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In present paper, a two-dimensional numerical study on a standing-wave thermoacoustic engine was performed with compressible SIMPLE algorithm based on a pressure-correction method. First, the simulation model was developed, and the time-dependent compressible thermoacoustic engine system was chosen through substantive numerical tests. Appropriate governing equations for mass, momentum and energy were introduced. Then, the computational results of the onset of the self-excited oscillations across the entire evolution process and the acoustical characteristics of the pressure and velocity wave were presented and analyzed. In addition, the standing-wave of the pressure and velocity along the center of the two stacks are investigated. The crucial nonlinear phenomenon that cannot be captured by the existing linear theory, like high harmonic frequencies, is also revealed in present paper. It is concluded that compressible SIMPLE algorithm could be employed in our future work to simulate and optimize thermoacoustic system. The present result is an important step toward development to predict the high-amplitude thermoacoustic systems and optimize thermoacoustic engine performance. [ABSTRACT FROM AUTHOR]

The paper presents a set of Monte Carlo algorithms, based on the Kac's stohastic gas model for simulation of the binary collision process in a homogenous rarefied gas. These collision algorithms avoid the repeat collisions and can be used for simualtion of the collsions in grid cells with small average number of particles, when the DSMC method is applied and the simulation requires computational resources of very large proportions. [ABSTRACT FROM AUTHOR]

The objective of this research is to build a nested generalized linear mixed model using an ordinal response variable with some covariates. There are three main jobs in this paper, i.e. parameters estimation procedure, simulation, and implementation of the model for the real data. At the part of parameters estimation procedure, concepts of threshold, nested random effect, and computational algorithm are described. The simulations data are built for 3 conditions to know the effect of different parameter values of random effect distributions. The last job is the implementation of the model for the data about poverty in 9 districts of Java Island. The districts are Kuningan, Karawang, and Majalengka chose randomly in West Java; Temanggung, Boyolali, and Cilacap from Central Java; and Blitar, Ngawi, and Jember from East Java. The covariates in this model are province, number of bad nutrition cases, number of farmer families, and number of health personnel. In this modeling, all covariates are grouped as ordinal scale. Unit observation in this research is sub-district (kecamatan) nested in district, and districts (kabupaten) are nested in province. For the result of simulation, ARB (Absolute Relative Bias) and RRMSE (Relative Root of mean square errors) scale is used. They show that prov parameters have the highest bias, but more stable RRMSE in all conditions. The simulation design needs to be improved by adding other condition, such as higher correlation between covariates. Furthermore, as the result of the model implementation for the data, only number of farmer family and number of medical personnel have significant contributions to the level of poverty in Central Java and East Java province, and only district 2 (Karawang) of province 1 (West Java) has different random effect from the others. The source of the data is PODES (Potensi Desa) 2008 from BPS (Badan Pusat Statistik). [ABSTRACT FROM AUTHOR]