the inverse problem, in which we identify the underlying first-order wave components, which when run through the second-order wave predictor matches the measured wave histories time point by time point."--V.

Prediction and Comparison with Data

Nonlinear Random Ocean Waves

WAVEMAKER is a FORTRAN subroutine to simulate random non-Gaussian ocean wave histories. It generates a first-order (Gaussian) wave process with an arbitrary power spectrum, and applies nonlinear corrections based on second-order hydrodynamics. Inputs to the routine include the first-order spectrum, the water depth, and a set of locations in the along-wave direction at which wave elevation histories are desired. It may thus provide useful input to estimate loads on spatially distributed ocean structures and ships. The WAVEMAKER package also includes a separate driver program, which facilitates input/output and generates several analytical spectral models. Its input is specified in command-line format, similar to that of the TF-POP program for hydrodynamic post-processing also developed in the Stanford RMS program. An example problem is included to demonstrate the use of WAVEMAKER and its driver. In terms of methodology, WAVEMAKER first uses standard frequency domain methods to generate first-order Gaussian histories at each location. For each of these, WAVEMAKER then evaluates the full set of second-order corrections according to hydrodynamic theory. Thus the first-order wave process, with N components at frequencies Wn, gives rise to a total of N2 corrections, spread Over all sum frequencies Wn + Wm, and to another N2 corrections over all difference frequencies Wn - Wm. WAVEMAKER also includes the ability to identify the underlying first-order Gaussian history from a given observed time history. This feature is particularly attractive for use in situations where the second-order non-linearity in the waves is built-in into the structural response calculations. To avoid doublecounting therefore, the input waves should be filtered to remove any second-order nonlinearity. WAVEMAKER takes in an input wave history and identifies its first- and second-order wave components. This identification, an inverse feature to simulation, is based on a Newton- Raphs.

Simulation and Identification of Second-order Random Waves

Wavemaker 2.0

"Second-order nonlinear models have been increasingly used in recent years to model nonlinear processes in offshore engineering. We apply such models to study the global responses of a spar floating platform. Traditional linear models of wave loads on offshore structures tend to be inaccurate in the response predictions. Second-order nonlinear models although computationally more expensive provide an opportunity to better predict these loads. Recent tests conducted on a spar floating platform offer wave tank data for extreme conditions in both the Gulf of Mexico and the North Sea. We model the tested spar floating platform as a linear rigid-body with 6 degrees of freedom. The incident wave loads are modeled as a second-order phenomenon. Of interest is the global response of the spar, which here is the total horizontal displacement near the spar deck. Although the apparent transient response and the few measured response cycles pose difficulties in calibrating the model, we find the model to offer reasonable predictions when compared to the measured results in wave tanks."--V.

Numerical Analysis and Comparison with Data

Spar Floating Platform

"Stochastic methods are described here to predict the effects of nonlinear ship loads on fatigue accumulation in random seas. These are found to be capable of predicting the net fatigue damage over many ship response cycles, at a fraction of the cost of direct time-domain analysis. It is also shown how these nonlinear ship load models can be used within a full reliability analysis of ship components. This uses first-order reliability methods to estimate not only the net effect of all uncertainty sources, but also the relative contribution of each (e.g., load vs. material property variability). In our ship load application, nonlinear effects are found to increase the relative impact of load (and wave) modelling and its uncertainty."--V.

Alok K. Jha

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