Large-time Behavior of Solutions to the Inflow Problem of Full Compressible Navier-Stokes Equations

Large-time behavior of solutions to the inflow problem of full compressible Navier-Stokes equations is investigated on the half line $R^+ =(0,+\infty)$. The wave structure which contains four waves: the transonic(or degenerate) boundary layer solution, 1-rarefaction wave, viscous 2-contact wave and 3-rarefaction wave to the inflow problem is described and the asymptotic stability of the superposition of the above four wave patterns to the inflow problem of full compressible Navier-Stokes equations is proven under some smallness conditions. The proof is given by the elementary energy analysis based on the underlying wave structure. The main points in the proof are the degeneracies of the transonic boundary layer solution and the wave interactions in the superposition wave.Comment: 27 page

Probabilistic collocation method for uncertainty analysis of soil infiltration in flood modelling

The probabilistic collocation method (PCM) based on the Karhunen-Loevè expansion (KLE) and Polynomial chaos expansion (PCE) is applied for uncertainty analysis of flood inundation modelling. The floodplain hydraulic conductivity (KS) is considered as one of the important parameters in a 2-dimensional (2D) physical model FLO-2D (with Green-Ampt infiltration method) and has a nonlinear relationship with the flood simulation results, such as maximum flow depths (hmax). In this study, due to the spatial heterogeneity of soil, log-transformed Ks was assumed a random field in spatiality with normal distribution and decomposed with KLE in pairs of corresponding eigenvalues and eigenfuctions. The hmax random field is expanded by a second-order PCE approximation and the deterministic coefficients in PCE are solved by FLO-2D. To demonstrate this method, a simplified flood inundation case was used, where the mean and variance of the simulation outputs were compared with those from direct Monte Carlo Simulation. The comparison indicates that PCM could efficiently capture the statistics of flow depth in flood modelling with much less computational requirements

Impact intensities of climatic changes on grassland ecosystems in headwater areas

Rainfall and temperature are the direct driving factors that affect grassland ecosystem evolution. The study constructed the assessment model of the driving factors, temperature and rainfall, that exerted influence on the primary productivities of the grassland ecosystems in headwater areas, and used the model to quantitatively analyze the primary productivity variations of the grassland ecosystems in the headwater areas with the temperature and rainfall from those in the baseline year on the basis of the nearly forty year climatic data collected by the meteorological stations of Qumalai County, Maduo County, Yushu County and Nangqian County. The results show that over the forty-one years between 1962 and 2002, the rainfall among the climate dominated natural factors was the critical factor that caused the primary productivities of the grassland ecosystems to vary in the headwater areas. Moreover, the temperature was the limiting factor of the primary productivities of the grassland ecosystems in the headwater areas, which gently affect the primary productivities of grassland ecosystems in the headwater areas.Key words: Climatic change, headwater, grassland, ecosystem, impact intensity

Associative classifier for uncertain data

Associative classifiers are relatively easy for people to understand and often outperform decision tree learners on many classification problems. Existing associative classifiers only work with certain data. However, data uncertainty is prevalent in many real-world applications such as sensor network, market analysis and medical diagnosis. And uncertainty may render many conventional classifiers inapplicable to uncertain classification tasks. In this paper, based on U-Apriori algorothm and CBA algorithm, we propose an associative classifier for uncertain data, uCBA (uncertain Classification Based on Associative), which can classify both certain and uncertain data. The algorithm redefines the support, confidence, rule pruning and classification strategy of CBA. Experimental results on 21 datasets from UCI Repository demonstrate that the proposed algorithm yields good performance and has satisfactory performance even on highly uncertain data