Experimental Analysis of Hydraulic Conductivity for Saturated Granular Soils

Hydraulic conductivity is the ability of a porous media to transfer water through its pore matrix. That is a key parameter for the design and analysis of soil fluid associated structures and issues. This paper presents the test results of the vertical hydraulic conductivity kv carried out on one poorly graded sand and three gap graded gravely sand. It was found that the vertical hydraulic conductivity of saturated soil depends on the grain size distribution curve, on the initial relative density of the soil. Compilation of these current test results and other test results published, shows that the common approaches predict well to some extent the vertical hydraulic conductivity kv for the poorly graded sand materials and underestimate the kv values for gap graded gravely sand materials. Therefore, new approaches are developed for the prediction of the vertical hydraulic conductivity in saturated poorly graded sand and gap graded gravely sand. The derived results from the new approaches lie in the range of the recommended values by (EAU 2012) and (NAVFAC DM 7 1974).

FUZZY APPROACHING SET AND FUZZYAPPROACHING FUNCTIONAL MAPPING

By establishing the concepts of fuzzy approaching set and fuzzy approaching functional mapping and making research on them, a new method for time series prediction is introduced.

Has the Prediction of the South China Sea Summer Monsoon Improved Since the Late 1970s?

Based on the evaluation of state-of-the-art coupled ocean-atmosphere general circulation models （CGCMs） from the ENSEMBLES （Ensemble-based Predictions of Climate Changes and Their Impacts） and DEME- TER （Development of a European Multimodel Ensemble System for Seasonal to Interannual Prediction） projects, it is found that the prediction of the South China Sea summer monsoon （SCSSM） has improved since the late 1970s. These CGCMs show better skills in prediction of the atmospheric circulation and precipitation within the SCSSM domain during 1979-2005 than that during 1960-1978. Possible reasons for this improvement are investigated. First, the relationship between the SSTs over the tropical Pacific, North Pacific and tropical Indian Ocean, and SCSSM has intensified since the late 1970s. Meanwhile, the SCSSM-related SSTs, with their larger amplitude of interannual variability, have been better predicted. Moreover, the larger amplitude of the interannual variability of the SCSSM and improved initializations for CGCMs after the late 1970s contribute to the better prediction of the SCSSM. In addition, considering that the CGCMs have certain limitations in SCSSM rainfall prediction, we applied the year-to-year increment approach to these CGCMs from the DEMETER and ENSEMBLES projects to improve the prediction of SCSSM rainfall before and after the late 1970s.

Detailed parametric design methodology for hydrodynamics of liquid-solid circulating fluidized bed using design of experiments

A design-of-experiments methodology is used to develop a statistical model for the prediction of the hydrodynamics of a liquid–solid circulating fluidized bed. To illustrate the multilevel factorial design approach, a step by step methodology is taken to study the effects of the interactions among the independent factors considered on the performance variables. A multilevel full factorial design with three levels of the two factors and five levels of the third factor has been studied. Various statistical models such as the linear, two-factor interaction, quadratic, and cubic models are tested. The model has been developed to predict responses, viz., average solids holdup and solids circulation rate. The validity of the developed regression model is verified using the analysis of variance. Furthermore, the model developed was compared with an experimental dataset to assess its adequacy and reliability. This detailed statistical design methodology for non-linear systems considered here provides a very important tool for design and optimization in a cost-effective approach

Research on predicting prosodic parameters for Chinese synthesis by data mining approach

Prosodic control is an important part of speech synthesis system. Prosodic parameters choice right or wrong influences the quality of synthetic speech directly. At present, text to speech system has less effective describe to reflect data relationships in the corpus. A new research approach - data mining technology to discover those relationships by association rules modeling is presented. And a new algorithm for generating association rules of prosodic parameters including pitch parameters and duration parameters from corpus is developed. The output rules improve the correctness of syllable choice in text to speech system.