Effect of Nonlinear Wave Kinematics on Dynamic Response of Spars

The difference between various approximate methods to compute the wave kinematics and forces acting on a Spar platform up to the instantaneous free water surface is investigated. Three types of procedures are considered; i.e., extrapolation, stretching (both simple extensions of linear wave theory) and the hybrid wave model (which involves decomposition of the incident wave into first- and second-order components). Of particular interest for the dynamic response of a Spar are the nonlinear (second- and higher-order) low-frequency forces. The effects of the different procedures are compared analytically and numerically for the inertia forces using Morison et al.’s equation as reported in 1950, but the conclusions can be extended to diffraction theory formulations.     

Extended Fourth-Order Depth-Integrated Model for Water Waves and Currents Generated by Submarine Landslides

In this paper, a preexisting higher-order depth-integrated wave propagation model is extended to include a moving seabed. As a result, the extended model can be applied to both wave propagation and the dynamic process of wave generation by a seabed disturbance such as a submarine landslide. The model has the linear dispersion relation in a form of (4,4) Padè approximant, and approximates the water velocity profiles along the water depth with a fourth-order polynomial of the vertical coordinates. The fourth-order model is aimed at extending the validity of the lower-order depth-integrated models from long waves to both long and shorter waves, as well as improving the approximation of the velocity field from the second order to the fourth order. Laboratory experiments are carried out in a wave flume to study wave generation by a submerged landslide model. Both water waves and water velocities are measured by using resistance-type wave gauges and a particle image velocimetry. The experimental data are then compared with the predicted wave height and water current based on the new model and two existing lower-order Boussinesq-type models. The results clearly show that the new model predicts the fluid velocity more accurately and is also able to predict the shorter trailing waves very well where the traditional Boussinesq model may be inadequate, thus validating the improvement provided by the fourth-order model.     

Flux-Difference Splitting Schemes for 2D Flood Flows

Two numerical models for 2D flood flows are presented. One model is first-order accurate and another is second-order accurate. Roe's numerical flux is used to develop the first-order accurate model, while second-order accuracy, in space and time, is obtained by using the Lax-Wendroff numerical flux. A simple operator splitting is found to yield the same results as that obtained by using more complicated, and thus, time consuming, operator splitting. Roe's approximate Jacobian is used for conservative properties and Harten and Hyman's procedure is followed for the entropy inequality condition. Flux limiter is used in the second-order accurate model that removes oscillations while maintaining the order of accuracy. The models are verified against available experimental data of a 2D flood wave due to partial dam-break. Numerical experiments are conducted to verify the models' ability to correctly predict behavior of the free surface, in addition to prediction of depth and velocity.     

Hybrid bilayer membranes in air and water: infrared spectroscopy and neutron reflectivity studies

In this report we describe the fabrication and characterization of a phospholipid/alkanethiol hybrid bilayer membrane in air. The bilayer is formed by the interaction of phospholipid with the hydrophobic surface of a self-assembled alkanethiol monolayer on gold. We have characterized the resulting hybrid bilayer membrane in air using atomic force microscopy, spectroscopic ellipsometry, and reflection-absorption infrared spectroscopy. These analyses indicate that the phospholipid added is one monolayer thick, is continuous, and exhibits molecular order which is similar to that observed for phospholipid/phospholipid model membranes. The hybrid bilayer prepared in air has also been re-introduced to water and characterized using neutron reflectivity and impedance spectroscopy. Impedance data indicate that when moved from air to water, hybrid bilayers exhibit a dielectric constant and thickness that is essentially equivalent to hybrid bilayers prepared in situ by adding phospholipid vesicles to alkanethiol monolayers in water. Neutron scattering from these samples was collected out to a wave vector transfer of 0.25 A(-1), and provided a sensitivity to changes in total layer thickness on the order of 1-2 A. The data confirm that the acyl chain region of the phospholipid layer is consistent with that observed for phospholipid-phospholipid bilayers, but suggest greater hydration of the phospholipid headgroups of HBMs than has been reported in studies of lipid multilayers.     

Hybrid Modeling for Soft Sensing of Molten Steel Temperature in LF

 Aiming at the limitations of traditional thermal model and intelligent model, a new hybrid model is established for soft sensing of the molten steel temperature in LF. Firstly, a thermal model based on energy conservation is described; and then, an improved intelligent model based on process data is presented by ensemble ELM (extreme learning machine) for predicting the molten steel temperature in LF. Secondly, the self adaptive data fusion is proposed as a hybrid modeling method to combine the thermal model with the intelligent model. The new hybrid model could complement mutual advantage of two models by combination. It can overcome the shortcoming of parameters obtained on line hardly in a thermal model and the disadvantage of lacking the analysis of ladle furnace metallurgical process in an intelligent model. The new hybrid model is applied to a 300 t LF in Baoshan Iron and Steel Co Ltd for predicting the molten steel temperature. The experiments demonstrate that the hybrid model has good generalization performance and high accuracy.     

Modeling Acoustic Waves in Saturated Poroelastic Media

In this paper we present a comparison of the linear wave analysis for four models of poroelastic materials. A nonlinear thermodynamical construction of a two-component model of such materials requires a dependence on the porosity gradient. In the linear version this dependence may or may not be present. Consequently, we may work with the model without a dependence on this gradient which is identical to Biot’s model or we can use the so-called full model. In both cases we can construct simplified models without a coupling between partial stresses introduced by Biot. These simplified models have the advantage that their application to, for instance, surface wave analysis yields much simpler mathematical problems. In the present work we show that such a simplification for granular materials leads to a good qualitative agreement of all four models in ranges of porosity and Poisson’s ratio commonly appearing in geotechnical applications. Quantitative differences depend on the mode of propagation and vary between 10 and 20%. We illustrate the analysis with a numerical example corresponding to data for sands. Simultaneously we demonstrate severe limitations of the applicability of Gassmann relations which yield an instability of models in a wide range of practically important values of parameters.     

New approaches to studying problem behaviors: A comparison of methods for modeling longitudinal, categorical adolescent drinking data.

Analyzing problem-behavior trajectories can be difficult. The data are generally categorical and often quite skewed, violating distributional assumptions of standard normal-theory statistical models. In this article, the authors present several currently available modeling options, all of which make appropriate distributional assumptions for the observed categorical data. Three are based on the generalized linear model: a hierarchical generalized linear model, a growth mixture model, and a latent class growth analysis. They also describe a longitudinal latent class analysis, which requires fewer assumptions than the first 3. Finally, they illustrate all of the models using actual longitudinal adolescent alcohol-use data. They guide the reader through the model-selection process, comparing the results in terms of convergence properties, fit and residuals, parsimony, and interpretability. Advances in computing and statistical software have made the tools for these types of analyses readily accessible to most researchers. Using appropriate models for categorical data will lead to more accurate and reliable results, and their application in real data settings could contribute to substantive advancements in the field of development and the science of prevention. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Level A in vivo-in vitro correlation: nonlinear models and statistical methodology

Some new nonlinear models for the relationship between the fraction of drug dose dissolved (absorbed) in vivo and that dissolved in vitro are described. The models are empirical in nature and are generalizations of the linear model that, at present, is the most commonly used model. The modeling approach is based on considering the time at which a drug molecule goes into solution (in vitro or in vivo) to be a random variable and relating the distribution functions using proportional odds, proportional hazards, and proportional reversed hazards models. The models are further extended by allowing the parameter that relates in vivo and in vitro to be a function of time. A statistical model for the data is developed and used as the basis for a statistical methodology for fitting these models. The methods are shown to be generalized linear mixed effects model (GLMM) methods. The models are fitted to some data sets, and the results demonstrate that these models have potential.     

Modeling of Lake Ice Characteristics in North America Using Climate, Geography, and Lake Bathymetry

Ice cover records from 128 freshwater lakes in the United States and Canada were analyzed. Multivariable linear regression models, log-transform models, and a combination of the two (the “hybrid” form) were used to express ice-in date, ice-out date, and maximum ice thickness as functions of mean air temperature, latitude, average depth, elevation, and surface area of each lake. Mean air temperatures are for periods from September 1 to December 31 for ice-in dates, February 1 to June 30 for ice-out dates, and September 1 to June 30 for maximum ice thickness. Data for individual years as well as averages (over the record length) for each lake were analyzed. The log-transform formulas proved best for estimating ice-in date, while the hybrid form provided the best models of maximum ice thickness. The linear regression model estimated the ice-out date best. In most cases, mean air temperature and/or latitude were the most influential parameters, followed by elevation. Lake surface area and depth had a small or no influence. R2 values of all equations ranged from 0.50 to 0.92.     

The influence of crystallographic texture on diffraction measurements of residual stress

X-ray or neutron diffraction can be used to measure residual stresses in crystalline materials. Normally, the d-sin² Ω-method is used to derive the stress from the diffraction data. However, the method fails if the samples are anisotropic because of a crystallographic texture, The present paper gives an overview of this problem. The theoretical part gives an analysis that sheds some light on the influence of crystallographic texture on diffraction data obtained during stress measurements. It is based on linear elastic models (Reuss and Voigt). The next part reviews the experimental findings of various authors, which shows that linear elastic models are sometimes insufficient to deal with anisotropy, especially in cases when plastic deformation has taken place. Finally, several methods (including some that make use of the ODF) that are used when dealing with textured samples are reviewed.     

Predictive model selection for repeated measures random effects models using Bayes factors

The random effects model fit to repeated measures data is an extremely common model and data structure in current biostatistical practice. Modern data analysis often involves the selection of models within broad classes of prespecified models, but for models beyond the generalized linear model, few model-selection tools have been actively studied. In a Bayesian analysis, Bayes factors are the natural tool to use to explore these classes of models. In this paper, we develop a predictive approach for specifying the priors of a repeated measures random effects model with emphasis on selecting the fixed effects. The advantage of the predictive approach is that a single predictive specification is used to specify priors for all models considered. The methodology is applied to a pediatric pain data analysis.     

Dam Break in Channels with 90° Bend

In practice, dam-break modeling is generally performed using a one-dimensional (1D) approach for its limited requirements in data and computation. However, for valleys with multiple sharp bends, such a 1D model may fail for predicting as well the maximum water level as the wave arrival time. This paper presents an experimental study of a dam-break flow in an initially dry channel with a 90° bend, with refined measurements of water level and velocity field. The measured data are compared to some numerical results computed with finite-volume schemes associated with Roe-type flux calculation. The 1D approach reveals the expected limits, while a full two-dimensional (2D) approach provides fine level prediction and rather satisfactory information about the arrival time. A hybrid approach is now proposed, mixing the 1D model for the straight reaches and local 2D models for the bends. The compatibility of the Roe fluxes at the interfaces requires a careful formulation, but the resulting scheme seems able to capture reflection and diffraction processes in such a way that the results are really good in what concerns the water level.     

An application of a linear statistical model in clinical anesthesia practice

One of the important topics in clinical practice of anesthesia, is "prediction". We have to predict what will happen next after obtaining some imperfect data about the patient. The standard and most popularized method is the one utilizing a linear statistical model. This linear model in prediction is based, in part, on the information theory, which says that the volume of information is the linear combination of its components. One of the characteristic models is linear regression analysis. In this paper, basic structure of linear statistical model and its application are discussed. In particular, problems and pitfalls in its application are demonstrated, focusing on variable selection, multi-colinearity and model checking, which will, I believe, lead us to have an access to this clinically important subject "prediction".     

六辊冷轧机板形快速预报方法

 与四辊轧机的板形预报问题相比,六辊轧机的未知量更多,计算速度更慢。为此,将模型耦合法拓展到六辊轧机,建立相关的带材塑性变形模型与辊系弹性变形模型的线性方程组。考虑到板形问题的输入参数中通常并不包含空载辊缝值,根据带材出口平均厚度已知的条件提出一个新的线性方程,将空载辊缝值纳入到未知量中,提高了模型耦合法的适用性。两个计算实例表明,六辊轧机板形快速预报的模型耦合法的计算结果与松弛因子法吻合良好,但可使计算速度提高100倍左右。     

Generalization of ETo ANN Models through Data Supplanting

This paper describes the application of artificial neural networks (ANNs) for estimating reference evapotranspiration (ETo) as a function of local maximum and minimum air temperatures as well as exogenous relative humidity and reference evapotranspiration in different continental contexts of the autonomous Valencia region, on the Spanish Mediterranean coast. The development of new and more precise models for ETo prediction from minimum climatic data is required, since the application of existing methods that provide acceptable results is limited to those places where large amounts of reliable climatic data are available. The Penman-Monteith model for ETo prediction, proposed by the FAO as the sole standard method for ETo estimation, was used to provide the ANN targets for the training and testing processes. Concerning models which demand scant climatic inputs, the proposed model provides performances with lower associated errors than the currently existing temperature-based models, which only consider local data.     

Applicability of Quasisteady and Axisymmetric Turbulence Models in Water Hammer

Two of the existing turbulence water hammer models, namely the two-layer and the five-layer eddy viscosity models, are implemented and analyzed and the accuracy of their quasi-steady and axisymmetric assumptions evaluated. In addition, a dimensionless parameter P (ratio of the time scale of radial diffusion of shear to the time scale of wave propagation) for assessing the accuracy of quasi-steady turbulence modeling in water hammer problems is developed and applied. It is found that the results of both models are in reasonable agreement, confirming that the turbulence modeling of water hammer flows is insensitive to the magnitude and distribution of the eddy viscosity within the pipe core. Comparison of model results with available data shows that the quasi-steady assumption becomes more accurate as the dimensionless parameter P increases. Furthermore, the analysis shows that the quasi-steady assumption is highly accurate as long as the simulation time is below the diffusion time scale and that this assumption causes an almost linear increase in the difference between model results and data with time. The accuracy of the flow axisymmetry assumption is evaluated by applying both models to a water hammer problem where flow asymmetry has been observed experimentally. It is found that the difference between models and data grows exponentially and reaches 100% after six wave periods.     

Investigation of a System Identification Methodology in the Context of the ASCE Benchmark Problem

This article briefly presents the theory for a system identification and damage detection algorithm for linear systems, and discusses the effectiveness of such a methodology in the context of a benchmark problem that was proposed by the ASCE Task Group in Health Monitoring. The proposed approach has two well-defined phases: (1) identification of a state space model using the Observer/Kalman filter identification algorithm, the eigensystem realization algorithm, and a nonlinear optimization approach based on sequential quadratic programming techniques, and (2) identification of the second-order dynamic model parameters from the realized state space model. Structural changes (damage) are characterized by investigating the changes in the second-order parameters of the “reference” and “damaged” models. An extensive numerical analysis, along with the underlying theory, is presented in order to assess the advantages and disadvantages of the proposed identification methodology.     

Second-Order Radiation Boundary Condition for Water Wave Simulation with Large Angle Incidence

A finite-element method (FEM) is used to simulate water wave propagation with large angle incidence at exterior boundaries. In this paper, the radiation boundary condition is expanded to a second-order approximation and a quadratic shape function is used in the FEM wave model. Cases used for verifications include wave scattering around a vertical cylinder and wave propagation over a submerged circular shoal with concentric contours. Numerical calculations based on this second-order radiation boundary condition are found to be in good agreement with theoretical and experimental results available. The numerical predictions show that this model has made a very good improvement over the first-order radiation boundary conditions for oblique wave incidence in coastal engineering.     

A double bootstrap method to analyze linear models with autoregressive error terms.

A new method for the analysis of linear models that have autoregressive errors is proposed. The approach is not only relevant in the behavioral sciences for analyzing small-sample time-series intervention models, but it is also appropriate for a wide class of small-sample linear model problems in which there is interest in inferential statements regarding all regression parameters and autoregressive parameters in the model. The methodology includes a double application of bootstrap procedures. The lst application is used to obtain bias-adjusted estimates of the autoregressive parameters. The 2nd application is used to estimate the standard errors of the parameter estimates. Theoretical and Monte Carlo results are presented to demonstrate asymptotic and small-sample properties of the method; examples that illustrate advantages of the new approach over established time-series methods are described. (PsycINFO Database Record (c) 2010 APA, all rights reserved)