Experimental and Numerical Analysis of a Dam-Break Flow through Different Contraction Geometries of the Channel

Dam-break wave propagation usually occurs over irregular topography, due for example to natural contraction-expansion of the river bed and to the presence of natural or artificial obstacles. Due to limited available dam-break real-case data, laboratory and numerical modeling studies are significant for understanding this type of complex flow problems. To contribute to the related field, a dam-break flow over a channel with a contracting reach was investigated experimentally and numerically. Laboratory tests were carried out in a smooth rectangular channel with a horizontal dry bed for three different lateral contraction geometries. A non-intrusive digital imaging technique was utilized to analyze the dam-break wave propagation. Free surface profiles and time variation of water levels in selected sections were obtained directly from three synchronized CCD video camera records through a virtual wave probe. The experimental results were compared against the numerical solution of VOF (Volume of Fluid)-based Shallow Water Equations (SWEs) and Reynolds-Averaged Navier-Stokes (RANS) equations with the k-ε turbulence model. Good agreements were obtained between computed and measured results. However, the RANS solution shows a better correspondence with the experimental results compared with the SWEs one. The presented new experimental data can be used to validate numerical models for the simulation of dam-break flows over irregular topography

Bridge afflux estimation using artificial intelligence systems

WOS: 000291606400003Most of the methods developed for the prediction of bridge afflux are generally based on either energy or momentum equations. Recent studies have shown that the energy method, which is one of the four bridge subroutines within the commonly used program HEC-RAS for computing water surface profiles along rivers, produced more accurate results than three other methods (momentum, WSPRO and Yarnell's methods) when applied to bridge afflux data obtained from experiments conducted in a two-stage channel. This work developed three artificial intelligence models (the radial basis neural network, the multi-layer perceptron and the adaptive neuro-fuzzy inference system) as alternatives to the energy method. Multiple linear and multiple non-linear regression models were also used in the analysis. The results showed that the performance of the adaptive neuro fuzzy inference system in predicting bridge afflux was superior to the other models

Prevalence of Childhood Affective disorders in Turkey: An epidemiological study

Aim: To determine the prevalence of affective disorders in Turkey among a representative sample of Turkish population. Methods: This study was conducted as a part of the "The Epidemiology of Childhood Psychopathology in Turkey" (EPICPAT-T) Study, which was designed by the Turkish Association of Child and Adolescent Mental Health. The inclusion criterion was being a student between the second and fourth grades in the schools assigned as study centers. The assessment tools used were the K-SADS-PL, and a sociodemographic form that was designed by the authors. Impairment was assessed via a 3 point-Likert type scale independently rated by a parent and a teacher. Results: A total of 5842 participants were included in the analyses. The prevalence of affective disorders was 2.5 % without considering impairment and 1.6 % when impairment was taken into account. In our sample, the diagnosis of bipolar disorder was lacking, thus depressive disorders constituted all the cases. Among depressive disorders with impairment, major depressive disorder (MDD) (prevalence of 1.06%) was the most common, followed by dysthymia (prevalence of 0.2%), adjustment disorder with depressive features (prevalence of 0.17%), and depressive disorder-NOS (prevalence of 0.14%). There were no statistically significant gender differences for depression. Maternal psychopathology and paternal physical illness were predictors of affective disorders with pervasive impairment. Conclusion: MDD was the most common depressive disorder among Turkish children in this nationwide epidemiological study. This highlights the severe nature of depression and the importance of early interventions. Populations with maternal psychopathology and paternal physical illness may be the most appropriate targets for interventions to prevent and treat depression in children and adolescents