This paper is concerned with the numerical simulation of multiphase,multi-component flow in porous media.The model equations are based on compositional flow with mass interchange between phases.The compositional model...This paper is concerned with the numerical simulation of multiphase,multi-component flow in porous media.The model equations are based on compositional flow with mass interchange between phases.The compositional model consists of Darcy’s law for volumetric flow velocities,mass conservation for hydrocarbon components,ther-modynamic equilibrium for mass interchange between phases,and an equation of state for saturations.High-accurate finite volume methods on unstructured grids are used to discretize the model governing equations.Special emphasis is placed on studying the influence of gravitational effects on the overall displacement dynamics.In particular,free and forced convections,diffusions,and dispersions are studied in separate and com-bined cases,and their interplays are intensively analyzed for gravitational instabilities.Extensive numerical experiments are presented to validate the numerical study under consideration.展开更多
A kind of combinatorial material methodology,also known as continuous compositional spread method,was employed to investigate the relationship between the optical band gap and composition of SiC thin films.A wide rang...A kind of combinatorial material methodology,also known as continuous compositional spread method,was employed to investigate the relationship between the optical band gap and composition of SiC thin films.A wide range of SixCy thin films with different carbon contents have been successfully deposited in a single deposition by carefully arranging the sample position on the substrate holder.The films were characterized by surface profiler,x-ray photoelectron spectroscopy,ultraviolet-visible spectroscopy,fourier transform infrared spectroscopy and Raman spectroscopy.The carbon content y increases linearly from 0.28 to 0.72 while the sample position changed from 85 to 175 mm,the optical band gap changed between 1.27 and 1.99 eV,the maximum value corresponded to the stoichiometric SiC sample at the position of 130 mm,which has the highest Si?C bond density of 11.7×10^22 cm^-3.The C poor and C rich SixCy samples with y value less and larger than 0.5 were obtained while samples deviated from the position 130 mm,the optical band gap decreased with the Si?C bond density.展开更多
Compositional data, such as relative information, is a crucial aspect of machine learning and other related fields. It is typically recorded as closed data or sums to a constant, like 100%. The statistical linear mode...Compositional data, such as relative information, is a crucial aspect of machine learning and other related fields. It is typically recorded as closed data or sums to a constant, like 100%. The statistical linear model is the most used technique for identifying hidden relationships between underlying random variables of interest. However, data quality is a significant challenge in machine learning, especially when missing data is present. The linear regression model is a commonly used statistical modeling technique used in various applications to find relationships between variables of interest. When estimating linear regression parameters which are useful for things like future prediction and partial effects analysis of independent variables, maximum likelihood estimation (MLE) is the method of choice. However, many datasets contain missing observations, which can lead to costly and time-consuming data recovery. To address this issue, the expectation-maximization (EM) algorithm has been suggested as a solution for situations including missing data. The EM algorithm repeatedly finds the best estimates of parameters in statistical models that depend on variables or data that have not been observed. This is called maximum likelihood or maximum a posteriori (MAP). Using the present estimate as input, the expectation (E) step constructs a log-likelihood function. Finding the parameters that maximize the anticipated log-likelihood, as determined in the E step, is the job of the maximization (M) phase. This study looked at how well the EM algorithm worked on a made-up compositional dataset with missing observations. It used both the robust least square version and ordinary least square regression techniques. The efficacy of the EM algorithm was compared with two alternative imputation techniques, k-Nearest Neighbor (k-NN) and mean imputation (), in terms of Aitchison distances and covariance.展开更多
From the viewpoints of both fuzzy system and fuzzy reasoning, a new fuzzy reasoning method which contains the α- triple I restriction method as its particular case is proposed. The previous α-triple I restriction pr...From the viewpoints of both fuzzy system and fuzzy reasoning, a new fuzzy reasoning method which contains the α- triple I restriction method as its particular case is proposed. The previous α-triple I restriction principles are improved, and then the optimal restriction solutions of this new method are achieved, especially for seven familiar implications. As its special case, the corresponding results of α-triple I restriction method are obtained and improved. Lastly, it is found by examples that this new method is more reasonable than the α-triple I restriction method.展开更多
文摘This paper is concerned with the numerical simulation of multiphase,multi-component flow in porous media.The model equations are based on compositional flow with mass interchange between phases.The compositional model consists of Darcy’s law for volumetric flow velocities,mass conservation for hydrocarbon components,ther-modynamic equilibrium for mass interchange between phases,and an equation of state for saturations.High-accurate finite volume methods on unstructured grids are used to discretize the model governing equations.Special emphasis is placed on studying the influence of gravitational effects on the overall displacement dynamics.In particular,free and forced convections,diffusions,and dispersions are studied in separate and com-bined cases,and their interplays are intensively analyzed for gravitational instabilities.Extensive numerical experiments are presented to validate the numerical study under consideration.
文摘A kind of combinatorial material methodology,also known as continuous compositional spread method,was employed to investigate the relationship between the optical band gap and composition of SiC thin films.A wide range of SixCy thin films with different carbon contents have been successfully deposited in a single deposition by carefully arranging the sample position on the substrate holder.The films were characterized by surface profiler,x-ray photoelectron spectroscopy,ultraviolet-visible spectroscopy,fourier transform infrared spectroscopy and Raman spectroscopy.The carbon content y increases linearly from 0.28 to 0.72 while the sample position changed from 85 to 175 mm,the optical band gap changed between 1.27 and 1.99 eV,the maximum value corresponded to the stoichiometric SiC sample at the position of 130 mm,which has the highest Si?C bond density of 11.7×10^22 cm^-3.The C poor and C rich SixCy samples with y value less and larger than 0.5 were obtained while samples deviated from the position 130 mm,the optical band gap decreased with the Si?C bond density.
文摘Compositional data, such as relative information, is a crucial aspect of machine learning and other related fields. It is typically recorded as closed data or sums to a constant, like 100%. The statistical linear model is the most used technique for identifying hidden relationships between underlying random variables of interest. However, data quality is a significant challenge in machine learning, especially when missing data is present. The linear regression model is a commonly used statistical modeling technique used in various applications to find relationships between variables of interest. When estimating linear regression parameters which are useful for things like future prediction and partial effects analysis of independent variables, maximum likelihood estimation (MLE) is the method of choice. However, many datasets contain missing observations, which can lead to costly and time-consuming data recovery. To address this issue, the expectation-maximization (EM) algorithm has been suggested as a solution for situations including missing data. The EM algorithm repeatedly finds the best estimates of parameters in statistical models that depend on variables or data that have not been observed. This is called maximum likelihood or maximum a posteriori (MAP). Using the present estimate as input, the expectation (E) step constructs a log-likelihood function. Finding the parameters that maximize the anticipated log-likelihood, as determined in the E step, is the job of the maximization (M) phase. This study looked at how well the EM algorithm worked on a made-up compositional dataset with missing observations. It used both the robust least square version and ordinary least square regression techniques. The efficacy of the EM algorithm was compared with two alternative imputation techniques, k-Nearest Neighbor (k-NN) and mean imputation (), in terms of Aitchison distances and covariance.
基金supported by the National Natural Science Foundation of China (61105076 61070124)+2 种基金the National High Technology Research and Development Program of China (863 Program) (2012AA011103)the Open Project of State Key Laboratory of Virtual Reality Technology and Systems of China (BUAA-VR-10KF-5)the Fundamental Research Funds for the Central Universities (2011HGZY0004)
文摘From the viewpoints of both fuzzy system and fuzzy reasoning, a new fuzzy reasoning method which contains the α- triple I restriction method as its particular case is proposed. The previous α-triple I restriction principles are improved, and then the optimal restriction solutions of this new method are achieved, especially for seven familiar implications. As its special case, the corresponding results of α-triple I restriction method are obtained and improved. Lastly, it is found by examples that this new method is more reasonable than the α-triple I restriction method.
