A three‐dimensional model describing stress‐temperature induced solid phase transformations: solution algorithm and boundary value problems

An always increasing knowledge on material properties as well as a progressively more sophisticated production technology make shape memory alloys (SMA) extremely interesting for the industrial world. At the same time, SMA devices are typically characterized by complex multi‐axial stress states as well as non‐homogeneous and non‐isothermal conditions both in space and time. This aspect suggests the finite element method as a useful tool to help and improve application design and realization. With this aim, we focus on a three‐dimensional macroscopic thermo‐mechanical model able to reproduce the most significant SMA features (Int. J. Numer. Methods Eng. 2002; 55 : 1255–1264), proposing a simple modification of such a model. However, the suggested modification allows the development of a time‐discrete solution algorithm, which is more effective and robust than the one previously discussed in the literature. We verify the computational tool ability to simulate realistic mechanical boundary value problems with prescribed temperature dependence, studying three SMA applications: a spring actuator, a self‐expanding stent, a coupling device for vacuum tightness. The effectiveness of the model to solve thermo‐mechanical coupled problems will be discussed in a forthcoming work. Copyright © 2004 John Wiley & Sons, Ltd.     

Modeling Tetrachloroethylene Decomposition in Photosonolysis Reactor

A mathematical model was developed for the decomposition of halogenated compounds in water and applied for tetrachloroethylene (PCE) in a flow-through photosonolysis reactor. To develop the model, a series of differential equations were formulated based on the principles of conservation of mass and mass action for hypothetical parent and daughter species, which were then solved analytically. The model sensitivity analysis was performed to determine if the model gives intuitive results. In support of the modeling effort, experiments were conducted. In a flow-through bench-scale reactor, simulated groundwater contaminated with PCE was irradiated with ultraviolet light and ultrasound concurrently in the presence of titanium dioxide (TiO2). The model was calibrated against the concentrations of PCE, trichloroethylene, methylene chloride, and chloride ion, obtained from the experiments. The proposition for the rate constants is based on fitting the model result to the experimental data. The model was further verified by comparing the model results against a different set of experimental data. The model results against available data indicated good agreement within experimental variations. The modeling suggests that PCE is more susceptible to the C-C double bond cleavage than the C-Cl bond cleavage. The results also support the assumptions that chlorinated methane compounds can be degraded at higher rates than chlorinated ethylene and that the values of the degradation rate constants increase with a decreasing number of chlorine atoms attached to the hydrocarbon compound.     

Real-Time Three-Dimensional Occupancy Grid Modeling for the Detection and Tracking of Construction Resources

Awareness of the construction environment can be improved by automatic three-dimensional (3D) sensing and modeling of job sites in real time. Commercially available 3D modeling approaches based on range scanning techniques are capable of modeling static objects only, and thus cannot model dynamic objects in real time in an environment comprised of moving humans, equipment, and materials. Emerging prototype video range cameras offer an alternative by facilitating affordable, wide field of view, dynamic object tracking at frame rates better than 1?Hz (real time). This paper describes a methodology to model, detect, and track the position of static and moving objects in real time, based on data obtained from video range cameras. Experiments with this technology have produced results that indicate that video rate 3D data acquisition and analysis of construction environments can support effective modeling, detection, and tracking of project resources. This approach to job site awareness has inherent value and broad application. In combination with effective management practices and other sensing techniques, this technology has the potential to significantly improve safety on construction job sites.     

Challenges in the Application of Stochastic Modal Identification Methods to a Cable-Stayed Bridge

This paper presents an analysis of the data collected in the ambient vibration test of the International Guadiana cable-stayed Bridge, which links Portugal and Spain, based on different output-only identification techniques: peak-picking, frequency domain decomposition, covariance-driven stochastic subspace identification, and data-driven stochastic subspace identification. The purpose of the analysis is to compare the performance of the four techniques and evaluate their efficiency in dealing with specific challenges involved in the modal identification of the tested cable-stayed bridge, namely the existence of closely spaced modes, the perturbation produced by the local vibration of stay-cables, and the variation of modal damping coefficients with wind velocity. The identified natural frequencies and mode shapes are compared with the corresponding modal parameters provided by a previously developed numerical model. Additionally, the variability of some modal damping coefficients is related with the variation of the wind characteristics and associated with a component of aerodynamic damping.     

Role of Ponded Turbidity Currents in Reservoir Trap Efficiency

The capacity to store water in a reservoir declines as it traps sediment. A river entering a reservoir forms a prograding delta. Coarse sediment (e.g., sand) deposits in the fluvial topset and avalanching foreset of the delta, and is typically trapped with an efficiency near 100%. The trap efficiency of fine sediment (e.g., mud), on the other hand, may be below 100%, because some of this sediment may pass out of the reservoir without settling out. Here, a model of trap efficiency of mud is developed in terms of the mechanics of a turbidity current that plunges on the foreset. The dam causes a sustained turbidity current to reflect and form a muddy pond bounded upstream by a hydraulic jump. If the interface of this muddy pond rises above any vent or overflow point at the dam, the trap efficiency of mud drops below 100%. A model of the coevolution of topset, foreset, and bottomset in a reservoir that captures the dynamics of the internal muddy pond is presented. Numerical implementation, comparison against an experiment, and application to a field-scale case provide the basis for a physical understanding of the processes that determine reservoir trap efficiency.     

Examining a dimensional representation of depression and anxiety disorders' comorbidity in psychiatric outpatients with item response modeling.

The current study replicated, in a sample of 2,300 outpatients seeking psychiatric treatment, a previous study (R. F. Krueger & M. S. Finger, 2001) that implemented an item response theory approach for modeling the comorbidity of common mood and anxiety disorders as indicators along the continuum of a shared latent factor (internalizing). The 5 disorders examined were major depressive disorder, social phobia, panic disorder/agoraphobia, specific phobia, and generalized anxiety disorder. The findings were consistent with the prior research. First, a confirmatory factor analysis yielded sufficient evidence for a nonspecific factor underlying the 5 diagnostic indicators. Second, a 2-parameter logistic item response model showed that the diagnoses were represented in the upper half of the internalizing continuum, and each was a strongly discriminating indicator of the factor. Third, the internalizing factor was significantly associated with 3 indexes of social burden: poorer social functioning, time missed from work, and lifetime hospitalizations. Rather than the categorical system of presumably discrete disorders presented in DSM-IV, these 5 mood and anxiety disorders may be alternatively viewed as higher end indicators of a common factor associated with social cost. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Production Equations for Unsteady-State Construction Processes

The production equation called Little’s law has been applied to construction data recently. However, Little’s law was derived for steady-state conditions assuming constant input and output rates and long production runs. Production in construction is inherently temporary, and learning curves and environmental influences often render input and output rates unequal and nonlinear. Starting with a conservation of mass formulation, general equations for work-in-process and cycle time for unsteady-state conditions and limited run production are developed. The motivation behind these equations is to explain common trends in production variables seen on construction projects. Previous studies have shown that when output from a construction production system is drastically increased, a significant upward impact is also seen on cycle time and work-in-process, and this work provides underlying theory and equations to explain these trends. Cycle time and work-in-process equations are presented as functions of time and on average. Data from construction activities are used to show that unsteady-state conditions commonly occur. Reasonable simplifications of the general equations provide guidelines for buffer sizing and resource allocation decisions.     

Modeling of Temperature–Frequency Correlation Using Combined Principal Component Analysis and Support Vector Regression Technique

A good understanding of environmental effects on structural modal properties is essential for reliable performance of vibration-based damage diagnosis methods. In this paper, a method of combining principal component analysis (PCA) and support vector regression (SVR) technique is proposed for modeling temperature-caused variability of modal frequencies for structures instrumented with long-term monitoring systems. PCA is first applied to extract principal components from the measured temperatures for dimensionality reduction. The predominant feature vectors in conjunction with the measured modal frequencies are then fed into a support vector algorithm to formulate regression models that may take into account thermal inertia effect. The research is focused on proper selection of the hyperparameters to obtain SVR models with good generalization performance. A grid search method with cross validation and a heuristic method are utilized for determining the optimal values of SVR hyperparameters. The proposed method is compared with the method directly using measurement data to train SVR models and the multivariate linear regression (MLR) method through the use of long-term measurement data from a cable-stayed bridge. It is shown that PCA-compressed features make the training and validation of SVR models more efficient in both model accuracy and computational costs, and the formulated SVR model performs much better than the MLR model in generalization performance. When continuously measured data is available, the SVR model formulated taking into account thermal inertia effect can achieve more accurate prediction than that without considering thermal inertia effect.